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Task Automation: Definition, Examples, Use cases and more

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Sometime in the fourth millennium BCE, someone in Mesopotamia decided that walking was overrated and there must be some way to create value from the activity. The wheel seemed like a good idea, and they may have been the first task automation expert in human history. Since then, humans have been at the forefront of automating tasks. With the advent of the digital revolution, task automation has become pervasive in personal and professional spheres.

What is task automation?

Task automation is the process of deploying modern technology to reduce or eliminate the necessity for human intervention in completing a task . Various aspects of our daily lives exemplify task automation, such as airport check-in kiosks, ATMs, fast-food ordering, prescription refills , and subscription services . This approach aims to enhance efficiency, accuracy, and speed in task execution, providing convenience and often optimizing overall processes. Task automation tools are ubiquitous today and have eased personal and professional activities. For example, Alexa, Siri, and Google Assistant are now part of our lives, and automation tools are used in banking, home management, education, and entertainment.

In the professional domain, task automation involves digitizing the mission-critical core processes of the business. The method may be as simple as capturing and automatically responding to emails and scheduling follow-ups or may cover complex multi-level organizational workflows.

A survey by McKinsey shows that although few businesses are entirely automatable (1%), 60% of all occupations can automate at least 34 percent of their tasks.

[Exclusive Free Webinar] Automate Task Processing with automated workflows.

Types of tasks that can be automated

Any repetitive task that follows a set of rules can be automated. Some task characteristics that make a task amenable to automation are:

• Recurrent: Any task that must be performed multiple times in a period and involves a specific set of steps every time can be automated. Examples include recurring bill payments.
• Trigger-induced: Some actions trigger a specific reaction, and such responses can be automated. Setting up email filters and autoresponders are examples of this type.
• Labor intensive: When an activity requires intensive manual effort and does not involve much decision making, it can be automated. However, in recent times, the advent of AI and ML techniques has also enabled the automation of decision-making tasks. The American Psychological Association reports that even brief mental blocks created by shifting between tasks can cost as much as 40 percent of an individual's productive time, which leads to a loss of employee morale and productivity. Data entry tasks are classic examples of such labour-intensive and potentially dull tasks. The automation of such tasks can lead to better utilization of human capital.

Task automation for business

Task automation in a business helps in the effective performance of repetitive and routine activities. It forms the cogwheel of the company's Business Process Management (BPM) and Business Rules Management (BRM). Tasks that can be automated can be categorized into document management, collaboratory, organizational, and data management activities.

• Document management: Any enterprise necessarily deals with paperwork and has some form of systematic document management workflow. Document workflow management allows methodical handling of documents from the time they enter the business activity, track its movements through various paths, monitor and instigate operational actions, and finally, reports/archives the document for posterity. Automated document management systems are software that can be used to extract data, track, manage and store all kinds of documents in an organized and easy-to-retrieve form. Document management automation would help cut out paper from the document workflow and increase productivity by over 50%.

If your PDFs deal with invoices, receipts, passports or driver's licenses, check out Nanonets online OCR or PDF text extractor to extract text from PDF documents for free . Click below to learn more about Nanonets PDF scraper .

• Collaborative tasks: A business runs through a collaborative effort among its participants and employees. In large companies, collaborative tasks may include employee work assignments, maintaining communication among various participants of a task or project, approval management, etc. Automating some or all of these tasks can help reduce time delays, save money, afford greater transparency of processes, and improve employee productivity and work satisfaction.
• Organizational tasks: A business workflow usually involves a series of actions to be completed in a specific order, with checkpoints and side flows depending on the scale and complexity of the business operation. Any rule-based task or workflow is amenable to automation and can help in the smooth running of the operation, with appropriate checks and approvals incorporated in the process.
• Data management tasks: This is closely related to document management. A company deals with various data and information, including and not limited to employee roster, customer information, product, inventory, payments, invoices, and fiscal data. Automation of data management can help eliminate bottlenecks and errors and can ease the process of archiving and auditing. With advances in big data and artificial intelligence, data management has become the best task of a business operation to be automated.

Want to scrape data from PDF documents, convert PDF to XML or automate table extraction ? Check out Nanonets' PDF scraper or PDF parser to convert PDFs to database entries!

Levels of Task Automation

Task automation may be used at various levels and to various extent in an organization depending on the scale of the business and its needs.

• At the basic level, automation can be used to digitize and automate simple jobs in an organization and provide easy access to information to all stakeholders. An example of basic automation is an in-house messaging tool or a digital workgroup for employees working on a specific project.
• Process level automation is used to manage tasks, processes, and projects and has built-in checkpoints, approvals, and updates for seamless transit of the process/project from start to end.
• The next level of task automation involves the integration of individual task automation and limits the involvement of human employees in specific human-centric activities. Such automation is often seen in customer support platforms in which customer comments/feedback are processed automatically and classified/annotated for further action.
• The most advanced level of task automation uses AI and ML tools that mimic human thinking. AI automation is the most advanced form of task automation in which computer software applications make human-like decisions. Hyper automation is an example of this level of task automation. It brings together the various components of any business – workforce, and workflow – to enhance the efficiency of operations and, thereby, the bottom line.

Business activities that are amenable to task automations

Task automation is already being used in the following departments of many business enterprises:

The Accounts Payable operations

Visual, the first spreadsheet software developed in 1978, was perhaps the first use of automation in the accounts payable workflow. Today, the Accounts Payable Process automation includes tools for invoice data capture, coding invoices with the correct account and cost center, approving invoices, matching invoices to purchase orders, and posting them for payments.

Many automation tools used in the accounts payable workflow use optical character recognition (OCR) and intelligent data capture (IDC) to automatically capture key data from accounts payable documents such as Purchase Orders, invoices, etc. They also use pre-set templates to convert all documents associated with AP management into digital versions.

All accounting tasks, including Accounts Payable, are automated using AI-based technologies. Machine Learning allows the AP system to learn from usual process elements and make meaningful suggestions on how to handle these elements in the future. In addition, such AI automation of the AP processes can give the AP team more time to devote to the analytical and administrative functions that benefit the company.

AI-based BPA tools like Nanonets can help automate AP tasks such as invoice digitization to make it a hassle-free process. For example, automation of invoice extraction using Nanonets has saved 90% time for the Accounts Payable team.

Automate Accounts Payable tasks with ease.

Accounting and banking operations

The digitization of financial documents is important for financial institutions like banks and individual banking customers and businesses. Banks must convert physical customer records into a manipulatable digital form. The customer and business bank customers must extract data from bank statements and other documents for other financial processing operations. Optical Character Recognition (OCR) tools are useful for extracting data from bank statements and other banking documents. Bank Statement OCR can extract relevant elements from bank statements and logically store them for future processing. AI-enabled OCR tools like Nanonets can help banks, individuals, and organizations that deal with bank statements by enhancing service delivery quality, providing access to error-free critical financial data, and periodic assessment of progress and financial wellbeing. Businesses have used automation tools like Nanonets to integrate accounting software such as Quickbooks, Sage, and Xero for better bookkeeping and auditing activities.

Human Resources

Human Resources is a complex and time-consuming process and would benefit through automation. HR task automation can include automation of employee onboarding administration, payroll, timekeeping, and benefits administration. HR automation systems can organize and store data such as employee profiles, schedules, attendance records, and related information. An automated evaluation or feedback system can help employees and employers assess performances and help in fostering performance improvement and collaboration.

Marketing management

Process automation of the information dissemination process and social media content development can optimize social interactions with an audience, bringing in new customers and retaining old ones. Social media automation can include scheduling social posts ahead of time or republishing popular articles periodically for more traction and to build a brand. Automation of the marketing tasks can reallocate time and resources into other areas of marketing that require human participation, such as budgeting and strategizing. Marketing companies have scaled their business 5x times using automation tools like Nanonets.

Customer Support

Chatbots and automated customer service are already a reality in many businesses, especially online ones. McKinsey reports that automation to revamp customer experience can save up to 40% on service costs. In addition, customer support automation can enable 24x7 access to information and help. Further, customer service automation can help consolidate and organize customer concerns for product improvement and brand building. Finally, customer service automation can also cater to younger customers who prefer the digital channel for customer support.

If you deal with invoices, receipts, passports or driver's licenses, check out Nanonets online OCR or PDF text extractor to extract text from PDF documents for free . Click below to learn more about Nanonets PDF scraper .

Steps required to automate tasks

The setting up of task automation in an enterprise depends upon the company's business processes and the identification of the tasks for automation. In addition, the choice of the automation tool depends on the following factors:

• The automation required: As described earlier, task automation can be basic, process level, integration level, or AI-enhanced. The level choice depends upon the company's needs, ability to commit to that level of technology, and fit with the work culture.
• Budgetary constraints: The automation of tasks entails the procurement of technology and expertise. This hinges on the amount of money available, which is dependent on the scale of the business, the bottom line, and the company's investment potential.
• Ease of use: The automation of a task requires a certain amount of human involvement and therefore requires a specific skill set. The need for training and tech support must be addressed before choosing the right tool.
• The extent of spread and collaboration: Automation eases collaborative efforts within an organization. A good task automation tool must provide access at various levels and allow easy collaboration among all participants. There also needs to be provided to include various approvals in the process.
• Integration with the other systems used in the company: While task automation can be a standalone entity, it can also be integrated into a larger hyper-automation system. The need and usefulness of hyper-automation in the present or a future period must be assessed before choosing a task automation tool. In the event of future integration, the tool must be compatible with the larger system of business process automation .

Building processes with automated tasks

Automated tasks can integrate and lead to process automation. For example, process automation could include one or more of the following task automation tools, each of which serves a different function of the process:

1. Automated Data Capture: The automated capture of data in the digital format and its classification and storage as a logical entity depends on intelligent processes that can recognize the data. OCR and ICR processes are increasingly leveraging AI and ML tools for the smart capture of data from various sources.

2. Intelligent Process Automation (IPA): An efficient and successful company has structured processes that follow predictable steps and have (largely) predictable outcomes. Such processes can be easily automated to remove bottlenecks caused by manual delays in intermediate steps. Intelligent Process Automation typically includes Digital Process Automation (DPA), Robotic Process Automation (RPA), and Artificial Intelligence (AI).

3. Automated Communication Management: Automation tools used in communication management include first-level customer support tools (e.g., chatbots), content creation tools (blog post scheduling), and strategy development. This again leverages multiple tools such as OCR, Voice recognition, NLP, and ML.

4. Automated Data Management: Automation tools for data management include software used in Business Intelligence (BI) and Online Analytical Processing (OLAP), Cluster Analysis, Network Analysis, Data Mining, NLP, ML, and cloud computing. It provides an efficient informational platform for better storage, security, analytics, and decision-making in various areas of business operation.

Automate data capture, classification, verification and more with just a few clicks!

Benefits of Task Automation

The advantages of task automation and eventual process automation include:

• Process Excellence: With task automation, employees can use their time, resources, and mental prowess on strategy, innovation, and technology rather than mundane activities. This can improve process outcomes, shorten cycle times, and maximize efficiency.
• Better data management: Task automation can enable storage of all critical operational data on a centralized server or cloud. According to Gartner , task automation will reduce Data Management manual tasks by 45 percent by 2022 .
• Error reduction: Repetitive manual processes are prone to errors. For example, manual data entry not followed by verification steps has been shown to have an error rate as high as 4% . There are two errors for every five entries made, to put that in perspective. This is a significantly large number that can affect even small data sets.
• Better transparency of data and processes within organizations:: Manual tasks are hard to track. Task automation can centralize processes and enhance transparency across the board while also logically integrating the business functionalities spread out across the organization.
• Audit readiness: Automated business processes allow standardization of operations and ensure maintenance of records of all stages of a business process, thereby creating an audit trail.
• Continuous process improvement : McKinsey & Company estimates that AI can automate as much as 45 percent or more of any particular job, allowing workers to focus on higher-level, mission-critical activities. Through the deployment of real-time data, the adaptive processes enabled by AI can guide improvements in all aspects of the business.
• Time savings: Manual tasks consume a lot of time, irrespective of the department and nature of work. For example , low-level, automatable tasks have been reported to consume 30% of IT departments' time, 47% of the AP department's time, and 75% of the time of HR and Payroll department staff. This naturally leads to time delays. Task automation can help avoid such delays and bottlenecks in the company's daily operations.

The above benefits translate to better bottom lines and performance efficiencies for enterprises.

Conclusions

Charles Darwin said that it is not the strongest of the species that survives but the one that is most adaptable to change. This holds good for businesses as well. Digital transformation is inevitable in business, and an enterprise that does not adapt will decline. Task automation is a synergistic tool slated to become the foundation for increased profits and productivity, enhanced customer satisfaction, larger bottom lines, and better worker morale.

Nanonets online OCR & OCR API have many interesting use cases that could optimize your business performance, save costs and boost growth. Find out how Nanonets' use cases can apply to your product.

Laskhmi is a 25-year veteran in research and technical content. She specializes in scientific documentation, research, and the impact of AI & automation in finance, accounting and business in general.

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The Impact of Technology and Automation on Education

• November 19, 2021
• in Productivity

The  importance of automating processes is an essential topic for many industries. However, one industry that benefits the most from it is education . Automation in the educational sector can reduce costs and improve efficiency. In this article, we will explore five ways that schools, universities, and other educational organizations can benefit from technology and process automation.  

1. Automating Classroom Technology to Manage Lesson Plans 

Many schools are already using classroom technology to manage lesson plans. There are several software platforms available online that can help teachers create, follow and update their lesson plans. Teachers can even update their lesson plans remotely. They can also make class lessons more dynamic by incorporating creative videos with music , creating interactive presentations , using teleprompters, and adding live data into documents displayed on screen. This way, they increase student engagement and interest in their studies. Most importantly, classroom technologies also allow instructors to upload content online without students having direct access to it, enabling them to plan and alter the content before presenting it to the students.  

2. Automating Administrative Workflows  

Administrative tasks such as record keeping and student management take up time at schools, universities, and other educational organizations. But automating management tasks  can streamline these activities into efficient processes, allowing school staff to focus on more essential tasks like teaching students.  

Through automation, teachers can have a permanent record of all graded assignments in an electronic format. This allows students to access their grades at any given point during the school year without having to ask for them multiple times or go into an administrator’s office if they need another copy.  

Teachers can also submit parent messages through online software , which automates communication between parents and schools, saving them hours each week! Automation saves teachers time and money, allowing them to focus on teaching instead of thinking about other administrative tasks.  

 3. Using Virtual Classrooms To Engage Students Who Are Not Physically Present On Campus 

Another way that technology improves education is through using technological tools that allow teachers to interact with students who are not physically present on campus .  Especially with the global health crisis we’re experiencing, virtual classrooms  allow teachers and professors to reach a larger audience by using platforms that allow for online lectures, interactive discussions among peers, Q&A sessions with experts in various fields of study, and more! Virtual learning environments have improved education outcomes while also saving time because it doesn’t require physical trips back and forth between campus buildings.  

Additionally, virtual classrooms allow students worldwide to enroll in lectures and lessons at any time of day as long as they have a computer with an internet connection. This also helps students who have full-time or part-time jobs because they can follow along in class in their free time instead of waking up early before work just for one specific lecture. Also, it makes going back through lesson materials easier, so teachers don’t have to worry about losing track of teaching materials which saves them valuable planning time each semester.

4. Automating Enrollment Processes 

Another way that schools can use process automation is by improving the enrollment process for both students and school staff. Technology allows parents, guardians, or students to submit their school application materials online. It saves time because it eliminates time spent on trips from student homes to campus buildings and not worrying about lost paperwork! An automated system can also accurately estimate by using AI technologies , how much aid each student will receive based on their family’s financials. This gives students and families more certainty instead of making them wait around throughout the summer months, just checking if they’ve been accepted into college yet.  

In addition, this also helps colleges save money by cutting down on paper use since everything is submitted electronically, from teachers’ recommendations to transcripts from high schools.  

5. Creating Automated Assessments With Artificial Intelligence

Lastly, process automation can help improve education by creating automated assessments with artificial intelligence . With automated assessments, instructors become more confident about what goes into the grade book. It allows them to maintain complete control over how those scores affect student performance and provides flexibility when dealing with particular circumstances like injuries or illnesses. With the rise of online learning, teachers have been tasked with grading massive assignments while also ensuring students are completing their work appropriately and on time. For many instructors, grading papers for an entire class takes hours as they spend so much time reading essays or checking test answers. It’s not only tedious but highly time-consuming!  

Automating assessments allows instructors to assess student progress quickly without taking up hours of grading papers or essays. Cognitive automation and AI technology make an effortless task possible for educators, which is why many schools are starting to use these technologies. Using cognitive automation is beneficial not only to teachers but also to students. It also allows students to receive better grades because it provides them with personalized feedback on their strengths and weaknesses. This helps instructors focus more time where it is needed the most. 

There is a lot of software available online that helps teachers with essay and quiz assessments. For essays, there are different tools that can easily check plagiarised essays like Grammarly. Also, there are software solutions with browser-based services designed for high school and higher education students that provide specific feedback and editing suggestions to uploaded essays. Students upload their work, and this software automatically suggests editing changes not only pertaining to basic conventions, but also with evaluations on the logic, cohesion, and clarity of the work; teachers can also electronically edit student’s work alongside this system’s suggestions, and view an assessment tool that analyzes student progress.

Finally, automated tools help educational organizations save resources,  such as saving hours of teachers and instructors, and spending less money on hiring additional staff for assessment activities. Technology has made these educational processes much more straightforward, thanks to intelligent automation capabilities. By using artificial intelligence, educational institutions can use their resources responsibly to make the best decisions for their students and employees.  

Bottom Line 

Automation brings plenty of benefits to the education sector. It is a godsend not only for teachers but for admin staff, too. Advanced technologies and automation give them more time to work on providing students the best education possible while ensuring efficiency, productivity , and transparency. At the same time, those same students have plenty of rest from all their busy classes and more room for studying their lessons!

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Automation inspiration

Automation for education: Less time record-keeping, more time with your students

Running an accredited educational organization, I know the sheer volume of record-keeping, compliance, and reporting obligations institutes face. It's no surprise that teachers and admin teams lose valuable time in data entry, tab-switching, and copy-paste.

Anyone who's worked in an accredited education setting knows about the mountains of information, records, and resources that institutes have to maintain. We love teaching, yet we dread the record-keeping that comes with it. The student should be the centerpiece of education, but all the data that needs to be shifted around results in what I call a "wall of administrivia," blocking students and educators from connecting and learning.

There are countless opportunities for automating and integrating the apps that teachers and administrative teams use. 

Automation helps us get the right data into the right systems, error-free and in near real-time to ensure regulatory compliance. Teachers can collaborate as a team around students, especially when marking assessments and providing support.

Some of our most valuable and time-saving automated workflows were created and tested in under 20 minutes without needing to code.

This has been one of the greatest returns on investment for us. As skilled and qualified specialists, it makes little sense to waste precious time clicking, typing, copy-pasting, and tab-switching. Every minute wasted on easily automated manual data work is valuable time lost for tuition, support, and learning.

To say we're obsessed with reclaiming that time is an understatement.

Breaking down the wall, one brick at a time.

Rather than planning and rolling out a massive automation effort, I have found that it's best to target one task, even a sub-task, and automate that before moving on to the next. This has four key benefits:

It's SMART ( S pecific, M easurable, A chievable, R ealistic and you can get it done in a T imely manner).

Your automation skills expand greatly as you successfully implement each one.

It's easier to monitor and manage a simple automation, and fix any issues.

What should I automate in an educational organization?

If you're seeking inspiration for your first automation project, here are some tips I've found particularly useful when automating at Accellier Education:

Listen . Talk to your teammates, teachers, admin, and faculty staff. Find out what frustrates them in their data entry or processing work.

Observe . Sit with staff and watch them perform common tasks (you can do this remotely using screen share, too).

Reflect . There are probably already things in your role that you wish you didn't have to do. What are some repetitive little jobs you find yourself avoiding? What parts of those could you automate? 

One of the first things I automated was creating a new Trello card when a student submitted an enrollment form on our website. Enrollment is an extremely important and high-value activity, so it must be well-managed.

Create Trello cards from new Gravity Forms submissions

Enrollment is a special and exciting time for us and our students. We want it to be high-touch with lots of communication so the student is supported and valued at every step. One of the worst things that could happen is a student slipping through the cracks due to human error. With automation, this risk is practically eliminated.

