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Reflect on today's workshop by thinking about how AI can shape the future of education. What potential do you see for AI in your classroom?

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Explore practical applications of AI in the classroom. Share any particular tool or service that caught your eye today and how you plan to integrate it into your teaching practice.

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For the last few decades, AI has been transforming ways in which we work and learn. Today, AI has even reached the ability to teach humans.

While artificial intelligence isn't here to replace teachers, AI-assisted learning methodologies have already impacted education. These models benefit teachers and students by automating mundane tasks, providing a personalized learning experience, and more.

In this article, we'll cover the following applications and benefits of artificial intelligence in education:

If you're looking to build AI solutions for your own educational use case, check out:

Or get in touch with our team to discuss your project!

And now, let's dive in.

Artificial Intelligence in Education: Overview

As the adoption of AI in education is growing at an exponential rate, so does the interest from market leaders.

The market size for AI in education was valued at US$ 1.1 Billion in 2020. According to UNESCO reports, it's expected to grow to around US$ 6 Billion by 2024-a substantial growth of 445% in only four years. Market Research Engine report expects the market size to reach US$12 Billion by 2027, another projected increase of 100% in three years.

Source: psmarketresearch

AI is also crucial to UNESCO's efforts to fulfill Goal 4 of 17 Sustainable Development Goals by 2030-" ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. " With this, the UN aims to fulfill the promise of "AI for all" in hopes that technology can help offer equal learning opportunities.

Combining technology with conventional teaching methods has several benefits. They range from intelligent tutoring systems and smart content creation for students to automating administrative tasks. Let's go through a few common applications of machine learning in education.

Learning assistance and better classroom experience

Artificial intelligence tools can help students understand complex subjects and present simple yet intuitive explanations for an enhanced learning experience.

A prime example of such a tool is the Microsoft Math application. This Optical Character Recognition ( OCR) based application recognizes mathematical equations from images and returns the solution. It also presents a detailed step-by-step solution, including explanations and interactive graphs to allow students to learn better.

Source: AI Lab Microsoft Math

While Microsoft's application focuses on just mathematics, some other AI products provide a well-rounded experience for student learning. For instance, virtual assistants created by Cognii use advanced Natural Language Processing (NLP) techniques to converse with students via chatbots. Being one step ahead of normal conversational AI, these AI-powered chatbots ask subject-related questions and provide feedback on the answers received.

AI can also enhance the classroom experience to improve the quality of education and engage students. For example, Blippar's computer vision-based technology brings immersive visual learning. Their augmented reality builder playground allows educational institutes to bring textbook material to life. This means students can experience visual spectacles like the solar system or a volcanic eruption in a 3-dimensional virtual space.

AI tools can also assist teachers in better understanding their students and improving the efficiency of classroom-related tasks. Microsoft Teams for Education offers Education Insights that use data analytics to inform educators of students' engagement, learning progress, and well-being. It also includes a vast library of digital apps, such as Reading Progress,that inform instructors of the student's progress and potential areas of improvement.

Personalization and closing knowledge gaps

Every student has different strengths, weaknesses, and learning styles. Artificial intelligence software can learn from students' study patterns and deliver personalized learning paths, aiding teachers who cannot divide their attention among all students at once.

Companies like Content Technologies and Carnegie Learning have developed smart platforms that use AI to provide personalized learning and feedback to students from pre-K to college. Their intelligent instruction design can identify knowledge gaps, redirect to new topics and create learning content catering to individual needs. As Content Technologies claim,

"Our AI systems can create with a staff of 20 what it would take a large publisher with a thousand employees to replicate. We are breaking down the cost barriers and have proven that AI can produce better content in less time at a fraction of the cost".

Improved accessibility

Some students require special attention and distinctive learning methodologies, which might make them feel isolated in a traditional classroom setting. Artificial intelligence can boost accessibility for students with hearing or visual impairments, physical disabilities, or those who don't speak the language well.

Tools like audio transcribers, included with MS Teams, can help students with hearing impairments understand lectures. Speech-to-text technology can also help in taking class notes or performing written exams. For example, Speech recognition software by Nuance claims to have a transcription speed of 160 words per minute and is beneficial for students with limited writing capabilities.

These tools bring new meaning to on-site and online education, ensuring every student has an equal opportunity to perform academically.

Outside-the-classroom tutoring

Artificial Intelligence chatbots prove to be helpful for students that require additional tutoring after school. The goal is to enable students to converse with the bots as they would with real teachers, providing a personalized, one-on-one learning experience.

School closures during the pandemic entrenched the need for AI in education, particularly for educational opportunities outside the classroom. Modern chatbots are already very efficient in their processing and can answer student questions at a response rate of 2.7 seconds. Companies like Capacity and AllHere have already developed conversational AI tools to answer student queries related to subjects and internal university matters such as assignments and exams.

