AI In Education: 5 Practical Applications

Here's a hand-picked list of top applications of AI in education. From learning assistance to administrative task automation, learn how AI is shaping the edtech landscape.
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8
min read  ·  
March 3, 2023
ai in education

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:

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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.

ai in education market
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.

💡 Pro tip: Check out V7’s Optical Character Recognition tool

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,

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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:

Haziqa holds an MS degree in Data Science. She has extensive experience in writing technical content for AI and SaaS companies.

“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”
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