Trello enables us to effectively manage and collaborate around the student enrolment and onboarding process, as they progress through important stages like funding, approvals, interviews and admissions, welcome, and getting settled into their course.

You can set up similar workflows using other apps, like:

Create Asana tasks from new Google Forms responses

Create Trello cards from new Typeform responses

How to identify automation opportunities

I like dorky acronyms. (Hey, I'm a teacher!) When identifying automation opportunities, I look for tasks or workflows that have VURVE :

Volume: The task happens a lot.

Uniform: The task is accomplished in a similar way every time it happens.

Repetitive: It's a task often repeated in succession (think tab switching, copy-pasting, or data entry)

Valuable: The task is important to your organization and is an important part of delivering value to your students or team.

Error: There's risk introduced from human error or inaccurate data entry (especially important for compliance record-keeping tasks!)

I've found the following 10 areas have the most ' VURVE-Y ' tasks, and they're usually all critical, high-value aspects of an education provider's operations:

Promotion, digital marketing, social media and sales

Enrollment processing and student onboarding

Fees and payments

Assignment marking (particularly evidence retention and record-keeping)

Teaching-related administrative work (attendance and participation records)

Student support and engagement activities

Scheduling, allocating, and managing resources

Completion, graduation, certification, and credentialing

Compliance house-keeping

What's the value in automation for universities and colleges?

While researching vocational educational providers in Australia for a conference presentation, one of the common themes that emerged was a sense that automation was perhaps too complicated or expensive. People often seem to think that embarking on an automation project is a massive and expensive undertaking requiring IT experts and expertise. People also struggled to see the value in it.

To get around this perception, I recommend evaluating with questions like this: "How much time would we save each year if we halved the time spent on a three-minute record-keeping task performed 10 times each day by five teachers?"

This is not an uncommon scenario. Perhaps it's entering grades or attendance into a spreadsheet and then sending that off to the faculty head. Or maybe it's retrieving student data from multiple systems to determine their progress.

In this scenario, if we shaved just 1.5 minutes off that repetitive job for five teachers, over the course of a year, the institute reclaims the equivalent of one person working full time for over seven weeks. That's highly valuable, skilled time. Time that could be spent creating and updating course resources, or simply more time connecting with students.

Three ways to use automation in education

Fostering real human-to-human connections between our team and our students has been a huge factor in our success. My favorite automations are ones that help accomplish this. While our carefully refined onboarding process is probably the single most valuable automation process, here are three more:

1. Using Trello to manage certification processes

For this automation, our Student Management System, Weworkbook, fires off what's called a 'webhook' once a student has satisfied all course requirements. Zapier catches the information in this webhook and starts the workflow to add a new card in Trello, with a special graduation checklist. Once the checklist is completed and the card moved to the 'Graduated' list, it triggers another Zap, to update our CRM, and sends a message to the student telling them their certification is on its way.

(While we issue badges and digital credentials automatically, of course, we think getting a beautiful, elegantly designed qualification on nice thick parchment is like a music lover getting their favorite album on vinyl.)

Create a Trello card when a webhook is received

2. A 'drip-feed' welcome message sequence for Canvas LMS

Canvas learning management system (LMS) doesn't provide a Zapier integration, so we built one ourselves using Zapier's developer platform. While this isn't for the beginner, it's still relatively easy with a basic understanding of JavaScript and REST APIs.

The benefits of this automation are:

It uses Canvas' internal messaging tool, not email, so it gets students onto the learning platform early on

It doesn't overwhelm the student with one big message

It builds engagement especially in that critical first week

3. Graduate announcements in our team Google Chat channel

This is a simple automation you can create with Zapier. It's a lot of fun for us as it symbolizes much of our mission. Our team loves celebrating the success of our graduates.

Create messages in Google Chat for new cards in Trello

When the graduation card is moved to the 'Graduated' list in Trello, Zapier automatically posts a message in Google Chat (you could also do this in Slack or Microsoft Teams).

Send Microsoft Teams channel messages for new Trello cards

Send Slack channel messages when new Trello cards get moved to lists

This gets our team chatting about the joy of achieving our mission. It's a huge morale boost for us, and often as a result of this simple little message, someone will surprise the graduate with a phone call or a personalized congratulatory video or note.

Improve systems for a streamlined business

Often, automation can help you achieve the same objective in a more streamlined way. Our automation journey has been an opportunity to review and modernize our systems and give us time back to spend on the things we love about our work, while ensuring administration and compliance requirements are still managed effectively.

Zapier gives us the power that was once only available to big organizations with big budgets. The benefits are more than just dollars and time. We've connected and automated systems for administration and enrollment allowing us to give students a better experience through fast, attentive, accurate, and valuable service.

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Paul Pellier

Paul is an education professional and a total nerd with a software and digital media background. He loves crafting streamlined, highly automated business systems, freeing his team to do what they do best—serve clients with excellent learning experiences.

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• Gravity Forms

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How To Incorporate Automation Into Your Education Tech Stack

4 steps to incorporate automation into your learning tech stack, key challenges when incorporating automation.

As the education industry continues to evolve, leaders are turning to technology to improve operations and enhance the student experience. One key area of focus has been incorporating automation into educational technology stacks. Automation has the potential to transform how education institutions operate by reducing manual workloads, freeing up time for staff to focus on more strategic initiatives, and providing a more personalized and efficient student experience.

Gartner predicts that by 2026, robotic process automation (RPA) and artificial intelligence (AI) will improve the student experience while reducing staff and faculty by over 20% per full-time student [1] . This statistic alone underscores the need for CIOs in the education industry to gain a more comprehensive understanding of incorporating automation into their operations.

In this article, we'll explore the best practices and strategies for incorporating automation into your learning technology stack using insights from Gartner's "4 Steps to Hyperautomation Success in Higher Education." [1] We'll cover the key steps you need to take to help you make the most of this powerful technology and position your organization for success in the years ahead.

What is automation in education?

Automation in education is using software to complete tasks that were previously done manually. In education, automation is commonly used to streamline administrative tasks , enhance the online learning experience, and manage student data efficiently.

Hyperautomation takes automation a step further because it is a disciplined approach to automating as many processes as possible. This involves the orchestrated use of multiple technologies, tools, and platforms, including artificial intelligence , machine learning , event-driven software architecture, robotic process automation , business process management , low-code/no-code tools, packaged software, and other types of automation tools. 

For any change, the process is reimagined with an automation-first outlook.

So why should CIOs care about incorporating automation into their learning tech stack? The answer is simple: to optimize and modernize their technology environments. By incorporating hyperautomation, higher education CIOs can streamline processes, reduce manual tasks, provide a more personalized and efficient student experience, and free up staff time for more important initiatives.

When incorporating automation into your learning tech stack, it's important to take a strategic approach to ensure success. This approach can be broken down into four key steps:

Step 1: Define the desired business outcome

When institutions incorporate automation tools, they usually encounter several challenges, from the initial "how to get started" stage to "scaling up." To address these challenges, higher education CIOs must work closely with stakeholders to identify the business problems the institution is trying to solve with a thorough business process analysis.

Institutions have various goals that range from improving enrollment rates to streamlining procurement processes or eliminating paper forms. For instance, an institution may want to automate its recruitment process to improve the prospect and application experience, resulting in a higher conversion of prospects to applicants. To achieve this goal, the institution can provide quick answers to common prospect questions via chatbots and nudge prospects to complete applications.

Or an institution may want to streamline the procurement process. RPA software can automate supplier checks, ensure greater consistency and fewer errors, and make sure there are no conflicts of interest.

The institution's goal could be to eliminate paper forms for faculty-facing processes to automate the faculty onboarding process. Low-code software platforms and workflow software can effectively reduce development time for forms and workflows, prefill forms from university systems, and route the results effectively so action can be taken.

Step 2: Redesign the process

Once it's been decided that automation is necessary, the next step is to redesign the process to ensure it meets the desired business outcomes. This step can help organizations identify inefficiencies and bottlenecks that can be addressed before any automation tools are selected.

The redesign process involves considering four options: enhance, convert, eliminate, and hide. These options are not mutually exclusive and can be combined to achieve the desired results. However, higher education CIOs should not underestimate the time and effort needed to complete the redesign process. Many processes cut across internal teams that may not see the end-to-end process and may be protective of their part of the larger process:

Enhance: Identify steps that you will keep but could work better. Ask yourself what "better" would look like. Is it faster, less expensive, more transparent, or more accurate?

Convert: Identify steps that can be converted to another method. Are there steps that require manual keying today but could be automated because the data doesn't change and the process is static?

Eliminate: Identify steps that can be removed. For instance, you can remove steps in an approval process where the approver adds no value.

Hide: Identify steps that can happen in the background without human intervention. This step can help make the process less confusing to students or employees, especially when the steps don't affect the outcome.

After the process has been redesigned, leaders should ask specific questions about the new process to determine which forms of automation to implement. These questions should cover the following topics:

Workflow: Determine the process's volume, input, processing path, and time frame, and whether it is internally or externally focused.

Data: Consider data configuration, data availability, and data usage, including any ethical or personally identifiable information issues.

Systems: Consider integrations, training and learning, legacy extensibility, prebuilt higher education content, and the skill level required to implement the chosen automation technology.

Step 3: Choose automation tools by mapping needs to tool characteristics

Once the process has been redesigned, the next step is to choose the appropriate automation tools that match the process requirements. To match the right tool to the specific process requirements, it's important to break down the process into its underlying characteristics. The following are some of the key automation tools and their characteristics:

Key automation tools

Robotic process automation (RPA) : RPA automates repetitive and rule-based tasks. It works best for high-volume, transactional processes with static input and fixed processing paths or where multiple applications need to be integrated.

Analytics : Analytics software enables data-driven decision-making and is best suited for processes that require a historical view or are predictive.

Natural language processing (NLP) : NLP tools enable computers to understand and respond to natural language and are a good fit for processes like customer service or chatbots.

Optical character recognition (OCR) : OCR tools convert scanned images or PDFs into editable text and work well to replace processes that require manual data entry.

Machine learning : Machine learning tools enable systems to learn from data over time and are well-suited for tasks that have a large amount of data.

Chatbots : Chatbots use NLP to provide conversational interfaces for users to interact with and can streamline customer service or support.

Intelligent business process management iBPM) : BPM tools provide a platform for automating business processes, which is good for processes that need end-to-end process management and orchestration.

Low-code solutions : Low-code platforms enable users to build applications without the need for coding and can help streamline processes that require custom applications.

Step 4: Assess business value change

After implementing automation technologies, it is essential to assess the business value of the changes made. CIOs in higher education must align metrics to desired business outcomes, such as increased enrollment, improved student retention, decreased time to graduation, increased fundraising, and other higher education business outcome metrics.

Cost reduction and efficiency may be part of the picture, but they are not the only metrics that should be used. Business outcome metrics such as revenue increases, student satisfaction improvements, cost savings, error reductions, increasing number of users, and compliance improvements are also powerful drivers to engage institutional leadership and the larger university community in the initiatives.

Assessing the business value change after automation is critical, and the assessment should be in terms of specific, quantifiable value statements. By evaluating the business value change and comparing it to the pre-automation state, CIOs can ensure that the automation initiatives are driving the desired outcomes and adjust the approach accordingly.

While automation offers numerous benefits for higher education institutions, there are challenges associated with this initiative. 

One of the most significant challenges is the need to balance efficiency gains with other important institutional priorities. Higher education CIOs must strike a delicate balance between the need for cost savings and the importance of maintaining quality, retaining students, and securing research grants.

Another challenge is the complex and rapidly changing technology landscape. Higher education institutions face a wide range of technologies with overlapping features, and selecting a single tool for any specific situation can be difficult. It is crucial to avoid becoming fixated on specific technologies, such as AI or chatbots, and to build an IT culture that is focused on the best functionalities of each technology to solve a business problem. An array of technologies can have an exponential impact compared with automation technologies that are adopted in isolation.

Institutional culture can also be a barrier to successful automation implementation. Many higher education institutions have a culture that is resistant to change, making it difficult to gain buy-in from stakeholders.

Finally, higher education institutions must also grapple with data security and privacy concerns.

Automating business processes involves collecting and processing sensitive data, and institutions must ensure that they have the necessary safeguards in place to protect this data from cyber threats and other risks.

While automation offers numerous benefits, it is important to approach implementation with a clear understanding of the challenges that must be overcome. By aligning metrics to desired business outcomes, building an IT culture focused on the best functionalities from each technology, gaining buy-in from stakeholders, and implementing appropriate data security measures, higher education institutions can successfully harness the power of automation to drive efficiency, quality, and innovation.

Are you ready to automate?

It's important to plan and implement automation initiatives strategically, following a series of steps such as assessing the current state, identifying areas for improvement, selecting appropriate technologies, and assessing the business value change.

Higher education CIOs must also align metrics to desired business outcomes and build an IT culture focused on identifying the right tool for the right automation task rather than being obsessed with specific technologies.

By taking these steps and addressing the key challenges, higher education institutions can successfully leverage automation to achieve their strategic goals and remain competitive in this rapidly evolving landscape.

To learn more about modernizing your education system, check out these articles: 

Modernize Higher Education with Student Information Systems

How to Ensure New School Initiatives Succeed

4 Steps to Hyperautomation Success in Higher Education , Gartner

Making Sense of the Digital Automation of Education

• Published: 23 November 2022
• Volume 5 , pages 1–14, ( 2023 )

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• Neil Selwyn 1 ,
• Thomas Hillman 2 ,
• Annika Bergviken-Rensfeldt 2 &
• Carlo Perrotta 3  

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Introduction

The ongoing digitization of education is ushering in all manner of changes to schools, colleges, and universities—not least the absorption of small (often imperceptible) automations into everyday educational practises and processes. Now, for example, the default setting of a learning management system might be to automatically cross-check any submitted written assignments with plagiarism detection software in order to generate a measure of ‘originality’. Some learning management systems might then link to automated-essay-grading software to quickly produce a set of provisional scores for a human grader to later orientate their own judgements against. At the same time, students writing these assignments might have incorporated neat turns-of-phrase suggested to them by their word processor ‘autocompletion’ feature, and might even have also run their writing through another plagiarism detection system to gauge the chances of being caught. Elsewhere, many classrooms have ‘student activity monitoring' software running in the background—monitoring keystrokes and online activity to judge if classes are actually engaged in their work or not. Whereas all these small tasks of checking, noticing, choosing, and deciding might once have been the responsibility of a school teacher or university tutor, now they are increasingly delegated to technology.

Such automations have become quickly woven into the digital fabric of schools and universities to the point that they now pass unnoticed. This Special Issue of Postdigital Science and Education challenges us to pause and pay renewed attention to these automations. The papers in this issue prompt us to ask what is going on when such tasks, while they may seem small, are delegated to software. These papers also prompt us to question the reasons why digital automations of education are being enacted, with what gains, and with what losses. While such questions might seem obvious, this has to date proven a curiously uncontroversial and non-contested area of education. On one hand, this lack of controversy reflects the seemingly small scope and limited significance of many of these automations, as well as the incremental nature with which they build up. Yet, on the other hand, there are also understandable reasons for educators to be broadly enthusiastic about this technology. For example, many educators have so far been happy to welcome technological advances purely on the grounds of presumed efficiencies and timesaving. Conceived along these lines, there is perhaps much to be hopeful about—not least the promise of digital technologies taking on some of the repetitive, mundane busy-work that many educators feel have come to blight their working lives. Why not let algorithms ‘free up’ teachers to concentrate on the higher-level pedagogical work that they have been trained for?

Running alongside these labour-saving arguments is the suspicion that automated technologies might actually be capable of doing a better job that a human teacher. For example, automated essay grading software can certainly process student assignments at an unimaginable scale and speed in comparison to even the most adept human grader. There is also understandable hope that many processes might be executed more fairly and more consistently than is possible when left to human educators alone. As Marion Fourcade ( 2022 ) puts it, this is the hope that ‘the divination of the algorithm [can] replac[e] the arbitrariness of the bureaucrat’. While many readers of a journal such as Postdigital Science and Education might feel that such arguments are highly contestable, we should not discount them altogether. In many cases, digital automations will faithfully re-instantiate the steps and rules already instantiated in the forms, frameworks, and guidelines of the existing bureaucracy. As with all technological innovation, digital automations of education are not a wholly bad thing and, in many people’s eyes, are certainly no worse than the non-automated arrangements that precede them.

The broad acceptance of these technologies to date reflects a general expectation amongst most education professionals that the full automation of pedagogical work (and therefore the complete replacement of teachers by machines) is extremely unlikely to occur. Most educators remain confident that their work is too complex and contextual to be completely taken on by automated digital tools. Certainly, while some routinised factory workers might well have cause to fear what Aaron Benanav ( 2020 ) describes as full ‘lights out’ automation (i.e., workplaces requiring no human presence so that the lights can be turned off), most people would agree that schools, colleges, and universities continue to be sites of intricate knowledge work that is centred around relational, embodied human activity. As such, it makes sense to anticipate the partial automation of schools and universities—populated by what Benanav describes as ‘labour-augmenting’ technologies. Most people in education, therefore, remain fairly comfortable that extreme predictions of fully automated classrooms and ‘robot teachers’ remain way off the mark. Instead, it is generally accepted that the challenge for contemporary education professionals is figuring out how best to work alongside (or ideally, work along with) these technologies.

That said, we need to avoid making the mistake of framing these discussions purely in terms of teachers and teaching. To date, the most pervasive forms of educational automation relate to the administration, management, and governance of education. For example, recent years have seen the Indian Ministry of Education implement computer-based automation to administer the country’s vast annual parent-school application procedure and filter out erroneous cases. Similarly, university admissions teams around the world now routinely make use of algorithmic sorting to draw up initial long lists of prospective student applications. Of course, many of the decisions now being delegated to digital systems are far more mundane than these potentially life-changing examples. For example, newly built school campuses are now often fitted with heating and lights that switch on under certain conditions, and visitor management systems that recognise the faces of enrolled staff and students while denying entry to strangers. In this sense, any talk about the ‘automation of education’ covers a wide range of automations that have been developed for a wide range of purposes and are resulting in a wide range of consequences. Thus, our conception of automation needs to stretch well beyond the particular domain of teaching and pedagogy, and also take note of arguments arising around other institutional, organisational, and social contexts outside of the school and university.

Questioning the Efficiencies of Education Automation

In one sense, the digital automations covered in this Special Issue might simply be rationalised as a necessary aspect of education systems keeping up to date with the rest of society. After all, similar automations are increasingly evident across many other areas of everyday life and society. Yet, the papers in the Special Issue convey a collective warning against taking these promises of automation in education at face value. It is well worth considering how and why the realities of these automated technologies might not always be fully meeting expectations. Such scepticism should not be too difficult a step for most people to take. Indeed, the fallibilities and frailties of digital automation should be obvious to any smartphone owner who has experienced their device’s autocorrection and autocomplete features suggesting wildly inappropriate or insensitive phrasing. More seriously still, we have become accustomed to reports of facial recognition systems having a hard time recognising people of colour. Such glitches, errors, and failures have been long noted in education. For example, Les Perelman’s work has shown how carefully arranged screeds of gobbledygook can nevertheless be awarded top marks by automated-essay-grading systems (see Perelman 2022 ). We ignore such shortcomings and omissions at our peril. These are not simply annoying episodes or amusing anecdotes of technological teething troubles.

Of course, from a purely technical point of view, it could be countered that these failures are all acceptable (if not expected) features of the advanced forms of machine learning that lie behind contemporary digital automation. After all, human judgement and decision-making are not perfect either and, in simple terms, machine learning involves the development of mathematical models that are refined when confronted with errors. In effect, these systems are designed to fail on a large scale in order to improve. This kind of machine learning has prevailed as a form of AI over more conservative or ‘tame’ forms built around knowledge representations such as intelligent agents where human expertise and wisdom is systematically modelled. As such, many of the automated technologies currently being installed into schools, colleges, and universities are certainly not elegant, sensitive intelligent representations of human expertise and action. As Hillary Mason ( 2018 ) reminds us, these technologies do not somehow constitute a form of ‘inscrutable magic’—rather this is technology that is the product of ‘math and data and computer programming, made by regular humans’.