A study conducted on Ghanaian students revealed that students that interacted with an educational chatbot scored 25% higher than those that did not.

Improved testing, grading, and assessment

We have talked about how educational chatbots can deliver feedback on answers and queries to improve student outcomes. The same models can be used for automated testing systems. AI-based assessment involves a combination of vision and language-based models to capture test information, understand it, and output a grade accordingly.

Vision-based grading

Computer vision-based tools such as GradeScanner use images of bubble sheets to score multiple-choice questions automatically. Other uses of computer vision for grading include using tools like MS Maths to score mathematics-related questions using images of the equations. These methods of grading can save teachers time and reduce errors.

Source: GradeScanner

Language-model-based grading

AI can understand the written text from essays or long-form answers, map it to traditional grade levels and create a score for the writing. Several educational organizations, such as Pearsons and Educational Testing Service, use AI language models to help score essays. Other uses of AI in education grading include plagiarism detection by services like Turnitin.

Automation of administrative tasks

77% of teachers feel they cannot do their best work because of a lack of time and resources. Grading assignments for a single class require teachers to carefully go through each submission which can take up to several hours. This takes away time from the teachers that they could otherwise use to work with the students.

AI graders are efficient enough to slash grading times by 90% and have cut the overall grading process time by 50%.

For example, artificial intelligence can provide universities with smart student enrolment methodologies. Most institutes review each enrollment application manually, filter out suitable candidates and approve scholarships or financial aid. AI predictive systems can help target potential candidates and automate tasks of application review. A private, mid-sized university was reported to have increased its enrolment yield by 15% by adopting Aible's AI system.

Besides enrolment, institutes and states still have to deal with thousands of student-related documents, such as grade sheets, applications, and performance reviews. For instance, the state of Nebraska reported spending approximately 655,000 staff hours per year to collect data reports from every school in the territory. The introduction of AI for task automation digitizes the document-handling process. This creates a network of education-related files and reports in a big data architecture, which can be accessed by any authorized institute and further used for processing.

Document processing using AI has been around for several years and automates the entire information processing cycle. V7's document processing engine delivers an accuracy of 99% for identifying key text fields and automates end-to-end workflows for data handling and document storage.

Final thoughts

AI in education delivers an enhanced experience for both students and teachers. It offers several benefits, including improved accessibility, tutoring, automated grading and administration, and streamlined workflows. These provide students with high-quality education and relieve teachers of fatigue.

Many institutes around the world already leverage AI to automate processes and deliver efficient education. However, certain standards would have to be met for wider adoption. AI models are prone to errors, and this can be damaging to students learning. Furthermore, ethical guidelines must be formed to ensure that models do not display biases while teaching and grading and that the educational material is not harmful to any community.

💡 Read next:

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AI In Education: 5 Practical Applications

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For the last few decades, AI has been transforming ways in which we work and learn. Today, AI has even reached the ability to teach humans. While artificial intelligence isn't here to replace teachers, AI-assisted learning methodologies have already impacted education. These models benefit teachers and students by automating mundane tasks, providing a personalized learning experience, and more.

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How do you envision incorporating AI technologies into your teaching practice after attending this workshop?

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Today, many priorities for improvements to teaching and learning are unmet. Educators seek technology-enhanced approaches addressing these priorities that would be safe, effective, and scalable. Like all of us, educators use AI-powered services in their everyday lives, such as voice assistants in their homes; tools that can correct grammar, complete sentences, and write essays; and automated trip planning on their phones. As a result, educators see opportunities to use AI-powered capabilities like speech recognition to increase the support available to students with disabilities, multilingual learners, and others who could benefit from greater adaptivity and personalization in digital tools for learning. They are further exploring how AI can enable writing or improving lessons, as well as their process for finding, choosing, and adapting material for use in their lessons.

Educators are also aware of new risks. Useful, powerful functionality can also be accompanied with new data privacy and security risks. Educators recognize that AI can automatically produce output that is inappropriate or wrong. They are wary that the associations or automations created by AI may amplify unwanted biases. They have noted new ways in which students may represent others' work as their own. They are well-aware of "teachable moments" and pedagogical strategies that a human teacher can address but are undetected or misunderstood by AI models. They worry whether recommendations suggested by an algorithm would be fair. Educators' concerns are manifold.

Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise as a result of AI being integrated in educational technology.

NEW!! Report on AI and the Future of Teaching & Learning

Key Insights

AI enables new forms of interaction. Students and teachers can speak, gesture, sketch, and use other natural human modes of communication to interact with a computational resource and each other. AI can generate human-like responses, as well. These new forms of action may provide supports to students with disabilities.

AI can help educators address variability in student learning. With AI, designers can anticipate and address the long tail of variations in how students can successfully learn-whereas traditional curricular resources were designed to teach to the middle or most common learning pathways. For example, AI-enabled educational technology may be deployed to adapt to each student's English language abilities with greater support for the range of skills and needs among English learners.

