AI in Education
The use of Artificial Intelligence in education is revolutionizing the industry. AI has transformed both its goal and method. The use of AI for education made it possible to teach students according to their aptitude. As a result, the focus of education shifts from test scores to quality improvement. Data science for education provides personalized solutions for students, which take into account their situations and needs.
Moreover, Artificial Intelligence in education uses Big Data to facilitate decision-making. Thus, precision teaching becomes possible while learning speed and flexibility improve. On top of this, AI for education also boosts the flow of information within the educational industry. In addition to this, resources can be shared across different regions more easily.
AI TRANSFORMS EDUCATION INDUSTRY
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.
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.
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How to Start Combining AI and Education?
Artificial Intelligence in education is getting increasingly important for this sector. Nonetheless, its full potential is yet to be fulfilled. More and more companies are reaping the benefits of AI for education. If your business plans to pair Artificial Intelligence and education, you should start right now.
Here are several important principles to remember:
- Data is the starting point. The type, quality, and quantity of available data are of prime importance for utilizing machine learning in education. After accessing it, you will get a general idea of your options.
Nonetheless, little or no data doesn’t mean you should end your project before even starting with it. You can still succeed in using data science for education. Study available public data sources or search for partners with relevant data. Design your own strategy to collect and store data necessary to utilize machine learning in education.
- Keep your ambitions in check. Your first project of using AI in education shouldn’t be too complicated. Small projects get completed faster. You will be able to build on your first success over time. The use of machine learning in education can be scaled. New capabilities will benefit your institution, and the value will increase.
- Brace yourself for failures. Employing data science for education is a challenging task. Be prepared to devote a lot of time and resources to it. Failure is a part of the way to implementing innovations. It is vital for organizational learning. Don’t be afraid to fail early, and you’ll save time and money.
AI for Education: Summary
The use of artificial intelligence in education changes the industry dramatically. The most obvious difference is customization. AI applications in education provide customized teaching, which allows students to learn more efficiently. Therefore, the application of Artificial Intelligence in education attracts the attention of industry players.
Many tech companies devote their resources to utilizing data science in education industry. For instance, New Oriental promotes the use of Artificial Intelligence in education with the help of independent development, external cooperation, and investments. Tencent creates an education branch to strengthen its ties with education organizations. The company helps individuals, schools, and organizations to use AI in education field.
AI technology is quickly changing various learning processes. The penetration of AI in education sector gives birth to some revolutionary products. Intelligent products gather data generated during different learning processes, such as teaching, learning, assessing, practicing, and testing. New tools using AI in education industry utilize voice and image recognition to produce customized solutions and effective feedback.
The spread of machine learning in education sector results in more intelligent adaptive education products. The use of cloud computing and deep learning produces customized homework, courses, testing, and scientific assessment. New educational systems can record learning data, track learning status, manage progress, keeping parents and schools in the loop. With the help of Big Data, they challenge traditional teaching systems, providing targeted teaching, quantifying, and visualizing learning progress. Therefore, the quality of teaching and learning soars.
How is AI changing the education industry?
The use of machine learning in education industry helps to develop personalized teaching and learning programs. The result is improved flexibility and learning speed.
How can AI help students?
New intelligent educational systems take into account the needs and aptitude of students. The educational process is modified accordingly to maximize the quality of learning.
What is the future of AI in education?
AI-powered education will continue to challenge traditional educational systems. With the spread of new instruments, the quality of teaching and learning will improve.