However, in the case of deep learning, which can be argued to be the prevailing form of machine learning of the 2020s, there is an inscrutability to the internal processes of the mathematical models generated that can give a misleading impression of ‘more-than-human’ sentience. Instead, it is important to see these seemingly inscrutable processes as the aggregation of sometimes hundreds of relatively simple and understandable operations. The output of each of these operations is trained through ‘brute force’ trial-and-error working with massive datasets and together they produce what appears to be a sophisticated result that is evaluated in relation to the assumptions made by developers. In this sense, the inscrutability of the technology derives from the sheer number of simple operations, and not from any sort of magical insight.

This spirit of statistically-relentless and socially-insensitive computation is exacerbated by the commercially-driven nature of how these technologies are sold and rolled-out into educational settings. In his interview piece for this Special Issue, Mark Andrejevic (in Andrejevic and Selwyn 2022 ) calls out education's ‘almost incomprehensible readiness’ to hand control over to the private actors who design and market automated decision-making software and systems. Of significance here, then, are the ways that subsuming authority to automated technologies opens education up to a number of prevailing ‘Big Tech’ logics. For example, in a commercial sense, any person coming under the gaze of an automated system (regardless of that person’s background or circumstances) tends to be framed as a ‘user’ operating wholly within a bounded digital ecosystem. This also implies a ‘ politics of scaling ’ —in other words, the ambition to develop education automations that move beyond localised actions, and instead develop into scalable, universalised actions that can dominate markets on a society-wide basis (Pfotenhauer et al. 2022 ).

Also of significance is what can be described as the Big Tech ‘ politics of modularity ’ (Birch and Bronson 2022 )—where technical standards for interoperability are exploited so that any external automated service can be easily ‘plugged in’ to a school or university’s broader digital infrastructure, creating a logic of just-in-time, unbundled automation where specialised services and resources are assetized and rented out on an on-demand basis (see Hansen and Komljenovic 2022 ). Indeed, one argument that emerges across this Special Issue is that such commercially-driven logics fundamentally clash with what might be traditionally seen as public education values and logics—promising a new form of education based around principles of contingency, compatibility, standardisation, and consumer-led provision of ‘education as a service’. In this sense, digital automation might not simply be a benign, neutral addition to existing ways of doing education. Instead, much digital automation presumes a radically reconfigured form and function.

Holding the Automation of Education to Account

Such is the scale and scope of the ongoing digital automations of education that our discussions urgently need to move beyond simply speculating on whether all of this is a ‘good thing’ or not. Given the extent to which these technologies are already being used, it is perhaps best to start reflecting on how we might want to best live with, gain control of, and/or fight against these technologies. As such, the most pressing debates to be having at this juncture are pragmatic discussions around technological regulation, governance, and even resistance. This reflects what Frank Pasquale ( 2020 ) has termed the ‘second wave’ of algorithmic accountability, which involves raising questions around the responsible integration of these systems in society. Indeed, Pasquale’s ‘new laws of robotics’ offer an excellent starting point for such discussions—a challenge first outlined in Pasquale’s interview for this Special Issue (Pasquale and Selwyn 2022 ) and subsequently picked up in the group discussion paper coordinated by Mathias Decuypere and colleagues (Decuypere et al. 2022 ).

Indeed, many of the papers in this Special Issue stress the need to begin asking difficult questions of these technologies—refining expectations of automated education as a problematic to be investigated, rather than a problem to be solved. In other words, discussions of automation in education need to be deliberately pushed toward fundamental questions after Emily Bender ( 2022 ) of what is being done in education in the name of ‘automation’, to whom, who benefits, and how democratic oversight might be exerted. In this spirit, then, we can conclude by considering a few different lines of thought that emerge from the papers in this Special Issue, and along which we might want to continue to hold automated technologies to account.

The Harms of Educational Automation

First and foremost in holding automated technologies to account is the need to highlight instances when automated systems result in obvious social harms within educational contexts. As is now widely acknowledged, some of the most substantial harms arising from digital technologies over the past few years have occurred when AI models amplify the discriminations baked into their training data and subsequently drive autonomous systems to discriminatory and disadvantaging ends. Various instances of this have already occurred in education, such as voice analysis systems erroneously judging students with non-native accents to be cheating on English-language tests, and exam prediction models consistently computing higher automated grades for students attending fee-paying schools (e.g., National Audit Office 2019 ).

Other harms might appear less substantial, yet nevertheless also need to be taken seriously. Take, for example, reports of online exam proctoring systems failing to detect the faces of Black students, or non-binary students having to mis-identify themselves as either ‘M’ or ‘F’ in order to register a ‘valid’ system profile. While such exclusionary glitches might not be designed deliberately into technologies, they effortlessly replicate and reinforce discriminations that minoritized students encounter regularly throughout their engagement with education systems. All told, perhaps the most pressing matters to be discussing about the roll-out of autonomous technology throughout education relate to how any data-driven system is imbued with ‘a tendency to punch down: that is, the collateral damage that comes from its statistical fragility ends up hurting the less privileged’ (McQuillan 2022 : 35).

Crucially, it is also important not to presume that these harms can somehow be ‘fixed’ and made ‘fairer’ through the curation of more inclusive training datasets, or more mindful approaches to progressive software design. Instead, educational discussions around this technology need to take heed of growing arguments—led by scholars such as Joy Buolamwini, Safia Noble, Simone Browne, Ruha Benjamin, and Timnit Gebru—that frame digital automation as exacerbating forms of ‘engineered inequality’ (Benjamin 2019 ) in already inequitable social contexts. This points to the ways in which AI technologies result in inevitably oppressive and disadvantaging outcomes ‘given their design in a society structured by interlocking forms of domination’ (Benjamin 2019 : 47). Following this logic, it is highly unlikely that automated systems implemented in already an unequal or discriminatory education context will somehow lead to radically different liberatory and emancipatory outcomes. Instead, it is most likely that any automations will lead to amplifications and intensifications of existing tendencies and outcomes.

This also raises the point—evident in a few of the papers in this Special Issue (e.g., Swist and Gulson 2022 ; Gibson 2022 )—that harms associated with automation in education are profoundly relational in nature, and therefore likely to be experienced differently according to individuals’ different backgrounds and circumstances. Any instance of some people being disempowered and disadvantaged by the implementation of digital automation in education is accompanied by other people being empowered and advantaged. As such, any particular automated technology might appear to work perfectly well for many teachers and students. Nevertheless, for many others, the same technology can simultaneously be experienced in profoundly less advantaging ways. This raises the need for discussions to progress beyond broad-brush concerns over presumed forms of universal harm arising from AI technologies—for example, what Viljoen ( 2021 ) terms ‘dignitarian’ fears over a general ‘dehumanisation’ of education processes and practises. Instead, more detailed questions need to be asked about the localised harms being experienced by specific individuals and groups. Regardless of our own personal positive experiences, we need to remain aware of the embedded socially differentiated ways in which education automations can impact on others in harmful and deleterious ways.

The Reductiveness of Educational Automation

Second is the need to pay close attention to the aspects of education that are diminished, de-emphasised, and side-lined in any educational turn to automation. More specifically, this requires us to problematise claims that it is possible to statistically model, monitor, and compute educational events without the inherent reduction of key qualities and characteristics. Of course, most technology developers would contend that it is possible to construct ‘epistemologically sound’ mathematical representations of any aspect of education as it is experienced and engaged in by students and teachers. For example, automating a bounded situation in a school or university ‘smart campus’ might require a small set of logical steps—an authorised student approaches a door, they are recognised by a smart-camera, and the door opens. However, it can be reasonably contended that there are many educational events and situations that are not possible to codify and model in this neatly defined and bounded manner. For example, while some aspects of the judgements that go into evaluating a piece of written work might be readily automated, the breadth of human judgements and expertise that lie behind the act of grading a written assignment might be reasonably seen as irreducible to even to the most contextually rich, complex statistical model.

At best, then, many automated systems in education are inevitably programmed to make narrow decisions on the basis of limited ‘proxy’ variables and, therefore cannot be seen to be fully sensitive to the social context of the process or practise being automated. As such, it might be argued that the data used to drive digital automations can never constitute an epistemologically sound representation of any human condition but are instead simple ‘signifiers whose referent is no longer a human subject but a cluster of correlations’ (Bolin and Andersson 2015 : 4). The concern here is that any instance of arranging and enacting a ‘real life’ education situation through computer-based models and simulations of the real-life situation will inevitably alter the fundamental human experience of education. As a result, one’s accumulated encounters with these various constrained automations begin to comprise a notably ‘narrow bandwidth’ experience of schooling.

For some, this reductiveness is a fatal flaw in the current turn to digital automation. In short, it can be argued that these are systems that promise short-term effectiveness in narrow application domains while being defective at a more fundamental level of human sociality. Of course, some people remain happy to anticipate AI and other automated technology fast advancing to a state of being somehow capable of a ‘framelessness’ (Andrejevic 2020 )—the fantasy of a perfectly complete, totally comprehensive ‘1:1’ capture of all human experience that then can be used to drive fully sentient technologies. Even if this framelessness were possible, it side-steps some basic aspects of human thought—such as Karl Popper's ( 1994 / 2001 ) observation of the inherent provisional and unreliable nature of human experiences, along with the fact that knowledge arises from the capacity to make inferences from information rather than simply having the information to hand. As such, the fantasy of 1:1 data does not do away with the human need to make inferences (that is, to impose ‘frames’), but simply replaces human forms of inferencing with computer-driven forms of inference. These are not more effective, efficient, or free from bias, but more often opaque and just as likely to be coloured by the biases involved in human inferencing. As implied in recent critiques of automated language systems as ‘stochastic parrots’ (Bender et al. 2021 ), we automate complex human processes such as sense-making and meaning-making at our own risk.

The Losses Inherent in Educational Automation

Another important question that needs to be asked of digital automation in education, is one of loss—as Moser et al. ( 2022 ) put it, ‘what do humans lose when we let AI decide?’. In his interview piece for this Special Issue, Mark Andrejevic (in Andrejevic and Selwyn 2022 ) frames this in terms of Harold Innis’ ( 1951 / 2008 ) notion of ‘media bias’—i.e., how any automated technology that is chosen to be used in a classroom will be designed around particular time/space arrangements, forms of knowledge, and sets of power relations. In this spirit, it is worth considering a number of possible reductions, elisions, and substitutions when it comes to the automation of education.

One key loss is what might be described as the replacement of judgement with reckoning. As Moser et al. ( 2022 ) reason, dominant justifications for the deployment of automated systems in social settings tend to conflate reckoning (i.e., decision-making based on summing up of various kinds of data, computation, and rule-driven rationality) with judgement. Genuine human judgement, Moser argues, is dependent on reasoning, imagination, reflection, and empathy. This is echoed in Andrejevic’s contribution to this Special Issue that the non-predictability of human judgement sets it apart from machine-based automated decision-making (Andrejevic and Selwyn 2022 ).

Such concerns are illustrated, for example, in the ways in which automated essay grading software essentialises what a human essay grader does in a mechanised procedural manner. As Siddarth et al. ( 2021 ) put it, this is technology that ‘mechanises our conception of human capacities’. Yet, as Wagener-Böck et al. ( 2022 ) argue in this Special Issue, when examined in practise, much of what is conceptualised as automation in education is actually a form of ‘symmation’ where humans and technology co-produce the effect of automation. In this sense, the technology not only conceptualises human capacities in mechanistic ways but also shapes practises so that the conception is performed. As is suggested throughout this Special Issue, even the smallest tasks carried out by teachers and students in educational settings have important social and relational qualities that are dispensed with when these actions are codified as ‘tasks’ and automated accordingly.

A similar conflation might be observed in terms of the replacement of discovery with retrieval —the former depending on an element of serendipity and fortuity that is not present in the ‘recommender systems’ that point us to already known content, driven by correlations and patterning. As Dwayne Monroe ( 2021 ) contends, applying statistical methods to already collected data is of no use in terms of developing capabilities such as inquisition, intuition, and taste-making. At best these systems are ‘mirroring your past behaviour back at you, making it harder to find something truly new’. As several of the papers in the Special Issue also suggest, we, therefore, need to question the assumptions taken in the development of automated systems in relation to their outputs and effects on education. To do this, we need to resist the temptation to get caught up in the apparent precision of a system’s data visualisations, the certainty of its reporting, and/or the definitiveness of the predictions that it purports to provide teachers and students. Indeed—to paraphrase Knott ( 2022 )—‘the algorithms driving these apps give the impression that they see the essence of [education] and render it accurately. But we’re only really seeing it through the financialised language of their creators’. (emphasis from the original)

Seeing Educational Automation in Terms of Power

Following on from these latter points is the need to explicitly frame the automation of education as entwined with dynamics of power and control. This pushes us to move beyond socially concerned talk of ‘harms’ and similar notions of (dis)empowerment, to deliberate conversations around the politics of automation that are imbued with a sustained deliberation of power. Here, we might follow Verdegem’s ( 2021 ) focus on the symbolic, political, and economic forms of power implicit in AI and digital automation. Take for example the symbolic power that derives from meaning-making and influencing the actions of others. In terms of digital automation, this is certainly evident in what Verdegem terms ‘AI ideology’—i.e., the ways in which human consciousness is manipulated to see digital automation as an important (if not inevitable) means of determining future forms of society and/or economy. Current dominant forms of AI ideology range from vague progressive notions of how digital automation might foster new forms of a ‘good society’, through to the boosting of fully automated work as the basis for a new era of ‘digital capitalism’. How particular voices and interests are able to drive these different societal understandings of what education automation is (and what education automation is for) is therefore a key form of contemporary power that we need to pay close attention to.

At the same time, digital automation is clearly linked with political power that derives from having the authority to coordinate individuals and their interactions. This political power is evident in what Verdegem ( 2021 ) terms the ‘social practises’of AI. These include the ways in which developers, programmers, and engineers choose to select training data and design the classification systems, models, and procedures that drive processes of AI and more specifically machine learning. Who gets to be involved in these design processes, who gets to determine what is included/excluded in the development of these systems (e.g., how things are represented through data), and what value is then extracted from their continuous refinement, are therefore all key forms of political power that are fast coming to bear on education.

Clearly, these technologies are also entwined with forms of economic power—power that derives from accumulating resources for productive activity. In terms of the digital automation of education, this form of power is perhaps most obviously exercised through ownership of the data infrastructures and computational power that underpins the development and deployment of AI systems and automated-decision-making technologies in education contexts. This form of power also lies in the expert data science and human resources involved in the development of these technologies. As such, a number of papers in this Special Issue (e.g., Gilliard and Selwyn 2022 ; Andrejevic and Selwyn 2022 ; Hansen and Komljenovic 2022 ) make the point how the digital automation of education involves the centralization of power and the commercial agendas of IT industry and ‘Big Tech’ actors, alongside a corresponding sidelining of education institutions, educators and other stakeholders. This chimes with Birch and Bronson’s ( 2022 ) argument that we need to give careful thought to how firms such as Amazon, Microsoft, and Google act as powerful ‘gatekeepers’—both in economic terms and in terms of shaping the digitization of public services such as education. Therefore, there is much to discuss here—from how the educational ambitions of Big Tech actors might be regulated and curtailed, through the need to politicise matters of digital infrastructure and resource ownership (Jäger 2019 ).

A number of papers in this Special Issue also explore the specific contention of how the outcomes of automated technology fold into state and structural power (e.g., Swist and Gulson 2022 ; Gilliard and Selwyn 2022 ). This chimes with the description emerging from various critical commentators of digital automation as a key part of the ‘synthetic governance’ of education—a form of control that does not pursue human replacement, but standardisation, docility, and the flattening of practise (see Gulson et al. 2022 ). This is evident, for example, in the ways that automated systems demand a legibility from teachers, students, and classrooms—i.e., outputs and outcomes that are ‘machine readable’ and ‘parseable’. As Legacy Russell ( 2021 ) reminds us, ‘demanding legibility is a strategy of the state’. The legibility demanded of teachers and students from education technologies, therefore, infers a mode of continuous accountability and possible coercion to act differently.

As Andrejevic notes in his interview piece (Andrejevic and Selwyn 2022 ), the logic of behavioural economics and ‘nudging’ inherent in automated media suppose that people’s behaviours can be internalised in ways that conform to a bounded model of the algorithmically rendered classroom. In this sense, automated technologies are concerned with exercising power over the actions and activities of the teacher, rather than the complete replacement of the teacher. As Anton Jäger ( 2019 ) concludes, any talk of the total replacement of the teacher, therefore, constitutes an act of class discipline—the threat of ‘we can replace you at any moment!’ needs to be seen as a disciplinary move rather than an actionable possibility.

Acknowledging the Futures and Histories of Educational Automation

A final set of ideas worth extracting from the Special Issue contributions is the call to engage both with the possible futures of digitally automated education and the preceding histories of pre-digital automation. To date, academic commentators have perhaps proven most comfortable with speculating and engaging with the futures of digital automation. Indeed, various papers in this Special Issue raise a number of pertinent points around continuing these future-facing discussions. For example, as papers by Cerratto Pargman et al. ( 2022 ) and Robinson ( 2022 ) show, there is merit in engaging with the many various predictions, speculations, and expectations around the possible future automations of education—not least in highlighting how talk of the imminent automation of education acts as a means of providing commercial actors and commercial logics with legitimacy. Indeed, from a sociology of expectations perspective, engaging with corporate and policy predictions can be a generative way to examine how corporate actors use ideas of the future to ‘guide activities, provide structure and legitimation, attract interest and foster investment. They give definition to roles, clarify duties.’ (Borup et al. 2006 : 285–286) As such, some of the papers in the Special Issue give a strong sense of the ‘sociotechnical imaginaries’ around education automation being advanced by governments, corporations, and other stakeholders in the ongoing digitization of education.

Nevertheless, we need to resist any temptation to play along too closely with the dominant forecasts and predictions that inform mainstream understandings and conversations about possible digital futures. Recently, Lee Vinsel ( 2021 ) has derided the ‘academic business model’ of critical researchers ‘playing along with hype to score cash money and prestige’—what Vinsel curtly terms ‘lending credibility to industry bullshit’. This chimes with Alfred Nordmann’s ( 2007 ) concerns with social scientists who develop critiques of ‘if/then’ scenarios that engage credulously with dramatic corporate claims over ‘incredible futures’ driven by ‘technological hubris’. The dangers of engaging with this type of speculative talk around education automation are obvious. Engaging critically with corporate expectations of automated futures runs the risk of reproducing (and even increasing) hyperbole, and lending credibility to industry promotional claims merely by taking them seriously, overstating the abilities of tech firms and/or the capabilities of their emerging products. As well as compounding unrealistic expectations, Nordmann ( 2007 ) warns that work of this nature distracts attention away from already-existing concerns—especially the actual risks and harms highlighted previously in this paper arising from current automated technologies.

Contributing to disrupting hyperbole around the potential benefits and risks of automation, other contributors to the Special Issue, therefore, highlight the need to pay more attention to the many historical precedents to the contemporary automations of education. All the ongoing talk of automatic exam grading, personalised learning, and bureaucratic efficiency has a history that needs to be acknowledged and learnt from. Indeed, prevailing claims, hopes, and fears around the general automation of society in the 2020s are not far removed from the claims, hopes, and fears in the 1950s—such as robots taking jobs, fears over dehumanisation, and hopes for increased precision and safety in the workplace.

Looking at the histories of automation of education, therefore, adds an invaluable dimension to our understandings of the area. As Bergviken Rensfeldt and Rahm ( 2022 ) demonstrate, paying close attention to the politics and technologies of education automations over time, renders visible societal debates, concerns, and resistances that were once prominent but that have since diminished. Revisiting and revitalising these historical discourses can, therefore, shine new light on education automation as it is currently materialised. Looking back over twentieth-century examples from the early attempts to ‘computerise’ classrooms in the 1970s and 1980s, there are many examples of questionable or malfunctioning automations that offer valuable insights on issues such as social justice and fairness (c.f. Park and Humphry 2019 ), as well as on issues around the values of public education and the roles of pedagogical work.

Conclusions

The contributions across this Special Issue combine to give a strong sense of the continued need to tackle the topic of automation and education in socially nuanced, politically aware, and appropriately sceptical ways. These forms of technology—and the logics that underpin them—are now a significant aspect of the digitization of education, and therefore require our sustained attention. As with most aspects of the algorithmic landscape of everyday life, these are technologies that are quickly becoming woven into the digital infrastructure of education—and therefore increasingly ‘invisible and forgettable’ (Knott 2022 ), only revealing themselves when they impact on our lives in notably unfair, harmful, and obtrusive ways. As such, now is the time to be drawing full attention to the reconfigurations of power that are taking place in the name of automation. These are technologies that need to be the subject of ongoing conversations about what we want AI and automated decision-making in education to be, what purposes we can benefit from and are prepared to devolve to technology, and what aspects of education we are definitively not willing to hand over.