AI supports powerful forms of adaptivity. Conventional technologies adapt based upon the correctness of student answers. AI enables adapting to a student's learning process as it unfolds step-by-step, not simply providing feedback on right or wrong answers. Specific adaptations may enable students to continue strong progress in a curriculum by working with their strengths and working around obstacles.

AI can enhance feedback loops. AI can increase the quality and quantity of feedback provided to students and teachers, as well as suggesting resources to advance their teaching and learning.

AI can support educators. Educators can be involved in designing AI-enabled tools to make their jobs better and to enable them to better engage and support their students.

Recommendations: Call to Action for Education Leaders

Recommendation #1: Emphasize Humans in the Loop

We reject the notion of AI as replacing teachers. Teachers and other people must be "in the loop" whenever AI is applied in order to notice patterns and automate educational processes. We call upon all constituents to adopt Humans-in-the-Loop as a key criteria.

Recommendation #2: Align AI Models to a Shared Vision for Education

We call upon educational decision makers, researchers, and evaluators to determine the quality of an educational technology based not only on outcomes, but also based on the degree to which the models at the heart of the AI tools and systems align to a shared vision for teaching and learning. The figure to the right describes the important qualities of AI models for education leaders to consider.

Recommendation #3: Design Using Modern Learning Principles

Achieving effective systems requires more than processing "big data"-it requires more than data science. Applications of AI must be based on established, modern learning principles, the wisdom of educational practitioners, and should leverage the expertise in the educational assessment community around detecting bias and improving fairness. Going forward, we also must seek to create AI systems that are culturally responsive and culturally sustaining, leveraging the growth of published techniques for doing so. Further, most early AI systems had few specific supports for students with disabilities and English learners and we must ensure that AI-enabled learning resources are intentionally inclusive of these students.

Recommendation #4: Prioritize Strengthening Trust

Technology can only help us to achieve educational objectives when we trust it. Yet, we learned through a series of public listening sessions that distrust of educational technology and AI is commonplace. Because trust develops as people meet and relate to each other, we call for a focus on building trust and establishing criteria for trustworthiness of emerging educational technologies within the associations, convenings, and professional organizations that bring educators, innovators, researchers, and policymakers together.

Recommendation #5: Inform and Involve Educators

We call on educational leaders to prioritize informing and involving educational constituents so they are prepared to investigate how and when AI fits specific teaching and learning needs, and what risks may rise. Now is the time to show the respect and value we hold for educators by informing and involving them in every step of the process of designing, developing, testing, improving, adopting, and managing AI-enabled educational technology. This includes involving educators in reviewing existing AI-enabled systems, tools, and data use in schools, designing new applications of AI based on teacher input, carrying out pilot evaluations of proposed new instructional tools, collaborating with developers to increase the trustworthiness of the deployed system, and raising issues about risks and unexpected consequences as the system is implemented.

Recommendation #6: Focus R&D on Addressing Context and Enhancing Trust and Safety

Research that focuses on how AI-enabled systems can adapt to context (diversity among learners, variability in instructional approaches, differences in educational settings) is essential to answering the question "Do specific applications of AI work in education, and if so, for whom and under what conditions?" We call upon researchers and their funders to prioritize investigations of how AI can address the long tail of learning variability and to seek advances in how AI can incorporate contextual considerations when detecting patterns and recommending options to students and teachers. Further, researchers should accelerate their attention to how to enhance trust and safety in AI-enabled systems for education.

Recommendation #7: Develop Education-Specific Guidelines and Guardrails

Data privacy regulation already covers educational technology; further, data security is already a priority of school educational technology leaders. Modifications and enhancements to the status quo will be required to address the new capabilities alongside the risks of AI. We call for involvement of all perspectives in the ecosystem to define a set of guidelines (such as voluntary disclosures and technology procurement checklists) and guardrails (such as enhancements to existing regulations or additional requirements) so that we can achieve safe and effective AI for education.

Listening Sessions

The U.S. Department of Education's Office of Educational Technology, with support from Digital Promise, held listening sessions about Artificial Intelligence (AI). We connected with all constituents involved in making decisions about technology in education, including but not limited to teachers, educational leaders, students, parents, technologists, researchers, and policy makers.

The goal of these listening sessions were to gather input, ideas, and engage in conversations that will help the Department shape a vision for AI policy that is inclusive of cutting-edge research and practices while also informed by the opportunities and risks.

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For a print-out and summary of the Artificial Intelligence Initiative, please review our AI One-pager

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Artificial Intelligence - Office of Educational Technology

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OET is working to develop policies and supports focused on the effective, safe, and fair use of AI-enabled educational technology.

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How can AI-tools boost student engagement and personalized learning? Share examples or ideas that you found compelling during our workshop.

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How can AI help make education fair for everyone and include all students?

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Would you recommend this workshop to other educators interested in integrating AI in education? Why or why not?