AI in Education

How AI is Used in Education – Potential Use Cases

Automated Curriculum Creation and Scheduling

Description: Scheduling is an essential administrative task in education. It helps students and teachers understand where they should be and when considering all the restrictions. With manual compilation, it could take months. Now, with modern software, it is solved faster, but problems of unrecorded preferences and flexibility remain. The challenge of curriculum design is to plan the correct order of courses, lesson planning, and the outcomes that students and teachers are expected to achieve.

Influence on business: Integrating machine learning algorithms allows scheduling classes in a few seconds and checking current learner’s and teacher’s calendars. Such systems help avoid double-booking, simplifies teacher replacements, and assign students by skill, not according to their schedule. This is especially true given the COVID restrictions and the need to limit live contacts between students. AI-based curriculum design provides flexibility to learning paths and detects gaps. Some advanced systems can even recommend useful materials, saving time and costs. 

Benefits: Reduced time, avoidance of conflicts in schedule, flexibility, and ease of management

Complexity: 2 (less than average)

Data needed: information about subjects, skills required, expected outcomes, teacher and student preferences and goals, regularly updated progress, and feedback.

Examples: Century Tech –  customized system for creating teaching and learning plans, personalized lessons recommendation; Nagarro –  an AI-based scheduling tool; Thinkster Math –  integrates one-on-one teaching and math curriculum, Vertex Intelligence –  scheduling with COVID restrictions.

Tracking and Analyzing Students’ Behavior

Description: Tracking student progress is fundamental to effective teaching. Trends in student behavior can state how well learning is aligned with their goals. Also, it shows which topics are the most important and which are hard to understand. Identifying emotions in the classroom can help detect engagement and correct the lecture part that causes negative emotions.

Influence on business: Machine learning algorithms allow monitoring and analyzing student performance. Moreover, based on the data, you can even model it. Identifying the factors affecting performance helps understand the system’s weaknesses and optimize them. Another challenge that monitoring can solve is student retention. 

Benefits: Increased student retention and results, course quality improvement.

Complexity: Tracking and analysis – 2 (below average), class mood detection and prediction – 3 (average).

Data needed: Student actions over time. For predictions – structured data with factors that can influence learning. 

Examples: Nagarro – a complex AI-based tool with the class-mood tracker. Kidaptive predicts future performance.

Automated Assessment and Evaluation

Description: AI can help not only in more effective learning but in teaching as well. One of the crucial but time and labor-consuming teacher’s tasks is checking assignments. Evaluating essays, homework, grading exams, tests, and term papers is a considerable job to handle. Grading allows the student to understand the quality and correctness of the completed work, so the evaluation must be accurate and objective. In addition to simply evaluating, the vital point is providing feedback and plagiarism checking. 

Influence on business: Introducing machine learning into the assessment process allows you to automate monotonous and lengthy routine tasks. This can free up a significant amount of time for teachers. This allows them to pay more attention to students, focus on creative work or course improvements. It also  makes teaching more effective as teachers have more time to mentor and engage students.  Smart grading can evaluate structure, style, detect mistakes, correct them, and check for plagiarism.

Benefits: Automated evaluation systems  reduce time, costs, and stress for teachers. They also increase learning and teaching efficiency and give them the ability to put more effort into mentoring and engaging content. In some cases, an AI-based assessment can be fairer and more accurate with plagiarism detection.

Complexity: 2-4 (varies from below to above average), depends on the task and rules for assessing.

Data needed: A collection of previous students’ papers and grades, feedback on them, set of rules for assessing, knowledge database.

Examples: M-Write by the University of Michigan, which analyzes writing assignments and identifies students who need help; Gradescope by UC Berkeley – ML-assisted grading tool; IntelliMetric® – AI-based scoring for written assignments that provides individual feedback, Emma by Unicheck –  NLP-based cheating prevention assistant.

Educational Content Improvement

Description. With the active development of digitalization, outdated content formats are no longer relevant. Innovative content aims to ease and customize learning for students. Auto-summarized study guides, flashcards, practical and interactive questions are replacing classic textbooks. Intelligent content systems support many formats: video and multimedia, simulations, games, etc.  

Influence on business. Customized digital learning interfaces simplify the learning process and preparation of materials. Textbooks and guides are now digital and can be generated and summarized by AI. Text information can be transformed into easy-to-perceive visualizations and simulations. Materials are timely updated and adjusted to learning paths. AI can detect difficult or less engaging chapters for students based on their feedback.

Benefits: 24/7 access to relevant learning material, better engagement, and perceiving information.

Complexity: 3 (average).

Data needed: A collection of digital lessons, articles, books, and students’ feedback on them.

Examples: Cram101 – An AI-based system for book summarization with extraction of key points. Quizlet – provides the most relevant materials for students. Netex Learning – a complex learning platform with content delivery, media integration, assessments, and gamified experience.

Personalized and Adaptive Learning

Description: Considering each student’s peculiarities, the educational system can maximize the benefits of learning. Personalized content reckons the needs, previous learning records, preferences, style, and progress. Adaptive learning adjusts to the level of knowledge, fills in gaps, and recommends materials. The vice versa is also possible i.e. if the student already knows a specific topic, the excess content is skipped. Also, some systems ease learning for people with visual, hearing, and speech disabilities.

Influence on business: Traditional one-size-fits-all teaching is ineffective and slow and it does not show the maximum capabilities of students. Machine learning allows for this  using advanced personalization and adaptive learning in a few seconds. Personalization makes the learning experience more enjoyable, offers more intelligent content , and fills the gaps by saving time if necessary. The system provides students with  information  to which they are most likely to react well.

Benefits: Improved course pass rates, engagement, and results. Focus resources on essential tasks, filling gaps in knowledge. A better learning experience boosts ROI.

Complexity: 4 (above average)

Data needed: Students’ needs, goals, and preferences; collected data on activities; time spent; difficulty; evaluation of materials; feedback.

Examples: Course recommendation: Coursera, personalized  courses: Duolingo, Querium, Kidaptive, Cognii, adaptive learning: Alta by Knewton, test preparation: Riiid, Nuance –  a speech-recognition system for people with writing disabilities.

Immersive and Virtual Classrooms

Description: The combination of VR, AR, and AI is the future of  education. This approach is especially common for foreign language learning or virtual science labs. AI learns and responds to actions taken in this environment, while AR and VR provide immersive and gamified experiences. Using emotion detection, AI can react and guide students if they get confused.

Influence on business. By using all types of information perception, students master the knowledge  better. Learning with an AI-teacher becomes less intimidating, and the attitude towards mistakes is more manageable. Students can boost their skills by learning in immersive simulations. Also, all sessions may be available for review and repetition.

Benefits: Increased engagement, interactivity, practical experience, better interaction between teacher and student, and learning outcomes.

Complexity: 5.

Data needed: Students’ actions and reactions, instructions and possible hints from teachers, knowledge database.

Examples: IE WOWRoom –  learning space with real-world simulations, expert holograms, big data analysis, and emotion recognition system. Another example is an immersive lab at Rensselaer Polytechnic Institute for Mandarin Chinese studying.