All the papers in this Special Issue, therefore, highlight the need to move these conversations away from bald, technical issues of precision, efficiency, speed, and scale. Instead, we need to better acknowledge how the claims and counterclaims around digital automation in education index deeper impulses and desires—around the forms of power, control, and standardisation that people wish to see established in education. Yet these conversations cannot be too dismissive or overly general—slipping from a constructive scepticism to a destructive cynicism of any technological application regardless of the details. As with all critical takes, there is little to be gained from getting bogged down in a technological pessimism amidst the prospect of education automation—seeing all things digital as inevitably broken, and expressing no desire to transform this predicament.

This, therefore, calls on researchers to take an active stance by developing better understandings of how digital automations can be meaningfully integrated into education and adopting a sense of realism (rather than idealism) when reflecting on where we might like to be going next (see Gallagher and Breines 2022 ; Gibson 2022 ). These are technologies that cannot be dis-invented or effectively banned outright. Given that digital automation is already a reality in a multitude of deceptively mundane guises, perhaps the most pressing questions to be asked are disarmingly practical—what do we want automation in education to be, and what is required to make those automations a reality?

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Selwyn, N., Hillman, T., Bergviken-Rensfeldt, A. et al. Making Sense of the Digital Automation of Education. Postdigit Sci Educ 5 , 1–14 (2023). https://doi.org/10.1007/s42438-022-00362-9

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How AI and Machine Learning are Transforming the Education Sector

By AACE Staff for AACE Review, October 22nd 2021

By Pamela Grace, The Coder School

Artificial Intelligence is impacting several industries, including education. It’s transforming the way teachers and institutions work while revolutionizing the learning process for students. According to research, by 2025, AI-powered education will be worth at least $5.8 billion and significantly higher in subsequent years. In this article, we’ll explore how AI is transforming the education industry and its benefits.

What is The Impact of AI on the Education Industry?

Artificial Intelligence is revolutionizing this sector in the following ways:

• Task automation

In addition to managing classrooms, teachers also traditionally handle organizational and administrative tasks. They include filing paperwork, overseeing lecture resources, and creating periodic progress reports. AI has the potential to make their work easier by automating these additional functions.

Statistics show educators spend more than 50% of their professional time performing non-teaching activities. This technology can grade tests, plan lessons, and generally make educators’ tasks better. In addition to increasing their productivity, it also gives them enough time for one-to-one discussions with their students.

• Smart content creation

AI and machine learning tools enable the creation of accurate and targeted educational content. They also use advanced data analytics to create solutions suitable to each student’s capabilities. AI tools can make these decisions based on the learner’s interaction with other apps. This content incorporates virtual solutions such as digital textbooks, lectures, and webinars.

Robots can enhance the learning experience by building interfaces that cater to students in different grades. They make the content understandable by splitting it into intelligible portions, highlighting lessons, and summarizing crucial points. This convenience allows students to achieve their academic goals faster.

• Personalized education

According to research, personalized learning improves your skills at whatever starting level you pick. Students have different characters and approaches. The traditional one-size-fits-all approach to teaching isn’t always fair to all learners.

AI can complement teachers’ efforts by personalizing the process depending on the student’s comprehension level. In addition to making content easier to understand, technology offers recommendations based on their interests and learning difficulties.

Carnegie Learning is an example of such a platform. It acts as a teacher’s assistant that personalizes experience after evaluating your strengths and weaknesses. Apart from providing actionable feedback to learners, they generate periodic reports to help teachers understand their students better.

• Virtual learning solutions

AI is an essential aspect of cloud-based digital content platforms that enable students to access learning resources anytime and from anywhere. It’s an innovative solution if you can’t physically attend. For instance, foreign learners can still benefit from American STEM classes via digital interaction.

What’s more, it’s easy to translate AI-powered content into several different languages to optimize it for all learners. These benefits facilitate an interactive community that includes non-English speakers of various grades and ages.

• Around-the-clock assistance

In the past, you had to be in class to receive assistance from teachers. If students didn’t understand concepts, they had to look for their lecturers for one-on-one follow-ups. Today, several AI-powered chatbots specifically serve the education sector. They provide 24/7 aid to students on various subjects.

• Facial recognition

Some academic institutions are piloting AI-powered facial scans to replace traditional student IDs. This solution improves security, research, and administrative functions. For instance, these scans make it more convenient for students to order food in the canteen and take books from the library.

Most schools also use them to identify troublesome students and prevent crimes. American institutions are bound to benefit from this technology, given the rising number of school gunfire incidents. AI-powered surveillance and machine learning solutions help keep your school safe.

• Secure virtual exams

The popularity of remote learning solutions means educational institutions have to invest in secure exam portals as well. Teachers can design pre-set question banks that AI tools can use to create queries and exercises. They can also grade tests once they’re complete.

The most apparent benefit of these tests is that they’re more secure than regular exams. They have advanced configurations that you can use to assign tests to designated students. The system prevents cheating by limiting availability and allocating questions randomly. In addition to eliminating the risk of human error, they provide educators extra time to focus on their core teaching activities.

Are there Any Downsides to Utilizing AI in Education?

Although AI plays an essential role in education, it’s not entirely perfect. This technology has some cons that will hopefully disappear with improved research and investment. One of them is technology addiction. Part of the reason social media is addictive is that companies use AI and machine learning to recommend enjoyable content.

The result is users spending unhealthy amounts of time on the platforms.

There’s a possibility of adverse consequences in the education sector too. Research shows higher stress levels in children who spend too much time with tech gadgets. In such situations, their brains release cortisol, a hormone that can harm memory nerve cells.

Another disadvantage of AI is that it doesn’t always produce quality content. Since it relies on algorithms, it depends on predefined data. Such software excels at repetitive, not unique, material. Machine learning seeks to solve this problem by not only analyzing data but also improving it. Overall, human teachers are still better at producing original, complex, and innovative solutions.

The threat of AI to the job market is a global concern that’s also real in the education industry. A combination of AI and other emerging technologies such as machine learning, robotics, cloud computing, and computer vision may create functional robotic teachers. They’ll always be available, work longer hours without tiring, and become more accurate as they learn. This scenario casts a dark cloud over the job market in the education field.

Because AI tools utilize algorithms, it increases the likelihood of ransomware and other malware attacks on institutions. They also use a lot of power, which increases your utility and administrative costs. Finally, high implementation costs make AI tools unsuitable for all users. Not all students can afford the computers and other devices necessary to use such systems.

Examples of AI-Based Education Apps

Duolingo consistently ranks as one of the most popular language training apps for iOS and Android. This AI-powered platform offers over 30 languages to more than 300 million users. Others are:

• Brainly : This app helps millions of students do their homework by providing fast and accurate answers. It encourages collaboration between students, teachers, PhDs, and other stakeholders.
• MATHIaU : Teachers can create personalized, step-by-step tutorials for students. It plugs knowledge gaps in real-time and allows teachers to offer tailored solutions to struggling students.
• Thinkster Math : This app improves your math skills by first identifying your strengths and weaknesses. It uses AI to visualize your thought process and logic while tackling a problem. Students achieve better results due to immediate, customized feedback and teaching methods.
• Oddizzi : This app teaches Geography using enjoyable methods. Its AI-powered algorithm allows you to become familiar with various places across the world. You can also take customized quizzes and track your progress.

Final Thoughts

AI has a significant impact on education. It automates various crucial tasks, including administrative functions and test evaluation. It also improves physical and cyber security with its biometric solutions. While it has some disadvantages, you can pick and implement beneficial aspects to your learning institution or experience. The main objective should be to ensure it helps students, teachers, and other stakeholders in the education industry.

This article is a guest author contribution from The Coder School .

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The Advantages Of Automated Systems In Education

From tracking attendance to updating grades, the role of automation in education, with some planning and thought, has significant potential.

Exploring the Advantages of Automated Systems in Education

Teachers are busy.

An enterprise intake orchestration coupled with custom-developed platforms offers a streamlined approach to improving procurement processes with Tonkean mundane tasks, directly contributing to a more productive educational environment for teachers and students alike. By focusing on refining processes like procurement, introducing these systems is not a mere convenience boost; it fundamentally transforms the educational landscape.

The Pressing Need for Automation in Modern Education

Recent shifts towards hybrid and online education models have made educators’ administrative burdens increasingly apparent. The range of duties, from curating content to facilitating digital platforms for better student interaction, underlines a significant challenge. Furthermore, the necessity for individualized learning paths for a broad student demographic exacerbates the need. With workflow automation taking over routine tasks, educators can dedicate themselves to their core mission: teaching and nurturing minds.

Moreover, the shift towards competency-based learning models necessitates a robust system for tracking individual student progress. Automating the assessment process reduces the administrative load on educators and provides real-time insights into each learner’s strengths and areas for improvement. This data-driven approach enables timely interventions and personalized learning plans, ensuring no student falls through the cracks.

Automation in Action: Transforming Educational Administration

Take, for example, a teacher bogged down by the weekly grind of managing attendance and grades. An automation platform can address this, updating records as students participate in online activities or hand in assignments. Similarly, consider a school administrator overwhelmed with enrolment documentation, now able to automate the entire process, from collecting student details to managing class assignments seamlessly. These scenarios illustrate tangible improvements workflow automation brings to educators worldwide.

In addition to attendance and grading, automation can revolutionize curriculum planning and resource management. Imagine a system that analyzes student performance data, identifies trends and suggests appropriate educational resources or adjustments to the teaching approach. This level of insight empowers educators to make data-informed decisions, optimizing the learning experience for each student.

The Broad Impact: Automation as a Catalyst for Enhanced Learning

Beyond simplifying administrative tasks, automation lays the foundation for interactive and individualized learning experiences. It transforms traditional class setups by efficiently distributing pre-class materials, ensuring preparedness and facilitating enriched discussions. Additionally, it empowers educators with tools for tailored professional development, allowing them to track their growth and uncover new learning opportunities.

Automation also paves the way for more engaging and interactive learning experiences. By leveraging technologies like artificial intelligence and machine learning, educational platforms can adapt to individual learning styles, offering personalized content and feedback. This tailored approach keeps students motivated and fosters a deeper understanding of the subject matter.

Overcoming Technological Hurdles

The perceived complexity and the learning curve are significant obstacles to embracing new educational technologies. However, platforms designed for user ease, like Tonkean, mitigate these fears, allowing educators of all technical backgrounds to adopt automation effortlessly. This focus on ease of use is vital, as it emphasizes educational enhancement rather than the intricacies of technology.

The future of automation in education holds boundless potential. From streamlining communications among educators, students and parents, to employing predictive analytics for customizing teaching methods per student needs, the scope is vast. Embracing these advancements means viewing them not as substitutes for the irreplaceable human element in education but as amplifiers of educational impact, enriching the student learning journey.

Furthermore, the collaborative nature of platforms like Tonkean encourages knowledge sharing among educators. By automating the collection and dissemination of best practices, these systems facilitate a culture of continuous improvement. Educators can learn from each other’s successes, adapt proven strategies, and collectively elevate the quality of education delivered.

TeachThought is an organization dedicated to innovation in education through the growth of outstanding teachers.

How artificial intelligence will impact K–12 teachers

The teaching profession is under siege. Working hours for teachers are increasing as student needs become more complex and administrative and paperwork burdens increase. According to a recent McKinsey survey, conducted in a research partnership with Microsoft, teachers are working an average of 50 hours a week 1 McKinsey Global Teacher and Student Survey. Average of Canada, Singapore, United Kingdom, and United States in 2017. —a number that the Organisation for Economic Co-operation and Development Teaching and Learning International Survey suggests has increased by 3 percent over the past five years. 2 TALIS 2018 Results: Teachers and School Leaders as Lifelong Learners , volume 1, Paris, France: OECD Publishing, 2019. Comparison of 2013 and 2018 total working hours for teachers in the United States.

While most teachers report enjoying their work, they do not report enjoying the late nights marking papers, preparing lesson plans, or filling out endless paperwork. Burnout and high attrition rates are testaments to the very real pressures on teachers. In the neediest schools in the United States, for example, teacher turnover tops 16 percent per annum. 3 Desiree Carver-Thomas and Linda Darling-Hammond, “Teacher turnover: Why it matters and what we can do about it,” Learning Policy Institute, August 16, 2017, learningpolicyinstitute.org. Note that the turnover in Title I schools is 16 percent, which is 50 percent greater than that in non–Title I schools. It is even higher for teachers with three or fewer years of experience, at 28 percent. In the United Kingdom, the situation is even worse, with 81 percent of teachers considering leaving teaching altogether because of their workloads. 4 Teachers and workload , National Education Union, March 2018. Further disheartening to teachers is the news that some education professors have even gone so far as to suggest that teachers can be replaced by robots, computers, and artificial intelligence (AI) . 5 “Is education obsolete? Sugata Mitra at the MIT Media Lab,” MIT Center for Civic Media, May 16, 2012, civic.mit.edu; John von Radowitz, “Intelligent machines will replace teachers within 10 years, leading public school headteacher predicts,” Independent , September 11, 2017, independent.co.uk.

Our research offers a glimmer of hope in an otherwise bleak landscape. The McKinsey Global Institute’s 2018 report on the future of work  suggests that, despite the dire predictions, teachers are not going away any time soon. In fact, we estimate the school teachers will grow by 5 to 24 percent in the United States between 2016 and 2030. For countries such as China and India, the estimated growth will be more than 100 percent. 6 Parul Batra, Jacques Bughin, Michael Chui, Ryan Ko, Susan Lund, James Manyika, Saurabh Sanghvi, and Jonathan Woetzel, Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages , McKinsey Global Institute, November 2017. Moreover, our research suggests that, rather than replacing teachers, existing and emerging technologies will help them do their jobs better and more efficiently.

Our current research suggests that 20 to 40 percent of current teacher hours are spent on activities that could be automated using existing technology. That translates into approximately 13 hours per week that teachers could redirect toward activities that lead to higher student outcomes and higher teacher satisfaction. In short, our research suggests that existing technology can help teachers reallocate 20 to 40 percent of their time to activities that support student learning.

Further advances in technology could push this number higher and result in changes to classroom structure and learning modalities, but are unlikely to displace teachers in the foreseeable future. Many of the attributes that make good teachers great are the very things that AI or other technology fails to emulate: inspiring students, building positive school and class climates, resolving conflicts, creating connection and belonging, seeing the world from the perspective of individual students, and mentoring and coaching students. These things represent the heart of a teacher’s work and cannot—and should not—be automated.

Make no mistake, the value of a good education starts early and lasts a lifetime. Research suggests that simply having an effective kindergarten teacher can affect the likelihood of a student completing college thus boosting their lifetime earnings by about $320,000. 7 Raj Chetty et al., “$320,000 kindergarten teachers,” Phi Delta Kappan , November 2010, Volume 92, Number 3, pp. 22–5, journals.sagepub.com. Technology, when used correctly, can facilitate good teaching, but it will never replace teachers. In the remainder of this article, we will outline how teachers spend their time today, how technology can help to save teacher time, and where that additional time might go. Note that we are intentionally focused on the impact of technology on teacher time. In future articles we will address its broader impact on student learning.

How teachers spend their time

To understand how teachers are spending their time today and how that might change in a more automated world, we surveyed more than 2,000 teachers in four countries with high adoption rates for education technology: Canada, Singapore, the United Kingdom, and the United States. 8 Teachers surveyed by country: 501 in Canada, 134 in Singapore, 509 in the United Kingdom, and 1,028 in the United States. We asked teachers how much time they spend on 37 core activities, from lesson planning to teaching to grading to maintaining student records.

We asked where teachers would like to spend more and less time. We asked what technologies teachers and students were currently using in the classroom to discover new content, practice skills, and provide feedback. Finally, we asked what was working well and where they faced challenges, both in the application of technology and more broadly across their role as teacher. Our findings were unequivocal: teachers, across the board, were spending less time in direct instruction and engagement than in preparation, evaluation, and administrative duties (Exhibit 1).

How technology can aid teachers

Once we understood how teachers spend their time, we evaluated automation potential across each activity, based on an evaluation of existing technology and expert interviews. We concluded that the areas with the biggest potential for automation are preparation, administration, evaluation, and feedback. Conversely, actual instruction, engagement, coaching, and advising are more immune to automation (Exhibit 2).

Where to save time with technology

The area with the biggest automation potential is one that teachers deal with before they even get to the classroom: preparation. Across the four countries we studied, teachers spend an average of 11 hours a week in preparation activities. We estimate that effective use of technology could cut the time to just six hours. Even if teachers spend the same amount of time preparing, technology could make that time more effective, helping them come up with even better lesson plans and approaches. For example, several software providers offer mathematics packages to help teachers assess the current level of their students’ understanding, group students according to learning needs, and suggest lesson plans, materials, and problem sets for each group. In other subjects, collaboration platforms enable teachers to search and find relevant materials posted by other teachers or administrators.

Technology has the least potential to save teacher time in areas where teachers are directly engaging with students: direct instruction and engagement, coaching and advisement, and behavioral-, social-, and emotional-skill development. It is worth pausing here for a moment to note that we are not denying that technology will change the student experience of learning, although we would recommend caution and measured expectations.

While controlled pilot studies have shown improvements in student learning from technology-rich, personalized blended learning , 9 John F. Pane et al., “How does personalized learning affect student achievement?,” RAND, 2017, rand.org. these improvements have not yet been realized on a large scale. The most recent Program for International Student Assessment scores suggest that, globally, students who use tablets, laptops, and e-readers in the classroom are performing worse than those who do not . Why the disconnect?

Our hypothesis is that implementing technology in the classroom at scale is hard. Just providing hardware is easy. Integrating effective software that links to student-learning goals within the curriculum—and training teachers on how to adapt to it—is difficult. This underscores why we believe that technology in the classroom is not going to save much direct instructional time. To improve student outcomes, the teacher still needs to be in the classroom, but their role will shift from instructor to facilitator and coach. For example, some teachers are using flipped learning in their classrooms. Instead of teaching a concept in the classroom and then having students go home to practice it, they assign self-paced videos as homework to give the basic instruction and then have students practice in the classroom, where the teacher can provide support and fill gaps in understanding.

How to improve student educational outcomes

Evaluation and feedback complete the teaching loop. As teachers understand what their students know and can do, they can then prepare for the next lesson. Technology has already helped here—for example, computer grading of multiple-choice questions was possible long before AI and is particularly penetrated in math instruction. More is possible. Advances in natural-language processing make it possible for computers to assess and give detailed, formative feedback across long-form answers in all subject areas. For example, writing software can look at trends in writing across multiple essays to provide targeted student feedback that teachers can review and tailor. Combined, these technologies could save three of the current six hours a week that teachers spend on evaluation and feedback.

Finally, administration is a bugbear of teachers globally. After all, who prefers filling out paperwork to interacting with children? Good news is on the horizon. Automation could reduce the amount of time teachers spend on administrative responsibilities—down from five to just three hours per week. Software can automatically fill out forms (or provide menus of potential responses); maintain inventories of materials, equipment, and products; and even automatically order replacements.

Where the time will go

What will teachers do with the additional 13 hours a week saved by the application of technology? Some of this time, hopefully, will be given back to teachers themselves—to spend time with their families and their communities—thus increasing the attractiveness of teaching as a profession.

Much of the time saved, however, can be plowed back into improving education through more personalized learning and more direct coaching and mentoring. In our survey, about a third of teachers said that they wanted to personalize learning but did not feel that they were doing so effectively at present. Their biggest barriers: time, resources, materials, and technology (Exhibit 3). Automation can help with all of these. Even when teachers believed that they were already providing tailored materials—and personalized feedback—to students, students often disagreed. While 60 percent of the teachers surveyed believed that their feedback was personalized to each student, only 44 percent of the students surveyed felt the same way.

Additional time can also help support social–emotional learning and the development of the 21st-century skills  that will be necessary to thrive in an increasingly automated workplace. It will enable teachers to foster one-on-one relationships with students, encourage self-regulation and perseverance, and help students collaborate with each other. Research shows that strong relationships with teachers promote student learning and well-being, especially for students from low-income families. 10 Desiree W. Murray et al., Self-regulation and toxic stress: Foundations for understanding self-regulation from an applied developmental perspective , Office of Planning, Research & Evaluation, February 13, 2015, acf.hhs.gov. Automation within the teaching profession could thus be a catalyst in reducing educational inequalities.

Finally, teachers could spend more time collaborating with each other. More time for collaboration should translate into better outcomes for students. International comparative studies show that high-performing school systems double down on peer coaching and collaborative lesson planning. 11 Andreas Schleicher, “Teaching Excellence through Professional Learning and Policy Reform: Lessons from around the World,” Paris, France: OECD Publishing, 2016. These practices can support teachers in improving and developing their craft. 11 Andreas Schleicher, “Teaching Excellence through Professional Learning and Policy Reform: Lessons from around the World,” Paris, France: OECD Publishing, 2016. For example, the leerkRACHT Foundation  in the Netherlands has introduced peer collaboration into 10 percent of Dutch schools, with 80 percent of teachers reporting improvement in student learning.

How to make it happen

All of this begs a question: How will we capture the promise of technology in our schools? The good news is that this is not about technology we have not yet invented. It will not require AI systems that pass the Turing test. To the contrary, achieving these savings in teacher time is mostly about adoption of existing education technology. Just bringing the average school to the level of the best would have a huge impact.

This, however, is no small task. It will require commitment across a broad range of stakeholders, including governments, school leaders, technology companies, and, of course, teachers and learners themselves. Four imperatives stand out as schools move to adopt technology wisely: target investment, start with easy solutions, share what is working, and build teacher and school-leader capacity to harness technology effectively.

The schools that are currently best in applying technology to save teacher time have often been able to access more funding than the average school. Democratizing these gains will entail increased investment in every school, especially those that are currently under-resourced. As investment increases, it will be critical to target it to the areas that can most effectively save teacher time and improve student outcomes (rather than to flashy but ineffective hardware).

Starting with easy solutions will provide early momentum. Proven technology that can replace simple administrative tasks or simple evaluative tools for formative testing can immediately provide teachers with respite, whetting their appetite for more holistic solutions.

Part of the problems that schools face today is the myriad of competing solutions, some of which are fantastic, but many of which promise great things but deliver little. Sharing what is working—and what is not working—is therefore critical. Neutral arbiters bringing objective and rigorous performance data, similar to the service that EdReports.org provides on curriculum, are necessary in the education-technology space. It will also be necessary to make best-practice solutions available to teachers at all types of schools and school systems.

Finally, building the capacity of teachers and school leaders to harness technology effectively will ensure maximum gains in not only saving teacher time but also improving student outcomes. Districts and schools need to balance introducing new technologies with fully integrating existing ones into the curriculum and teachers’ professional development. Districts need to use accepted, widely adopted tools for consistency. However, teachers should have the freedom to pilot alternatives, and they should have a strong voice in deciding which tools are working in the classroom and should roll out districtwide. Technology companies, too, need to be better in including the voice of the teacher when guiding product development.

If these four imperatives are met, then we are hopeful that automation will be a boon and not a bane for teachers. Ten years from now, with the support of a range of education technologies, teachers should have more time for themselves—and more time for their students. They can pour that time into improving student outcomes and preparing students for a more challenging and automated workforce.

Jake Bryant is an associate partner in McKinsey’s Washington, DC, office, Christine Heitz is a consultant in the Denver office, Saurabh Sanghvi is an associate partner in the Silicon Valley office, and Dilip Wagle is a senior partner in the Seattle office.

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• Technology in the Classroom

How automation in education is changing classrooms

Automation in education is transforming classrooms across the country. The more educational institutions implement automation software, the more streamlined and effective the learning process becomes.

For example, automation saves time and vast quantities of paper. There’s no longer any need for teachers to print out dozens of forms or tests for each student or for students to print out their homework. 

There’s also a lot less administrative work for teachers to worry about. Ultimately, that lets teachers spend more time with their pupils, writes Alyssa Johnson at eLearning Industry. Teachers still spend a lot of time on administrative tasks like marking exams and assessing homework, but technology can take care of a lot of these tasks for them.

Spending more with students improves the quality of education as well. Best of all, automating different aspects of the classroom has never been easier. Here are five common tasks that teachers can start automating today.

Speed up your school’s workflow with ready-made education form templates .

Attendance and registration

An online spreadsheet is a much more effective and efficient way of taking attendance than paper-based registers. Not only does it save paper, it also makes it easy for teachers to generate attendance reports at the end of the semester without having to sift through dozens of pages.

And you can track more than just student attendance with spreadsheets. They’re also a great tool for automating book logs so teachers can see at a glance which students have checked out which books and how long they’ve had them. 

For an insightful look into the future of higher education, explore “ 8 Top Trends in Higher Education to Watch in 2024 ” on Jotform’s blog.

Quizzes and tests

More and more students are using laptops and tablets in class, so it makes sense to start doing away with paper quizzes and start creating electronic versions instead. Running quizzes online means teachers don’t have to print them out or grade them by hand.

Instead, they can use online forms or other software tools to generate quizzes that grade themselves automatically after students submit them. Teachers can add explanations to the correct answers if necessary or even provide links to additional materials. 

Automating quizzes and feedback doesn’t just save time, says former teacher Matt Miller , author of Ditch That Textbook . It also gives students feedback when they need it most. The more time there is between testing and feedback, the more disconnected the students’ learning experience becomes. 

Student information

Teachers collect a lot of important information from students at the start of the year. This can result in dozens or even hundreds of different paper forms that teachers have to collate and store. That isn’t just time-consuming — it also increases the chances of one or more of those forms getting lost.

Instead of having students fill in paper forms by hand, collect information electronically and store everything in a spreadsheet or database.

Communication with parents

Endless email chains and one-on-one conversations with parents can eat up a lot of a teacher’s time. There’s no reason why teachers can’t automate at least some of these conversations, giving them more time to focus on their students. 

One way to do this is to use an online form to collect information from parents or to request a classroom volunteer. Rather than emailing every parent individually and having to make note of their answers, a teacher can use an online form that feeds every response into an online database, making it easy to find the information they need.

Teachers can create a shareable online spreadsheet to keep parents abreast of progress, key dates, and any other information they want to share with parents, says Kelly Bielefeld , executive director of college and career readiness at Wichita Public Schools.

Assignment tracking and submission

Teachers can use online forms and databases to monitor and collect assignments from students. Rather than having them print out work or even email it to the teacher, students can use online forms to submit tasks in a standardized way.

When used in conjunction with an online database, all assignments feed into a single space, which allows teachers to see at a glance who has submitted work and who hasn’t.

Teacher Jessica Parker says she does this with her students. She used to have students email her assignments, but would then waste time trying to find them. Having students submit everything in one place is much more efficient and saves a lot of time.

Automate your classroom with Jotform

Jotform’s online forms and templates make it easy for teachers to start automating their workflow. There are dozens of education-related templates that let teachers track everything from attendance to student information to reading logs. 

Our suite of education forms also helps you get rid of paperwork and collect data in a standardized way. Use these forms to gather student feedback and other information, evaluate courses, and generate pop quizzes. 

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Hyperautomation in Education: Use Cases, Benefits, and Solutions

Parvathy s. nair.

The successful incorporation of technology into education has shown the world just how powerful a tool it can be for transforming learning; tech has helped affirm and advance relationships between educators and students and reinvented conventional approaches to learning and collaboration.

In this era of advanced technology, automation has already revolutionized various industries, including healthcare , finance , and manufacturing. The education sector is no exception to this trend. 

Challenges that the Education Sector Faces Today

While hyperautomation and its associated technologies have many potential applications within higher education to improve modernization strategies, specialists believe that educational institutions that adopt hyperautomation may experience immediate benefits in addressing everyday challenges such as:

• Financial constraints within administrative budgets
• Attracting and retaining talent
• Isolated departments across different campuses
• Dealing with an aging workforce and managing issues related to workflow acceptance
• Supporting existing and new technology systems that may have limited cross-platform communication
• Ensuring data protection and cybersecurity for both staff and students.

Related article: A Practical Guide to Implementing Hyperautomation Into Your Enterprise

How can Hyperautomation Technology Benefit the Education Sector Specifically?

Hyperautomation, as highlighted in our comprehensive guide , is essentially technology that can automate many processes, including administrative tasks, curriculum planning, and learning assessments. The technology combines artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to create a smarter, more efficient, and more productive system. 

The pain points that the education sector faces, as highlighted above, can be addressed with hyper-automated integrations. For example, educational institutions need to manage large amounts of data on student enrollment, academic performance, and teacher evaluations. 

A simple use case of hyperautomation here can automate these processes and reduce the burden on administrative staff, enabling them to focus on more valuable and pressing tasks. In addition, hyperautomation can improve the quality of education by providing personalized learning experiences for each student! 

By using AI and ML algorithms, hyperautomation can identify the learning style of each student and create a customized learning experience that meets their individual needs. Furthermore, hyperautomation can help to reduce costs by automating repetitive tasks, such as grading assignments, scheduling classes, and sending reminders to students.

Popular Use Cases of Hyperautomation in Education

Administrative tasks.

One of the most promising use cases is the automation of administrative tasks. For example, hyperautomation can be used to manage student enrollment, create schedules, and manage the budget. Another use case is the automation of curriculum planning. 

Identifying Effective Teaching Methods 

Hyperautomation can help to identify the most effective teaching methods and create a customized curriculum for each student. 

Hyperautomation can be used to automate assessments, such as grading assignments and exams. By automating these processes, teachers can spend more time providing personalized feedback to students.

Can Hyperautomation Benefit the Education Sector?

Yes! By adopting use cases like the above, here’s how hyperautomation can be of benefit to the education sector– 

Increased efficiency

One of the primary benefits is increased efficiency. By automating administrative tasks, teachers and administrators can focus on providing quality education to students and can provide personalized learning experiences for each student. 

Reduced Costs 

This handy piece of technology can help to reduce costs by automating repetitive tasks, such as grading assignments and scheduling classes. By reducing the workload of administrative staff, educational institutions can save money and improve productivity.

Increased accuracy

Another benefit of hyperautomation is increased accuracy. By using AI and ML algorithms, hyperautomation can identify the learning style of each student and create a customized learning experience that meets their individual needs. This, in turn, provides enough data for the most effective teaching methods to be identified. Hyperautomation integrations can create a customized curriculum for each student, improving their academic performance!

Hyperautomation technology can truly revolutionize the education sector! The use cases of hyperautomation in education are numerous, and the benefits of hyperautomation are significant. As educational institutions face challenges in managing large amounts of data, providing quality education, and reducing costs, hyperautomation technology will become an increasingly important tool to help address these challenges.

Hyperautomation, Education and Autonom8

Integrating Autonom8 into your workflow can give your faculty and staff the firepower they need in this back-to-school season. Here are a few of the many use cases you could adopt with the A8 suite for your institution –

• Student Registration and Applications
• Assessments and grading
• Course Information and Lesson Plans
• Student feedback
• Student engagement 
• Remote learning
• Admission support
• Maintenance requests
• Leave or vacation requests

Elevate the campus experience and look at the service offerings on the table in Autonom8’s Hyperautomation platform for education video here . Sign up for Autonom8’s hyperautomation platform for education today to see how we can make your educational institution soar.

Hyperautomation in Education FAQs

What is hyperautomation used for.

Hyperautomation is a technology that combines artificial intelligence, machine learning, and robotic process automation to automate a wide range of processes, including administrative tasks, curriculum planning, and learning assessments.

How can hyperautomation technology benefit the education sector?

Hyperautomation can benefit the education sector by automating administrative tasks, providing personalized learning experiences for each student, reducing costs, and improving the quality of education.

What are some use cases of hyperautomation in education?

Some use cases of hyperautomation in education include automating administrative tasks, curriculum planning, and assessments such as grading assignments and exams.

How can hyperautomation technology provide personalized learning experiences for each student?

Hyperautomation technology can identify the learning style of each student using AI and ML algorithms and create a customized learning experience that meets their individual needs.

How can hyperautomation technology reduce costs in the education sector?

Hyperautomation technology can reduce costs by automating repetitive tasks, such as grading assignments and scheduling classes, and reducing the workload of administrative staff.

What are some benefits of hyperautomation in the education sector?

Some benefits of hyperautomation in the education sector include increased efficiency, improved accuracy, personalized learning experiences, and cost savings.

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Automation in education: What aspiring teachers should know

Automation is set to drastically alter the workplace, but just how much will technology such as artificial intelligence (AI) affect teachers?

Such information is helpful for those aspiring to enrol in teaching courses at universities – knowing how, when, what, where and who will be impacted by automation’s impact on the profession lets students make the right preparations for their future careers.

According to a study by Silver Swan Recruitment, the teaching profession has “an incredibly low chance of automation” in the future at just one percent, one of the lowest on their list.  

Research by McKinsey Global Institute’s 2018 report on the future of work echoed this. 

They estimate that the number of school teachers will grow by five to 24 percent in the US between 2016 and 2030; for countries such as China and India, the estimated growth will be more than 100 percent. 

While these reports suggest that teachers have some semblance of job security and that they won’t be replaced by robots anytime soon, there are still aspects of their work that will be affected by technology. 

McKinsey’s research also suggests that, rather than replacing teachers, existing and emerging technologies will help them do their jobs better and more efficiently.

Last year, Stephen M Kosslyn, Foundry College president and CEO aptly noted in the Harvard Business Review : 

“While much has been written about the sorts of jobs that are likely to be eliminated, another perspective that has not been examined in as much detail is to ask not which jobs will be eliminated but rather which aspects of surviving jobs will be replaced by machines.”

Capturing the potential of technology in K12 teaching

When applied effectively, technology can help K12 teachers better prepare for class. Source: Shutterstock

So, what are the top things teacher trainees know about the impact of technology in future classrooms?

Some of the broader impacts highlighted by McKinsey in How artificial intelligence will impact K-12 teachers  include:

Technology can automate administrative tasks

McKinsey concluded that the areas with the biggest potential for automation in teaching are “preparation, administration, evaluation and feedback,” actual instruction, engagement, coaching and advising are more resilient to automation. 

Automation could reduce the amount of time teachers spend on administrative responsibilities, from five to three hours per week. Software can also automatically fill out forms or provide menus of potential responses; maintain inventories of materials, equipment, and products; and even automatically order replacements.

Thus, by automating administrative tasks, technology can help teachers allocate more time towards activities that support student learning.

Save time on class preparation

Across the four countries McKinsey studied, they found that teachers spend an average of 11 hours a week in preparation activities.

They estimate that effective use of technology could cut the time to just six hours. They note that even if teachers spend the same amount of time preparing, technology could make that time more effective, helping them come up with better lesson plans and approaches.

Enhance personalised learning

The effective application of technology can potentially save teachers some 13 hours a week, which could be plowed back to the teachers themselves (ie. for them to spend more time with their families, etc) or into engaging in more personalised learning, direct coaching and mentoring.

“In our survey, about a third of teachers said that they wanted to personalise learning but did not feel that they were doing so effectively at present. Their biggest barriers: time, resources, materials, and technology. Automation can help with all of these,” said the report.

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The implications of automation for education

Harry a. patrinos, raja bentaouet kattan, kevin macdonald.

Automation is heralding a renewed race between education and technology. However, the ability of workers to compete with automation is handicapped by the poor performance of education systems in most developing countries. This will prevent many from benefiting from the high returns to schooling. Schooling quality is low   The quality of schooling is not keeping pace, essentially serving a break on the potential of “ human capital ” (the skills, knowledge, and innovation that people accumulate).  As countries continue to struggle to equip students with basic cognitive skills-  the core skills the brain uses to think, read, learn, remember, and reason- new demands are being placed.

In fact, the skills demanded by the labor market are evolving.  Employers are demanding more flexible workers able to function at a high level.  They are also demanding who can perform non-routine cognitive tasks. There is also a growing demand for social skills . There is a steady growth of jobs requiring high levels of social interaction in the labor force.   The limits of the East Asia growth model   In East Asia, we have reached the limits of the industrial model. What once worked for developed East Asia may not work for developing Asia. In fact, the technological revolution – or Fourth Industrial Revolution ( #4IR ) – and automation implies deskilling for many workers and a need for new skills for many.   Two essential parts of the East Asian growth story are trade reform and human capital. Policies that either incentivized exports or liberalized trade effectively activated a comparative advantage in low-cost, trainable labor. Korea, for example, had a GDP per capita in 1962 that was commensurate with many sub-Saharan African countries. But after a decade of labor intensive, export driven growth, real GDP per capita in Korea doubled.   The decline in the cost of automation technology and the breadth of tasks that are now able to be automated are expected to significantly disrupt labor markets in the coming decades. This poses significant challenges to developing countries.    Middle income countries tend to have the highest proportions of workers in automation-prone occupations (such as bookkeepers, mail carriers, tellers, among others). For low income countries, having low-cost, low-skill labor may no longer be a comparative advantage that they can exploit to achieve rapid economic growth as East Asia’s middle income countries did in the past.    The race between education and technology   Nobel prize winning economist Jan Tinbergen in 1974 pointed to the skill-biasedness of technological progress with its consequences for income inequality. He also highlighted the pivotal role of education in mediating this relation.   During the 20th century, human capital acquisition boosted incomes and lowered inequality. However, the reverse has been true since about 1980. The educational slowdown that ensued was accompanied by rising inequality . Does this mean that education was losing its mediating effect?   This has profound implications for education systems, which find themselves in a constant struggle, indeed a race – a term coined by Tinbergen – to keep pace with the demand for skills and now an ever-growing change in the types of skills demanded.  

• Focus on basic skills, early development, and measure and improve early reading;
• Give opportunities to workers to invest in relevant skills for the labor market that make them benefit from, and remain immune to, automation; and
• Use evidence from labor market returns to education to implement financial innovations and use future earnings to finance higher education
• East Asia and Pacific

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• Task Automation 101: The What and How of Automating Work 

How often do you run out of time trying to complete all the tasks on your to-do list? It’s common to hear phrases around the office like “time is money” and “work smarter, not harder,” but embracing those aphorisms can be difficult when you’re burnt out and drowning in work.

This is why task automation is essential. Instead of repeating the same mundane things over and over, turning your brain to mush, it’s time to take back control of your precious work hours and let automatic tools do the boring tasks for you.

In this article we’ll cover the basics of task automation: what it is, what you should (and shouldn’t) automate, how to choose the right task automation software, and provide a simple step-by-step guide on how to set yourself, and your team, up for success.

What is task automation? 

If you’re not utilizing any task automation tools yet, this might all seem a bit overwhelming. That’s ok – we promise it isn’t. In fact, you’re probably already automating things in your personal life without realizing it.

That Netflix subscription that auto-renews every month? That’s an automated task. Any subscription that’s set up with your payment information and automatically renews would serve as an example here.

It’s not a trick question; task automation is exactly what it sounds like: using software or tools to automatically complete tasks for you.

Some instances are more detailed and complicated than others, but they all have the same end goal: to save you time and resources, and ultimately improve the efficiency and effectiveness of your workflows. 

How to determine which tasks you should automate

By now you might be thinking, “what can the robot overlords do for me?” There’s a lot that task automation software and AI tools can do to free up your time, but it’s important to pick and choose wisely. Be strategic in what you automate, and you’ll thank yourself later.

The types of tasks you choose to set up for automation will vary depending on the specific work you do. Let’s look at some common tasks and break them down by internal departments. 

Task automation examples for marketing teams

• Schedule social media posts across multiple platforms.
• Schedule email marketing campaigns.
• Easily pull metrics and run reports on website traffic.

Task automation examples for sales teams

• Send follow-up emails to leads.
• Schedule sales meetings with clients.
• Pull reports from CRMs .

Task automation examples for project managers

• Set reminders for yourself or team members.
• Run reports (daily, weekly, or quarterly) as needed.
• Create rules for tickets in project management tools like Asana or Trello , and send updates on changes made to tickets.

Task automation examples for developers 

• Review and test new or existing code.
• Monitor data.
• Organize and assign support tickets. 

Task automation examples for HR

• Process payroll and employee benefits.
• Approve or deny time off requests.
• Schedule recurring meetings with employees.

There are tons of options when it comes to what you can automate, but you need to identify what you should automate based on your immediate needs and overall goals and objectives. 

6 questions to ask to determine which tasks to automate

The main objective of task automation is to stop doing repetitive tasks to free up more of your time, so start by identifying those. When determining what to automate, ask yourself the following questions: 

• What are my most common recurring tasks? These can be tasks you complete daily, weekly, or monthly. Anything that follows a set schedule can likely be automated. Tasks that happen sporadically won’t fit nicely into automation tools.
• How much time would I save if I didn’t have to complete this task? If you’re going to save hours every week, it’s worth the investment to automate your task.
• Does this task require critical thinking? If it’s a simple action, it can likely be automated. However, if it requires more human-specific brainpower, you should keep it on your personal to-do list.
• If this task is automated, can it be integrated with other tools we use? If you’re tracking tasks on Kanban boards or need to update customer support tickets, check if your existing software can integrate with whatever task automation tools you’re considering for use.
• Does this task require a single person to be completed, or an entire team? If your task requires teamwork or a lot of brainstorm power, it shouldn’t be automated. Remember, task automation software can’t think critically or brainstorm, so don’t ask it to. If the task is something an individual person would typically be responsible for, it can probably be automated.
• Does this task require emotional intelligence? Again, if your task requires thinking like a human, it shouldn’t be automated. For example, responding to customer support tickets or dealing with peer reviews should be completed by an actual human. Software tools don’t emote like we do, so they can’t complete tasks that require any sort of emotion.

Asking these qualifying questions will help you determine what tasks should, or shouldn’t, be considered for task automation. To narrow down your scope even more, you can implement different tools and exercises to focus on your priorities.

Using the Eisenhower Matrix

When determining which tasks you should automate, you can implement the Eisenhower Matrix to help prioritize your work. The questions above will help you identify the types of tasks that have the potential to be automated, but you’ll still need to narrow things down to highlight your priorities.

The Eisenhower Matrix is a tool often used for task management. It helps people determine which tasks on their lists are the most urgent so they can focus on priorities and ensure their workflow is smooth and efficient.

When using this tool, you’ll plot tasks into four separate boxes based on urgency and importance. The boxes are labeled as “Do,” “Schedule,” “Delegate,” and “Delete.” It looks like this:

• Do : These items are time-sensitive and important. They should be done immediately and are not candidates for task automation.
• Decide : These tasks might be important for long-term success, but they don’t have set deadlines. They might be candidates for automation, but you can worry about analyzing them later.
• Delegate : These items need to be completed but you don’t have to be the one who does them. These tasks are perfect for automation tools – we highly recommend you move these items over to be automated as soon as possible.
• Delete : These tasks are not urgent or incredibly important. Take some time to figure out if they truly need to be done. If so, they can probably be automated, but truthfully, you can delete them off your to-do list and focus on what really matters.

Types of tasks that shouldn’t be automated

There will always be work that needs to be completed without the help of automation. Ideally, if you’re able to successfully automate all your recurring, mundane tasks, you’ll have more time to focus on items that require your individual attention.

Let’s look at what shouldn’t be automated: 

• Customer support. You need team members to deal with customer-facing tasks and issues. Human interaction is key for building trust, which is needed in these situations. You can automate assigning or closing out customer-related tickets, but dealing with the actual issue must be done by a human.
• Training new team members. Onboarding a new team member requires both critical thinking and, to an extent, emotional investment. Filling out paperwork and watching training videos is one thing, but sitting down and ensuring they understand what they need to complete their job is an investment.
• Cyber security. Keeping your company protected from third-party cyber attacks is crucial. There’s always the possibility for tools or software to become compromised. It’s critical that you have staff running their own checks to ensure everything is alright.
• Deploying new updates to production servers. I can’t imagine pushing a deploy to a production server and just assuming everything is fine; that is nightmare fuel. Parts of this process might be automated, but there should always be post-deploy checks to ensure everything is as expected.
• Quality assurance. QA should rarely be automated. This process requires checking for errors human users might stumble upon, so it requires human thinking. 
• Closing sales deals. There are many tasks that sales teams can automate, but sales calls with leads, and conducting final conversations to close out deals, needs to be done by a team member. If you rely on just sending an email and hoping for the best, you’re going to miss your sales targets.

As a rule of thumb, anything that has an urgent deadline, requires critical thinking, emotional intelligence, or the input from multiple team members should not be automated. 

How to choose the right task automation software

There are countless options for task automation software, so picking the right one for your needs may seem overwhelming. Lucky for you, we’re here to help make this process less stressful.

First, you need to identify what types of tasks you’ll be automating so you can narrow down the appropriate type of tool. Task automation software typically falls into two main categories: 

• Robotic process automation (RPA) : These tools copy human actions that are repetitive, frequent, and have standard outcomes, such as data entry or scheduling recurring meetings.
• Business process automation (BPA) : These tools are used to automate workflows and can be more complex. They often integrate actions across teams and systems. 

Once you know what type of tool you need, you can start researching options. Different automation tools will benefit different companies. There isn’t a specific recommendation that works for everyone.

When looking at your options, consider the following: 

• Price . Is it a one-time fee, or a recurring monthly fee? 
• Number of users . Do you have to pay for additional team members to use the tool?
• Integrations . What other programs or tools will the task automation software work with? Are there any it’s not compatible with? 
• Support . If you run into issues, is it easy to contact customer support, or do you have to debug things yourself?
• Reviews . What are other people saying about the tool? Do they like how it works; has it helped streamlined their business? Or are people leaving negative comments and voicing concerns?
• Feedback from your team . If you’re looking for a solution to help your team automate tasks, collect feedback from them directly. Don’t make a final choice based on assumptions – if you’re not part of the day-to-day work, you won’t know what they truly need.

If you can, sign up for a free trial to test a task automation tool before paying for a subscription. If everything works out, you’ve found your solution! If not, do some more research until you find a good fit.

Step-by-step guide for automating tasks

Now that you know more about task automation, you’re ready to get started! You can follow these four simple steps to set yourself, and your team, up for success. 

• Identify tasks to automate . Remember, you want to automate simple, repetitive tasks that occur frequently to free up time and resources. If something is high-priority or requires teamwork or critical thinking, it shouldn’t be automated.
• Find an appropriate automation tool . Research your options and collect feedback from your team on what would best serve their needs. Ask what gaps need to be filled and find a tool that will effectively assist in getting work done.
• Set things up and optimize your workflows . After you purchase a task automation tool, take the time to set everything up thoroughly. Communicate with your team so everyone knows what’s happening, what the expectations are, and what the tool will take care of versus what’s still expected of everyone else. Sync actions with other tools and software you’re using so people get appropriate updates and can track actions related to their work.
• Adjust as needed . Review and adjust your automations on a regular basis. If your team has new goals or KPIs to work towards, adjust your automation to better reflect your needs. Business changes can happen quickly, so be ready to pivot.

Remember: task automation is an invaluable tool, and one you should always be utilizing to its full extent. You have goals to achieve and shouldn’t be wasting time on the same boring tasks day in and day out. Automation is our friend, and your workflows will be better with it!

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Task Automation: 2024 Utimate Guide With Examples And Use Cases

Introduction

‍ Task automation is the process of automating manual tasks. It is achieved by eliminating the tasks from a workflow entirely or taking over some steps to reduce human labor. Automation has become increasingly popular in recent years as businesses look for ways to streamline processes and cut costs. But what does task automation look like in practice? How can you take advantage of it?

This ultimate guide will explore the basics of task automation, with examples and case studies to help you understand how to use it in your own business. Read on for an in-depth look into the world of automation.

What Is Task Automation?

Task automation is the process of automating tasks that we typically need to perform manually. This includes anything from simple tasks like sending an email to more complex tasks like managing a website. Task automation can save time and improve efficiency by allowing users to focus on other tasks while the automation tool handles the repetitive work.

There are a number of different ways to automate tasks, including using dedicated task management tools, custom scripts or macros, and even integrating with third-party services. The right approach will depend on the user's specific needs and the automated task type.

Task automation is used for a wide variety of tasks, including:

Sending Emails: Automated email tools can handle everything from sending out mass emails to customers to sending individualized messages based on customer interactions.

Managing Website Content: Automation tools can help with everything from publishing new content to updating existing web pages.

Data Entry: Automation can help reduce the amount of time needed for data entry by automatically importing data from sources such as spreadsheets or databases.

Generating Reports: Automated reporting tools can generate reports on a regular basis, making it easy to track progress and identify areas for improvement.

These are just a few examples of how task automation is used currently; the possibilities are nearly endless. With the right tool in place, virtually any type of task can be automated, freeing up users' time for more important work.

The Benefits of Automation of Tasks

Task automation offers plenty of benefits. For starters, automating tasks can save you a lot of time. This is especially true if you have a lot of tasks that need to be completed on a daily basis. With automation, you can set things up so that all of your tasks are completed without you having to lift a finger.

In addition to saving time, automated tasks can also help to improve your accuracy and efficiency. When humans are left to complete tasks manually, there is always the potential for error. However, when automation is used, the task is completed as it should be every time. This can help to improve your bottom line by ensuring that tasks are completed correctly the first time around.

Automating tasks can also help to improve your customer service levels. When customers know that they can rely on automated systems to handle their inquiries quickly and efficiently, they are more likely to be satisfied with your company. Automation can help you create a better overall customer experience, which can only lead to good things for your business down the line.

What Types of Business Tasks Can You Automate?

There are many types of business tasks you can automate. Some are common across all types of businesses, while others may be specific to your industry or product line.

The more important the task you're trying to automate, the more likely it is that you'll have a successful project.

Here are some task automation examples :

Customer service: This is one of the most common places you'll find yourself trying to automate. The key here is to identify the most common customer service scenarios, as well as how long each scenario takes per customer contact. From there, create scripts and workflows that enable agents to perform these scenarios quickly and consistently.

Order Management: Order processing involves a large number of steps that may be time-consuming or error-prone. Excluding errors, they take multiple hours or days per order. You can automate these processes and get orders ready for shipping in no time.

Sales: Sales automation is all about automating repetitive tasks so salespeople can focus on closing deals instead of managing data entry and other tasks that take up their time during a call or meeting with customers. While Salesforce is the most popular tool for automating sales processes, there are many other tools available; just make sure your solution integrates with your existing CRM system.

Task Automation Tools

Following are some of the popular task automation tools:

• IFTTT: IFTTT is a great tool for automating simple tasks. It's also useful for automating tasks that require human interaction, such as receiving a text when your favorite sports team scores a goal or getting an email notification every time someone you follow on Instagram likes one of your posts. IFTTT can be used to create recipes that involve multiple apps, allowing you to accomplish more with less effort.
• Process Street: Process Street is a great choice to start with building your own templates and automating your tasks. The interface is easy to use and very intuitive, allowing you to add images, videos, checklists, and much more. You can also import existing templates from other platforms or create new ones. You can even share your templates with other people if they’re members of the same organization—this is a great way of making sure that all team members know what needs doing and when.

Automating tasks is one of the best ways to improve efficiency and increase productivity. However, many businesses struggle to implement automation because they find it difficult to create a system that works for their specific needs.

Zorp is a platform that allows you to build your own custom automation tool by combining different modules from their library. This means that you can easily create a custom tool that fits your exact business requirements. This makes Zorp an excellent solution for companies who want to automate tasks in their daily work but need help finding a flexible product for their needs.

Task automation tools are a great way to get your business running smoothly, but they can also be used to make your life easier.

Zorp empowers you to create your customized task automation tool without having to go through the hassle of learning how to program or code your own solutions from scratch. Zorp will give you the tools and support necessary to create a custom task automation tool tailored specifically to your needs without needing any prior knowledge or experience.

Frequently Asked Question

How do you automate tasks.

Automation is a way to reduce human error and speed up processes. You can automate tasks by creating scripts that run on a schedule or by using applications to connect different apps.

What is an example of a task that could be automated?

An example of a task that could be automated is the collection of data. Automation can be used to collect data from sources such as websites and social media sites, as well as to analyze the collected data and compile it into a report.

What are automatable tasks?

Automatable tasks are those that machines can perform. Automation is the use of machines to perform tasks that humans would otherwise do. Automating tasks can make them faster, cheaper, and more consistent than human workers.

Following are some of the automatable tasks:

• Frequent, regular tasks
• Software-based tasks
• Repetitive tasks
• Time-consuming tasks
• Rule-based tasks

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Automating Administrative Tasks: A New Era for Educational Institutions

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The world is rapidly embracing automation. From manufacturing processes to customer service, automation has permeated various industries, offering efficiency, accuracy, and scalability. Educational institutions are no exception. A growing number of schools, colleges, and universities are exploring how automation can streamline administrative tasks, thus allowing them to focus more on their core mission: education.

Just as a student might turn to a writer who can do my paper from a reliable paper writing service to maximize their time, educational institutions are turning to automation to optimize their administrative processes. They are leveraging technology to handle repetitive, time-consuming tasks that otherwise drain their staff’s time and resources.

Key Benefits of Automating Administrative Tasks

By automating administrative tasks, educational institutions stand to gain in several ways.

Efficiency and Accuracy

By mitigating the likelihood of errors inherent in manual data input, automation offers the ability to handle extensive volumes of data with greater speed and precision than is humanly possible. This results in more reliable data, which can inform better decision-making.

Moreover, the efficiency that automation brings to educational institutions is not confined to task execution. It also aids in better resource allocation. When an automated system handles routine tasks, management can more effectively allocate human resources to areas where they can bring maximum value.

Time and Cost Saving

Automating tasks that were previously performed manually frees up staff time, allowing them to focus on more critical, high-value tasks. It can also reduce costs related to paper-based processes and manual labor.

Additionally, cost savings from automation can be redirected to areas that directly impact students’ learning experiences. Schools can invest more in innovative teaching tools, classroom infrastructure, and other resources that can enrich students’ education.

Ensuring Equitable Access

While the benefits of automation are clear, it’s essential to consider equitable access to these new technologies. Not all students and staff may have the same level of comfort or familiarity with digital tools. Institutions need to take active measures to ensure that the transition toward automation does not unintentionally exacerbate the digital divide. This involves providing necessary training and support to ensure everyone can benefit from automated systems.

As institutions transition to automation, they should also focus on providing assistance to those who might initially struggle with the new systems. This could involve offering tutorials, having a tech support team in place, and regularly seeking feedback to identify and address any issues promptly. The goal is to ensure that everyone—staff and students alike—can adapt to and benefit from the automation revolution in education.

Types of Administrative Tasks That Can Be Automated

Several administrative tasks can be automated in an educational setting, including but not limited to:

• Admission Processes : Automation can help streamline processes like application handling, document verification, and student onboarding.
• Attendance Tracking : Automatic attendance tracking can eliminate manual roll calls and associated record-keeping.
• Timetable Management : Timetabling software can create and manage class schedules, reducing the administrative burden.
• Fee Collection and Financial Management : Automating these tasks can reduce paperwork and manual record-keeping, thus reducing errors and enhancing efficiency.

Beyond these tasks, automation can also assist in areas like communication and student engagement. Automated email systems, for instance, can send reminders for assignment due dates or important school events, ensuring that all students receive timely and consistent communication.

Challenges and Considerations

While the benefits of automation are significant, its implementation is not devoid of challenges. Institutions need to consider factors such as cost, data privacy, and staff training. It’s essential to ensure that any automation solutions adopted align with the institution’s needs and capabilities.

Furthermore, while automation brings many benefits, it also presents new challenges, including the risk of dependence on technology. Institutions must have a robust contingency plan to ensure that they can continue to function effectively even in the event of a system failure or cyber-attack.

Impact on Staff Roles and Responsibilities

The automation of administrative tasks inevitably changes the roles and responsibilities of administrative staff. Instead of focusing on routine, mundane tasks, administrators can shift their focus to more strategic, value-added activities. This transition, however, requires training and upskilling to equip staff with the necessary skills to manage and work alongside automated systems.

Despite the technological shift, it’s critical to acknowledge that automation serves as a tool and does not intend to replace human personnel. Automation can handle routine tasks efficiently, but human touch and judgment are still essential in many aspects of educational administration. Schools must strike a balance between automating tasks for efficiency and maintaining the personal touch that only humans can provide.

The Role of Artificial Intelligence (AI) in Automation

Artificial Intelligence (AI) has emerged as a crucial player in facilitating the automation of administrative tasks in educational settings. AI-powered chatbots, for instance, are increasingly being used to handle routine inquiries from students and parents, providing immediate responses and freeing up administrators for more complex tasks.

Additionally, AI-enabled systems can process and analyze copious amounts of data to extract valuable insights. For instance, they can track and analyze student performance data over time, helping educators identify trends, address learning gaps, and customize teaching strategies.

The Role of Software in Automating Administrative Tasks

The role of software is paramount in the successful deployment of administrative task automation. From student information systems (SIS) that manage student records to learning management systems (LMS) that handle course administration, the choice of software can significantly impact the efficiency and effectiveness of automation.

It’s of utmost importance to opt for software that offers customization to align with the unique requirements of the institution. The software should have a user-friendly interface, robust features, and the ability to integrate with other systems in the institution. This ensures seamless operations and a smoother transition for all users.

Moreover, robust software solutions offer scalability, allowing institutions to adapt to growing student populations and evolving needs over time. This flexibility is crucial in the ever-changing landscape of education, ensuring that the benefits of automation can continue to be enjoyed even as circumstances change.

Privacy and Security in Automation

As automation becomes increasingly prevalent in educational institutions, privacy, and security concerns come to the fore. With vast amounts of sensitive student and staff data being processed and stored digitally, educational institutions need to ensure robust data protection measures are in place.

This involves implementing strong cybersecurity measures to protect against data breaches, including the use of encryption, secure data backup, and regular system audits. Educating staff and students about cyber threats and safe online practices is also essential to maintain the integrity of the system.

In addition, institutions should comply with all relevant data protection regulations, such as the Family Educational Rights and Privacy Act (FERPA) in the U.S. This includes obtaining necessary permissions before collecting or using personal data and ensuring that data is used for its intended purpose only.

Future Prospects of Automation in Educational Administration

The future of automation in educational administration looks bright. As technology progresses, the potential and reach of automation are likely to expand correspondingly. We can anticipate more advanced AI and machine learning applications, offering even greater efficiency and personalization in administrative tasks.

There are also prospects for increased automation in decision-making processes . With AI’s ability to analyze massive amounts of data and identify patterns, it could play a significant role in areas such as admission decisions or curriculum development.

However, as we adopt the future of automation, it is imperative to consider the ethical considerations it brings forth. It’s crucial to maintain a human element in our educational systems and ensure that automation serves to enhance rather than replace the valuable contributions of educators and administrators. Automation should be a tool to empower humans in education, not a substitute for them.

Adapting to Automation: Skills for the Future

The shift to automation in administrative tasks is changing the skills landscape within educational institutions. While there’s less need for manual data entry, there’s an increased demand for skills related to managing and maintaining automated systems.

This necessitates continuous learning and professional development for administrative staff. Institutions should provide training in areas such as data analytics, cybersecurity, and software management . Staff also need to be proficient in digital literacy, given the technological nature of automated systems.

Moreover, as automation takes the helm of routine tasks, personnel can dedicate their efforts toward tasks that necessitate human intellect and creativity. This could involve strategic planning, problem-solving, or developing innovative approaches to improve administrative processes. Hence, skills in these areas will become increasingly important in the automated era.

Implementing Automation: A Step-By-Step Approach

The shift toward automation is a considerable initiative that necessitates meticulous planning and implementation. Institutions should begin with a thorough analysis of their current administrative processes to identify areas that would benefit most from automation.

The next step involves selecting appropriate automation tools. This requires a detailed understanding of the institution’s needs and capabilities, as well as a comprehensive evaluation of different software options. It might also be beneficial to engage the services of a technology consultant or specialist.

Finally, the implementation phase should be gradual, starting with one process or department before expanding to others. This allows for any issues to be addressed in a controlled environment, minimizing disruption to the institution. Feedback from early users should be used to fine-tune the system before a full-scale rollout.

Automation and Student Experience

While the administrative benefits of automation are clear, it’s equally important to consider its impact on students. After all, the ultimate goal of educational institutions is to provide quality education, and any administrative changes should serve this purpose.

Automation can enhance students’ experience in various ways. For instance, automated enrollment and payment systems can make these processes more convenient and less time-consuming. Similarly, automated communication tools can ensure that students receive timely and consistent information from their institution.

However, institutions must also be mindful of potential challenges. Not all students may be comfortable or familiar with digital tools, and institutions should ensure adequate support is available for these students. Additionally, as more student services become automated, institutions should continue to offer personal support and guidance. While automation can streamline processes, the value of personal interaction in the educational experience should not be underestimated.

Automation: An Environmentally Friendly Choice

Automation also brings benefits from an environmental perspective. By digitizing administrative tasks, educational institutions can reduce their reliance on paper, leading to significant environmental savings. This is in line with global efforts to reduce waste and promote sustainable practices.

An automated, paperless administration not only reduces the institution’s environmental footprint but also enhances efficiency. Digital records are easier to search, access, and manage compared to physical files, making processes like record retrieval and auditing more efficient. Thus, automation supports both environmental and administrative goals.

Beyond Automation: The Role of Human Engagement

While automation promises numerous benefits, it’s essential to understand that technology cannot substitute the importance of human interaction within the educational landscape. Personal engagement and human judgment play a critical role in many administrative functions.

For instance, while AI can aid in the admissions process by shortlisting candidates based on certain criteria, the final decision often requires human judgment, considering factors such as the student’s motivation, resilience, and fit with the institution’s culture.

Similarly, although automation can streamline student services, personal guidance from advisors and counselors is irreplaceable. Such individuals offer empathetic guidance and counsel, aiding students in maneuvering through academic hurdles and personal issues. Thus, as institutions embrace automation, they must also continue to prioritize and value human engagement.

Conclusion: Automation—A Necessity Not a Luxury

As we move further into the digital age, automation in educational administration is becoming less of a luxury and more of a necessity. Automation holds the promise of streamlining processes, alleviating administrative loads, and liberating resources for more strategic tasks.

However, the transition toward automation must be handled thoughtfully, taking into consideration costs, staff training, and ensuring equitable access. In much the same way that students rely on the best essay editing services to enhance the quality of their academic work, educational institutions are leveraging automation to improve the quality and efficiency of their administrative tasks. Like a skilled editor who transforms a good paper into a great one, automation has the potential to elevate educational administration to new heights of efficiency and effectiveness.

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Task Automation

Task Automation involves using software, machinery, or other technologies to perform tasks with minimal or no human intervention. It is often used to handle repetitive, time-consuming, or error-prone tasks, thereby increasing efficiency and accuracy. Task Automation can range from simple tasks such as sending out email reminders to more complex processes like data analysis and report generation.

Why is Task Automation important?

Task Automation is important for several reasons:

• Efficiency : It enables organizations to accomplish tasks more quickly and accurately, reducing the time spent on manual, repetitive activities.
• Cost Savings : Automation can lead to cost savings by reducing the need for human labor for routine tasks.
• Error Reduction : Automated systems are less prone to errors compared to manual processes, improving data accuracy and consistency.
• Employee Productivity : It frees up employees’ time, allowing them to focus on more strategic and high-value tasks.
• Scalability : Automation makes it easier for organizations to scale their operations without proportionate increases in labor costs.

What are some examples of Task Automation?

Examples of Task Automation include:

• Email Automation : Automatic sending of emails based on triggers or schedules.
• Data Backup and Synchronization : Automated systems for backing up and synchronizing data across platforms.
• Social Media Posting : Scheduling and auto-posting of content on social media platforms.
• Invoice Generation : Automatic creation and sending of invoices based on predefined criteria.
• Customer Support Chatbots : Using chatbots to handle basic customer inquiries and support.
• Robotic Process Automation (RPA) : Using software robots to automate repetitive tasks such as data entry, data extraction, and process workflows.

How can I start implementing Task Automation in my organization?

To start implementing Task Automation in your organization:

• Identify Repetitive Tasks : Start by identifying tasks that are repetitive, time-consuming, and don’t require high-level human decision-making.
• Set Clear Goals : Define what you want to achieve through automation, such as cost savings, increased efficiency, or error reduction.
• Choose the Right Tools : Research and select automation tools or software that best fit your organization’s needs.
• Pilot Implementation : Begin with a small-scale pilot implementation to test the effectiveness of the automation.
• Train Employees : Provide necessary training to employees on how to use and manage the automation tools.
• Monitor and Optimize : Continuously monitor the automated processes and make improvements as necessary.

What are the challenges associated with Task Automation?

Challenges associated with Task Automation include:

• Initial Costs : The initial investment in automation tools and technologies can be significant.
• Integration Issues : Integrating automation tools with existing systems and processes can be complex.
• Employee Resistance : Employees may resist automation due to fears of job loss or change in job roles.
• Maintenance and Upgrades : Automated systems require ongoing maintenance and upgrades.
• Complexity : Some tasks may be too complex or variable to be effectively automated.

What is the future of Task Automation?

The future of Task Automation is expected to involve:

• Advanced AI and Machine Learning : Integration of AI and machine learning for more intelligent and adaptive automation.
• Wider Adoption Across Industries : As technology evolves, more industries are likely to adopt automation for a wider range of tasks.
• Human-Robot Collaboration : Greater collaboration between humans and robots, where robots handle repetitive tasks while humans focus on creative and strategic aspects.
• Process Automation and Optimization : Focus on automating not just tasks but entire processes for increased efficiency.
• Ethical and Societal Considerations : Increased focus on the ethical and societal implications of automation, including its impact on employment and data privacy.

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Technology and automation in education.

Nowadays, the situation is demanding digital transformation for all industries and education is the most important sector that is seeking digital transformation, automation in education has various benefits for the management, admin staff, administration, and students. 

Just think about how your institution can grow and how the students will get more time to study and learn with the spacious time that your institution is spending in the academic and administrative tasks just from the admission process to the examination process. Automation in Education can make everything simple and easier for them. 

Now let’s understand how technology can change the education sector in tremendous ways:

How technology plays an important role in education?

Technology is changing the overall ways of working and opening new doors of innovation and creation for the world, almost all industries are making their way to opt for online and technical things that can help them in growing more. It has a specific role to be played in the education sector, having a tech hand you can incorporate to aid and enhance the overall education experience. 

Technology has made it possible for the administration to handle the overall academic and administrative tasks easily and efficiently, it also has made it easier for the students to have individual learning than group learning instead of online classes they can get any of the required data anytime and anywhere according to their convenience, it ultimately results in more output creation than other things. 

What is Automation?

Automation is a method or technique of operating and controlling a single or different process, its core focus is to minimize the manual work in repetitive tasks, and it helps the teachers, faculties, and admin to focus more on the proper teaching and learning process of the students. 

Looking at the aspects of students, automation helps in optimizing the time along with helping them to learn whenever they want, whatever they want, and however, they want.

How can you access code-free automation?

It is often one of the most common challenges for the school administrations, as not the faculties, admin staff, and management knows about coding, so it becomes difficult for them to grasp the coding process. But most of the automation in education processes involves no code and they work the same way to eliminate the manual process of the organization. 

The organization opting for the automated systems just requires some basic training that can help them to understand and execute the automated system.

Processes that need automation in an institution:

• Course Registration and Pre-admission 

Through automation it becomes very easy for institutions to register the courses and also to complete the admission process thoroughly, they do not need to distribute forms for admissions, collect them, and not even register them manually for individual courses, all these things process out automatically without any data glitches and problems, so bringing automation in education you can make the admission process easier to manage without any error. 

• Student Shortlisting 

Automation makes it easy to select students, there are various ways to shortlist students based on various criteria like EQ, IQ, etc. Students who apply and perform well based on your criteria can be eligible for admission, with a dashboard and list marking all the details. So in simple words, automation makes it easy to shortlist eligible and interested candidates. 

• Student Grading and Assessment

When it comes to grading the students, faculties follow a manual and lengthy process of filling forms and report cards to mark the grades, meanwhile, automation helps them to do all these tasks simply and efficiently marking the grades with just one single click for particular students after having the quizzes, tests, projects, or any other form of assessment. This process even helps the students to get faster feedback to work forward for their next assignments. 

• Attendance Record 

Through automation in education, you can manage all the small and big tasks of the academy and administration, one of them are to mark attendance, so with automation, you will need to take even a little bit of stress about registers and notebooks to manage the attendance, digital attendance will automatically record the date and time for each student with your one click, you will also have a systematic and updated data to check any attendance presence for any day. 

• Administrative Tasks 

There are so many tasks in an institution to manage, administration, accounts, marketing, and a lot more. Imagine you can have access to all this information in just one click, seeming interesting? With automation, you can manage all the tasks eliminating the physical work and bringing efficiency, agility, and transparency to all the administrative operations. Just think how things will become easy for you to have Report Cards, Certificates, Payslips, and various other things in a single hand along with capturing them for a lifetime eliminating the files eaten by spiders and termites. 

• Scheduling the Classes

In the manual and offline world, schools take the examination in the middle, and at the end of the year, it becomes a time-consuming process, just think of the situation that how automation can make the process easy for you by organizing the whole process automatically, the interaction between student and teachers can be quite easier and through as teachers can make the online papers easily and can check them using automated system meanwhile students can also study hard with any time available online courses, they will also get the opportunity to collect the hall ticket online instead of going to the school/college occupying their study time. All this can directly and straightforwardly impact the growth of the student and the organization.

• Audit Report and Compliance 

Schools and colleges are the platforms that use and teach utmost regulations and discipline to students. All the things here need perfection and compliance. So using automation you can manage all the audits systematically without any paperwork. Automation in education helps in managing the complete system of accounting, auditing, and compliance. 

Check out our video on Automation in Education through ERP

Conclusion 

There are numerous benefits of automation in education, it allows the administration and faculties to focus on their main motive which is the growth of students and the organization. 

Academia is one of the best platforms that provide thought-leading, flexibility, high scalability, and comprehensive services to institutions that are focusing on the great growth of their students’ organization along with driving efficiency, productivity, and transparency. s

So what are you thinking about?  Opt for automation in your institution to transform the complete work experience and output generation completely. So contact us now and book your free demo to learn more about how you can leverage automation through Academia in your institution.

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AI & Automation

How to use ai to automate tasks.

Content Operations Specialist

June 27, 2024

Boring, repetitive tasks, such as summarizing call details after every customer interaction or repeatedly answering the same questions, can drag your team down and hurt productivity. 

But with artificial intelligence (AI), we can finally say goodbye to these mundane chores. 

Interestingly, a survey by Gartner found that up to 80% of executives believe automation can be applied to any business decision. This shows the incredible potential for organizations that integrate AI into their processes.

In this blog, we’ll closely examine what AI task automation is and how it can be used to modify how your organization works. ⬇️

What is AI Task Automation? 

The role and benefits of ai in task automation, what are the different tasks ai can automate, how to automate tasks with ai, why clickup is the best ai automation tool, operational, ethical, and privacy challenges of implementing ai, make ai work for you with clickup.

AI automation refers to using artificial intelligence technologies to streamline and perform (repetitive) tasks typically and traditionally done by humans.

This approach addresses skills and labor shortages while increasing operational efficiency; freeing employees from repetitive tasks, allowing them to focus on higher-value, strategic work.

AI automation combines Natural Language Processing (NLP), Robotic Process Optimization (RPA), computer vision, and other machine learning algorithms that effectively automate business processes. 

Trained on large unstructured data sets, these technologies can execute complex tasks without human intervention by following predefined rules and adapting based on real-time data inputs.

Machine learning models, which are trained on data, identify patterns and generate predictions . 

NLP algorithms enable systems to understand and generate human language , crucial for applications like chatbots. RPA bots mimic human actions to perform repetitive tasks, while computer vision processes visual information to make decisions based on images and videos.

Many organizations use these AI automation for business purposes, across functions ranging from marketing to customer service . 

For instance, Amazon uses AI in fulfillment centers to detect damaged goods, tripling the accuracy compared to humans. Trained on millions of images, this AI flags imperfect items for further assessment, leading to resale, donation, or reuse instead of direct customer shipping.

Benefits of AI automation

Artificial intelligence plays a multi-dimensional role when it comes to automation. Let’s see how it can benefit your business. 👀

• Enhance productivity: AI can handle repetitive tasks, allowing your teams to focus on more important work that needs human attention. This reduces their workload and boosts their productivity and the quality of their work
• Solidify decision-making: By supplying data to AI, you can receive assistance in forecasting, identifying future product trends, and accessing industry insights. These insights and patterns that human eyes might miss help improve your decision-making process
• Save costs: AI automation helps save costs by reducing labor costs, increasing efficiency, and minimizing errors. It optimizes resource utilization, enhances predictive maintenance, and streamlines supply chains, thereby reducing logistical waste and downtime

Artificial intelligence can automate many aspects of business operations, ranging a few simple tasks (think status updates) to complex, multilayered inventory processes.

Below are five use cases that will help you understand how it works.

1. Operations management

On the operations front, AI can help automate back-office tasks like processing invoices, handling documentation and document processing, managing accounts, overseeing the supply chain, and keeping track of inventory. This helps streamline operations and reduce supply chain costs incurred through inventory mismanagement.

Walmart uses AI to streamline its supply chain in various ways. AI helps manage inventory by monitoring stock levels and predicting demand, so they avoid stockouts and overstocks, keeping customers happy and reducing costs. 

It also optimizes the movement of goods , cutting costs, boosting productivity, and speeding up deliveries to stores. For pricing, AI sets prices based on demand, competition, and costs , helping Walmart stay competitive and maximize profits.

2. Customer service

Almost 90% of customers consider an immediate response an essential part of customer service. 

That’s why the customer service industry extensively uses AI CRM tools and AI-powered chatbots to answer repetitive customer queries , such as ‘When does the store open?’ or ‘Is this product returnable?’ 

AI task automation removes the burden of providing such responses from human workers, involving them only in complex issues. It also saves you from the hassle of hiring night-shift workers or investing money in more employees in general.

Amazon’s AI advancements, including Q in Connect and Amazon Connect Contact Lens, demonstrate how AI is used for customer service automation. 

“With just a few clicks, contact center leaders can leverage new capabilities powered by generative AI in Amazon Connect to enhance the more than 15 million customer interactions handled on Amazon Connect every day.”

3. Data analysis and predictive analytics

Much decision-making relies on vast datasets, surveys, and reports, which can be overwhelming for your human colleagues to manage due to their sheer volume. This is where AI becomes invaluable with its machine learning, NLP, and computer vision capabilities.

AI task automation can accurately analyze large datasets and provide necessary insights , allowing business teams to focus more effectively on work relevant to their field. Of course, you can’t completely omit the human element from the decision-making process, especially on aspects like sentiment analysis. This is what the combination looks like in practice:

A good example is e-commerce retail businesses that use large amounts of customer data to create user profil es. These profiles automatically suggest products to consumers based on their activity and purchase history.

AI tools like Klaviyo and Attentive rely on AI-based automation for advanced audience segmentation features in email marketing. These features analyze customer behavior to create email segments for personalized outreach .

With this, you can automate separating customers based on certain demographics, so there’s no need to manually go through every customer profile and sort them into a certain marketing bracket.

4. Marketing

AI can automate marketing tasks like email and text campaigns, managing social media posts, generating personalized product recommendations , or creating social media copies. This helps you reach more customers on the right platforms at the right time, ultimately boosting sales.

Moreover, a recent McKinsey study projected that AI could add up to $4.4 trillion in value to the global economy by 2030, with marketing being a primary beneficiary.

In the image above, you have Heinz using a version of Dall-E to produce award-winning visual campaigns using text-to-image AI and automating the graphical design process , or Coca-Cola using AI content creation tools to create Christmas cards for a festive campaign.

You can use AI to automate sales tasks such as qualifying leads, scheduling appointments with prospects, and auto-generating follow-up emails at fixed intervals. These help your sales team close more deals and increase revenue.

AI and machine learning transform the prospect assessment system by continuously refining scoring models to identify high-potential leads . This allows sales teams to engage with top prospects at the right moment, boosting conversion rates and revenue. 

AI goes beyond traditional datasets and incorporates diverse data sources , such as social media, website interactions, and email engagement, to provide a comprehensive lead picture. 

For instance, Razorpay’s ML-powered lead scoring exemplifies this effectiveness, achieving a 50% increase in monthly Gross Merchandize Value (GMV), a 70% reduction in team effort, and a one-month shorter conversion cycle.

We’ve covered a lot about what AI task automation can do for you, but now, let’s talk about how you can actually put it to work for your business.

To get started with AI automation, follow these steps.

Step 1: Identify tasks that can be automated

The first step is to identify tasks in your business that can be automated. 

Focus on time-consuming, error-prone, or repetitive tasks . Be mindful of what you are automating. Don’t automate a lot at once, as it can be difficult to manage.

Also, if you are just starting with automation, save the critical areas for later when your business and teams are used to AI automation. 

Step 2: Choose the right tools

This step is pretty much a make-or-break for your AI automation journey. With so many AI automation tools , picking the right one is key, based on what you need and what fits your budget .

For example, ClickUp and Asana are solid options for automating tasks and managing projects. But if you’re focusing on AI email marketing and management, you might consider Mailbutler or EmailTree.

Step 3: Set up the tool

After selecting an appropriate process automation tool , the next step is setting it up. This typically involves supplying the tool with training data and configuring it to automate the desired tasks. 

You can also talk with your AI service providers and develop bespoke solutions to your problems. 

Step 4: Constant testing and monitoring

Once you have set up the AI tool, it’s essential to test and monitor how the automated tasks are performing. This ensures the tool functions correctly and effectively automates the desired tasks.

Conduct audits consistently to ensure data security, privacy, and transparency .

Selecting the right tool for your automation journey is essential. But here’s the thing: you can now skip the headache of choosing a different AI tool for each task.

Yes, there’s software that condenses all those functionalities into a neat little package: ClickUp.

Let’s take a glance at some of its most exciting AI and automation features.

ClickUp Brain

ClickUp comes with the groundbreaking in-built AI assistant ClickUp Brain . It is the world’s first neural network that integrates tasks, documents, and people across your company’s knowledge base with AI.

The three main features include:

• AI Knowledge Manager: Ask questions and automatically extract answers from anywhere from your ClickUp workspace
• AI Project Manager: Manage and automate multiple tasks like summarizing chat threads and updates for task completion, assignment, and more
• AI Writer : Create content and draft replies for clients automatically using the AI Writer

Plus, ClickUp Brain integrates with other tools, facilitating seamless data transfer. 

You can use ClickUp Integrations to easily integrate popular apps such as HubSpot, GitHub, and Twilio or create custom webhooks for any other application , allowing seamless automation across your digital stack from a centralized platform.

ClickUp AI Builder

One unique feature that differentiates ClickUp’s automation capabilities from other applications is its intuitive AI Builder . 

Using ClickUp Brain, the AI Builder makes workflow automation effortless for any team. Just describe the automation you need in plain English, and our integrated AI will swiftly configure task automation across any Space , Folder, or List.

ClickUp Recurring Tasks

And if you don’t want to waste too much time manually automating simple tasks that come up daily, weekly, monthly, or at any fixed interval, use ClickUp’s Recurring Tasks . You can choose between Daily, Weekly, Monthly, Yearly, Days After, or Custom repetitions.

The Recurring Tasks feature also lets you create custom specifications of when the task should recur , like after it’s completed or after a certain time period. You have a lot of freedom with this feature, as you can virtually repeat tasks in any way that you can imagine.

ClickUp Automation Library

ClickUp comes with a host of automation features under ClickUp Automation . These include templates, shortcuts, email automation, audit logs, and integrations that will help you zap through your daily tasks.

Speaking of templates, ClickUp offers over 100 prebuilt templates in its Automation library. These help you swiftly automate repetitive tasks like assigning tasks, posting comments, changing statuses, moving Lists, and much more.

ClickUp doesn’t just handle internal automation—it’s also a champ at customer service. Improve your communication efficiency with ClickUp’s email automation.

Address customer feedback submitted via ClickUp Forms automatically, ensure partners and vendors stay informed with automatic project updates, and more.

ClickUp Audit Logs

If you are worried that you might get lost in this sea of automation, ClickUp has a solution for that as well. 

With ClickUp’s Audit Logs , you can easily track automation status, actions, and locations. You can also verify details and make necessary adjustments from a unified dashboard, ensuring seamless management of your automated processes.

ClickUp’s powerful project management combined with automation has brought great results for our clients.  For instance, at STANLEY Security, a global security solutions company, ClickUp helped bring all the distributed teams together on one single platform. They were able to save 8+ hours every week and reduce time to generate reports by 50%. 

“We’re able to customize and automate ClickUp to suit each specific initiative, and it has allowed us to streamline and simplify our workflows, which has increased our team’s capacity exponentially.”

While AI automation undoubtedly signals the advent of the future, business implementation is still rough around the edges. Let’s examine some of these challenges and explore possible solutions.

Operational challenges

First on our list are operational challenges that hinder the capabilities of AI in the workplace . Let’s take a closer look:

• Cost of implementation: This depends on what you are using AI for. The price will be relatively low for simple content creation or assistance. However, if you need to analyze large data sets, you need the computing power to back it up. As a business, you might incur huge costs for database integration and lengthy data training . The best solution is a SaaS service with built-in AI functionality and other workflow automation tools .
• Data quality: The results from AI are only as good as the data you feed it. But here’s the snag: in many businesses, data is all over the place—it’s siloed, disorganized, or just plain low quality. And that’s a big roadblock to reaping the benefits of AI. This so-called ‘dirty data’ could be outdated, inaccurate, incomplete, or inconsistent . Before you can do anything useful with it, you’ve got to clean it up. That means scrubbing out errors, filling in missing bits, and making sure everything’s consistent and up-to-date
• Lack of technical skills: Adopting AI requires serious know-how and skills many organizations lack. Putting AI into action can be challenging without that expertise, holding back its full potential. But here’s the fix: it’s simple, though it might require some resources—invest in training your employees to handle these changes
• Integration with legacy systems: Legacy systems are often incompatible with AI. Modifying such systems to accommodate AI is often complex and time-consuming. Hindered integration with existing systems can lead to inefficiencies and increased costs. Modernizing these systems with an efficient operational framework and cutting-edge technologies to facilitate AI adoption

Ethical challenges

A PwC survey found that 85% of CEOs believe AI will significantly change their business in the next five years, with ethical concerns being a key issue.

AI faces many ethical issues, such as:

• Data privacy and security: AI systems depend heavily on data, and the collection, storage, and analysis of large amounts of personal and sensitive information raises concerns about privacy and security. Safeguarding this data per local laws and ensuring its responsible use is crucial for maintaining trust in AI technologies
• Lack of transparency in AI decision-making: AI algorithms are complex, and how they arrive at certain decisions might not be clear to individuals without technical know-how. This lack of transparency can raise concerns about biases and prejudices , particularly in critical areas such as healthcare and criminal justice
• Job disruption: AI-powered automation has the potential to reshape the job market. With a lot of the knowledge-work tasks being automated, workers will eventually need to upskill or even change careers entirely

“I see it (AI) as a productivity enhancer. It will destroy employment in some areas; I mean, there will be parts of the labor markets where tasks can be replaced to a degree. But then you will also find other ways of innovating and creating more jobs somewhere else. I mean, this is the story of economic growth and innovation for hundreds of years that you have an innovation that is basically labor saving and that reduces employment in some areas, but then boosts it in others.”

The solution? A change in perspective

Companies must promote transparency in AI decision-making, establish ethical guidelines for AI use, and conduct frequent audits to identify biases. Take steps to upskill your workforce so there is little traction in adopting AI automation.

In an interesting conversation, Pascal Bornet, Author of IRREPLACEABLE & Intelligent Automation discusses a middle-path where AI and human skills play equal parts in getting jobs done. He stresses the importance of developing and strengthening uniquely human abilities, termed “Humics,” which are difficult for AI to replicate. 

These include:

• Genuine creativity: Creating original ideas and artistic expressions based on human experiences, emotions, and intuition
• Critical thinking: Analyzing information, questioning assumptions, and making ethical judgments based on human values and context
• Social authenticity: Building deep, trust-based relationships, communicating with empathy, and leading others with emotional intelligence

By focusing on these Humics and synergizing with AI, individuals can enhance their value by automating routine tasks and emphasizing human creativity and interpersonal skills . 

Bornet also advises that employees be “change-ready,” if they really want to keep up in the unpredictable and quick-changing world.

AI is the future, and there is no doubt about it. And bringing it into your business automation? That’s bound to slash costs and free up your employees’ time.

However, the major deciding factor is your choice of AI task automation software . You can go down the specialized route and seek assistance from different AI applications for different tasks. 

But when it comes to centralized automation, there’s no platform as comprehensive as ClickUp. It is easy to use, powerful, flexible, and cost-effective. You don’t need coding prowess to incorporate custom or pre-built automation into your workflow.

So, what’s keeping you here? Try ClickUp for free today!

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Another lifesaver for data engineers: Apache Doris Job Scheduler for task automation

Job scheduling is an important part of data management as it enables regular data updates and cleanups. In a data platform, it is often undertaken by workflow orchestration tools like Apache Airflow and Apache Dolphinscheduler . However, adding another component to the data architecture also means investing extra resources for management and maintenance. That's why Apache Doris 2.1.0 introduces a built-in Job Scheduler. It is strategically more tailored to Apache Doris, and brings higher scheduling flexibility and architectural simplicity.

The Doris Job Scheduler triggers the pre-defined operations at specific time points or intervals, thus allowing for efficient and reliable task automation. Its key capabilities include:

Efficiency : It adopts the TimeWheel algorithm to ensure that the triggering of tasks is precise to the second.

Flexibility : It supports both one-time jobs and regular jobs. For the latter, users can define the start/end time, and intervals of minutes, hours, days, or weeks.

Execution thread pool and processing queue : It is supported by a Disruptor-based single-producer, multi-consumer model to avoid task execution overload.

Traceability : It keeps track of the latest task execution records (configurable), which are queryable by a simple command.

Availability : Like Apache Doris itself, the Doris Job Scheduler is easily recoverable and highly available.

Syntax & examples ​

Syntax description ​.

A valid job statement consists of the following elements:

CREATE JOB : Specifies the job name as a unique identifier.

The ON SCHEDULE clause: Specifies the type, trigger time, and frequency of the job.

AT timestamp : This is used to specify a one-time job. AT CURRENT_TIMESTAMP means that the job will run immediately upon creation.

EVERY : This is used to specify a regular job. You can define the execution frequency of the job. The interval can be measured in weeks, days, hours, and minutes.

• The EVERY clause supports an optional STARTS clause with a timestamp to define the start time of the recurring schedule. CURRENT_TIMESTAMP can be used. It also supports an optional ENDS clause to specify the end time for the job.

The DO clause defines the action to be performed when the job is executed. At this time, the only supported operation is INSERT.

The above statement creates a job named my_job , which is to load data from db2.tbl2 to db1.tbl1 every minute.

More examples ​

Create a one-time job : Load data from db2.tbl2 to db1.tbl1 at 2025-01-01 00:00:00.

Create a regular job without specifying the end time : Load data from db2.tbl2 to db1.tbl1 once a day starting from 2025-01-01 00:00:00.

Create a regular job within a specified period : Load data from db2.tbl2 to db1.tbl1 once a day, beginning at 2025-01-01 00:00:00 and finishing at 2026-01-01 00:10:00.

Asynchronous execution : Because jobs are executed in an asynchronous manner in Doris. Tasks that require asynchronous execution, such as insert into select , can be implemented by a job.

For example, to asynchronously execute data loading from db2.tbl2 to db1.tbl1 , simply create a one-time job for it and schedule it at current_timestamp .

Auto data synchronization ​

The combination of the Job Scheduler and the Multi-Catalog feature of Apache Doris is an efficient way to implement regular data synchronization across data sources.

This is useful in many cases, such as for an e-commerce user who regularly needs to load business data from MySQL to Doris for analysis.

Example : To filter consumers by total consumption amount, last visit time, sex, and city in the table below, and import the query results to Doris regularly.

Step 1 : Create a table in Doris

Step 2 : Create a catalog in Doris to map to the data in MySQL

Step 3 : Ingest data from MySQL to Doris. Leverage the catalog mechanism and the Insert Into method for full data ingestion. (We recommend that such operations be executed during low-traffic hours to minimize potential service disruptions.)

One-time job : Schedule a one-time full-scale data loading that starts at 2024-8-10 03:00:00.

Regular job : Create a regular job to update data periodically.

Technical design & implementation ​

Efficient scheduling often comes at the cost of significant resource consumption, and high-precision scheduling is even more resource-intensive. To implement job scheduling, some people rely on the built-in scheduling capabilities of Java, while others employ job scheduling libraries. But what if we want higher precision and lower memory usage than these solutions can reach? For that, the Doris makers combine the TimingWheel algorithm with the Disruptor framework to achieve second-level job scheduling.

To implement the TimingWheel algorithm, we leverage the HashedWheelTimer in Netty. The Job Manager puts tasks every 10 minutes (by default) in the TimeWheel for scheduling. In order to ensure efficient task triggering and avoid high resource usage, we adopt a Disruptor-based single-producer, multi-consumer model. The TimeWheel only triggers tasks but does not execute jobs directly. Tasks that need to be triggered upon expiration will be put into a Dispatch thread and distributed to an appropriate execution thread pool. Tasks that need to be executed immediately will be directly submitted to the corresponding execution thread pool.

This is how we improve processing efficiency by reducing unnecessary traversal: For one-time tasks, their definition will be removed after execution. For recurring tasks, the system events in the TimeWheel will periodically fetch the next round of execution tasks. This helps to avoid the accumulation of tasks in a single bucket.

In addition, for transactional tasks, the Job Scheduler can ensure data consistency and integrity by the transaction association and transaction callback mechanisms.

Applicable scenarios ​

The Doris Job Scheduler is a Swiss Army Knife. It is not only useful in ETL and data lake analytics as we mentioned, but also critical for the implementation of asynchronous materialized views . An asynchronous materialized view is a pre-computed result set. Unlike normal materialized views, it can be built on multiple tables. Thus, as you can imagine, changes in any of the source tables will lead to the need for updates in the asynchronous materialized view. That's why we apply the job scheduling mechanism for periodic data refreshing in asynchronous materialized views, which is low-maintenance and also ensures data consistency.

Where are we going with the Doris Job Scheduler? The Apache Doris developer community is looking at:

Displaying the distribution of tasks executed in different time slots on the WebUI.

DAG jobs. This will allow data warehouse task orchestration within Apache Doris, which will unlock many possibilities when it is combined with the Multi-Catalog feature.

Support for more operations such as UPDATE and DELETE.

• Syntax description
• More examples
• Auto data synchronization
• Technical design & implementation
• Applicable scenarios
• Sponsorship
• Discussions
• How to contribute
• Source code
• Improvement proposal

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Prospects dim for quick shift to statewide teacher salary schedule in Oregon

• Published: Jun. 27, 2024, 7:00 a.m.

To avert situations like the Portland Public Schools teachers' strike, pictured above, a state task force has been considering whether to recommend a statewide salary schedule for public school employees, but change is unlikely to come soon. Beth Nakamura

• Julia Silverman | The Oregonian/OregonLive

A state task force whose recommendations could have seeded a dramatic overhaul of how Oregon school districts bargain labor contracts with teachers is inching towards some consensus, after months of discussion.

But at least for now, the group’s findings look unlikely to imminently upend the state’s current system, which allows each school district to independently negotiate wages and benefits with its teachers and support staff without state limits or sideboards.

“We’ve heard a number of positives and also negatives associated with statewide salary and minimum salary schedules and mixed results from other states regarding whether a statewide salary schedule has actually addressed workforce issues,” task force member Alisha McBride, superintendent of the Vale school district in eastern Oregon, said during Wednesday’s meeting of the Joint Task Force on Educator Salary Schedules. “The topic deserves continued exploration, but a strong commitment to moving toward a statewide salary schedule [is] premature based on the information that we have so far.”

More specific policy proposals from the group won’t come until closer to the start of the legislative session in 2025.

But task force members seem to be aligning on a list of shared values, findings and priorities that suggests that Oregon should “continue to explore the challenges and benefits associated with moving towards statewide salary schedules or statewide minimum salaries for Oregon education.”

Both are delicate topics in a state with 197 school districts that each cherish their local control and with powerful educator unions who aren’t used to statewide guardrails on bargaining.

Districts and school employee unions are also frustrated with what they say are mismatches between how much state budget experts estimate that labor costs will increase in a given year versus the cost of living boosts sought by teachers and school employees to keep up with inflation and housing prices. Most school districts spend about 85% of their operating budgets on salaries and benefits.

Nowhere was that mismatch more evident this past school year than in Portland, where teachers went on strike for 11 days in November while seeking a 23% percent cost of living adjustment over the course of a three-year agreement.

But the state’s budget allocation to the district had presumed much leaner, more conservative raises of just 2.55% per year. The two sides eventually settled at 14.4% compounded increases over three years, with district leaders warning that even that amount would force tens of millions in budget cuts in the coming years.

At the end of the strike, state leaders from Gov. Tina Kotek on down acknowledged that the same dynamic could play out in dozens of other school districts. While no other districts have weathered strikes since Portland, budget cuts were common statewide this spring, from Medford to Bend to Salem-Keizer .

The task force has investigated whether setting a universal salary schedule could ward off similar disputes and potentially encourage more people to both enter teaching and to remain in the profession, particularly in hard to fill specialties like special education. Under such a system, each district would still bargain individually over non-wage issues such as working conditions.

But with a final adoption of their shared “values, findings and goals” set for next week, task force members on Wednesday said more study on a statewide educator salary schedule is needed, particularly to account for regional differences in the cost of living, and to unpack support staff roles, which are classified differently depending upon the school district.

The task force’s final report is due to the Legislature in mid-September. Task force members include Sen. Michael Dembrow and Rep. Courtney Neron, both Democrats from the Portland area who chair the Senate and House Education Committees, respectively. Other task force members include representatives for teachers, unions, administrators, school support staff and parents.

— Julia Silverman covers education policy and K-12 schools for The Oregonian/OregonLive. She can be reached via email at [email protected] . Follow her on x.com at @jrlsilverman.

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Task force recommends Columbus City Schools board close nine schools, realign four

The Columbus City Schools Superintendent's Community Facilities Task Force has recommended to the school board nine schools that it believes should be closed, as well as four other schools recommended for grade realignment.

The board voted unanimously Tuesday to accept the recommendations from the task force, but plans to take more time to get additional feedback from the community before voting to close any schools.

In a presentation during a Columbus City school board meeting Tuesday , the task force recommended that the board consider closing six elementary schools, two middle schools and a high school building. Last month, the task force presented options where up to 20 possible school buildings in the state's largest district could be closed under  nine different recommended scenarios .

Superintendent Angela Chapman told The Dispatch in an interview on Monday that the work of the task force "has and always will be about centering the work on the needs of our students."

"We know that these are difficult conversations to have in our community, but that doesn't mean that we should shy away from them," Chapman said. "Our kids are depending on us to lean in and have these difficult conversations —so that we can provide them with the best opportunities, the best learning spaces, best facilities that they need."

The group said that the decisions were not necessarily based off of any of the nine scenarios initially presented to the board.

Al Edmondson, chair of the task force, said Monday that the group was laser-focused on improving the quality of education for students in the district. He said he wants the community to know that "we heard them" about concerns regarding the proposals.

"And we hear loud and clear — and we want what's best for the kids," Edmondson said.

Nine schools recommended for closure

• Broadleigh Elementary School:  Students would be redistributed among the Eastgate, East Columbus, and Fairmoor elementary schools. The task force said the transition will maintain the quality of education and provide students with a broader range of academic and extracurricular opportunities.
• Buckeye Middle School:  Students would move to Marion-Franklin High School, creating a 6-12 grade site.
• Columbus City Preparatory School for Boys:  Students would join the Columbus City Preparatory School for Girls.
• Fairwood Elementary School:  Students would be moved to the Ohio Avenue or Livingston elementary schools.
• Lindbergh Elementary School:  Students would be redistributed among the Binns, Burroughs, and West Mound elementary schools.
• Moler Elementary School:  Students would be moved to the Lincoln Park or Livingston elementary schools.
• North Linden Elementary School:  Students would be redistributed among the Huy, Maize, Innis, or Northtowne elementary schools.
• West Broad Elementary School:  Students would be redistributed to the Westgate, Highland, or Valleyview elementary schools.
• McGuffey Road Facility:  Columbus Alternative High School would move its programming to East High School, which would remain open and CAHS would share space in the building.

East High School was one of the buildings initially recommended for closing because of its small student population base. There were only 50 graduating seniors from East High's Class of 2023.

Task force also proposing grade band realignment of four schools

The task force also recommended the district modify the grade bands of four schools, meaning that the ages served by the schools would change:

• Marion-Franklin High School:  Students from Buckeye Middle School will move to Marion-Franklin, creating a 6-12 grade site.
• Linden-McKinley STEM Academy:  Consolidate sixth-grade students from the feeder pattern, creating a 6-12 grade site.
• South 7-12:  Consolidate sixth-grade students from the South feeder pattern, creating a 6-12 grade site.
• Starling Pre-K -8:  Merge sixth through eighth-grade students into Westmoor and Hilltonia middle schools.

Task force also recommends closing Downtown district office

The task force also recommended closing the Columbus Education Center, located downtown at 270 E. State St., and relocate the administrators and staff working there to the district administrative building at 3700 S. High St., where most board meetings are currently held. The closure was not initially recommended by the task force.

The district said the task force recommendation is in response to the community requesting that the task force should also consider an administrative site for consolidation during the closure process. The closure of the Downtown location, however, would eliminate a central location for residents to attend school board meetings and leaves those living in the city's Northwest, Far North and Northeast sides driving a half-hour or more to the administrative building on the South Side, depending on traffic.

District says closures could save $90 million over 10 years

If the board approves the closures, the district could see up to $90 million in savings over a 10-year period, according to information the district presented this week.

The district could save as much as $7.4 million in permanent improvement savings, over $853,000 in maintenance costs and $788,000 in utility costs each year, according to its information.

District officials: mergers, redistricting could take years

District administrators told The Dispatch that even if closures are approved this year, closures and mergers could still be well over a year away. Chapman encouraged parents concerned about the future of their students' education "to take a deep breath" and recognize that the final recommendations have not been approved by the board.

The superintendent noted that the process could go beyond even when a parents' student is in a school slated for closure.

"You may have a student at Broadleigh Elementary School today, but you may not have a student at Broadleigh Elementary School by the time these decisions take effect," Chapman said.

Russell Brown, Columbus City Schools' chief of strategy and performance, said that even if the district closes schools, there would be a lengthy process of community engagement for determining the new boundaries of each school during the redistricting process.

Why didn't some schools get selected for closure?

Some schools initially recommended for closure like Cranbrook, Siebert and Lindbergh Elementary Schools, which all have high English as a Second Language (ESL) programs, were not put forward in the final recommendations because of the challenges presented with transitioning students.

Chapman said "that any transition for the school communities, it would be setting them up for success, and providing them with more opportunities."

"Therein lies the inherent challenge in us being able to really consider transition those schools at this time," Chapman said. "We would have to build — build — bigger and better facilities to make that happen. And we're not prepared to do that today."

Will any jobs be at risk?

Chapman said the district "is not anticipating" that any jobs will be directly lost across teaching, staff or administrative positions as a result of closures, although the district can choose whether to fill vacant teaching positions.

She said that the mergers could offer an opportunity to maximize the resources that the district does have, including offering more regular access to gym, art and other elective programs.

"Instead of spreading our resources out over more sites, we're spreading our resources out over fewer sites," Chapman said. "So we should have greater capacity to meet the existing staffing needs that we have."

What could happen to closed schools?

Chapman said she had a vision of shuttered CCS schools being potentially used as a space for a community partnership offering early childhood education to pre-kindergarten children. She said that expanding access to early childhood education would benefit the district and hopes the city could offer universal access to children.

"That's one of the ways that I think that we could truly be working together with our community, to support the needs in our community — as well as the needs of the district — because they go hand in hand," Chapman said.

However, if the board should choose to sell the school buildings, the district would be required to first offer those properties to charter schools .

Under Ohio law, when a district declares a property "surplus," or property a government body does not need, it must first solicit bids from high-performing community or charter schools if it has been unused for over a year. Declaring a property surplus is different than maintaining a vacant property. If the district receives a bid, it must sell to the charter school at market rate. If more than one applies, it goes to an auction.

If no "high-performing" charter schools solicit bids, all other charter, STEM or college preparatory schools will be permitted to bid on surplus schools. Only then, if there are no takers, does the property go to the public for sale.

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