According to a study by the National Assessment and Accreditation Council, 90% of the colleges in India are of poor quality. The system itself encourages a rat race among students, which only makes them concerned about marks and mugging up facts instead of learning the concepts which stress them out. 20% of students between the ages of 4 and 16 show signs of mental disorders, according to a report by The Economic Times.
Teachers use outdated techniques to impart knowledge which today’s students find monotonous. The 2017 ASER survey states that 34% of boys and 19% of girls dropping out of school, list “lack of interest” as the reason. Moreover, teachers aren’t even trained properly. 86% of teachers who took the central government’s annual ‘Teacher Eligibility Test’ in 2016, failed.
How AI Can Help
Personalised Learning: AI enables personalised learning for students. A learning app, for example, can incorporate an algorithm to gather data about a student’s learning pace, strengths, weaknesses and tailor educational content to match their unique needs. Learning progress can be tracked by gathering data on various performance metrics and real-time feedback can be provided as well.
This gamification of learning can rekindle engagement among students and it’s exactly what a popular app called the Byju’s app aims to do with Artificial Intelligence. The same technology can also be used to train teachers.
Decreasing Teacher Workload: AI can be used to automate teachers’ tasks so they can focus on areas like personality development and providing human interaction. For example, an ML-powered homework and tests grading software that assesses students’ work. Some schools in China have already implemented this, where the AI system uses an evolving knowledge base to apply general logic to grade students’ work.
Identifying Learning Disabilities: Since humans can’t effectively identify learning disabilities, AI systems can be applied that use advanced tests and huge data sets to identify learning disabilities and suggest teaching methods accordingly. An example of this is the perceptron based learning disability detector (PLEDDOR). It’s an artificial neural network model that identifies three learning disabilities – dyslexia, dysgraphia and dyscalculia, using curriculum-based tests conducted by special educators.
Course Improvement: Gaps in schools’ courses can confuse students. Using machine learning, this problem can be solved. A good example of where this is achieved is Coursera, which is a huge open online course provider. When many students submit the wrong answer to an assignment, the system alerts the teacher and gives students a customised message offering hints to the correct answer. Augmenting School
Administration: A school-wide, data-driven AI infrastructure is an indispensable tool for school administrators which provides countless insights into everyday operations.
Student monitoring systems can be used to identify students at risk of dropping out or failing their exams and take measures accordingly, enhancing overall retention. “Virtual counselling” can help students identify and choose their future career based on an analysis of their interests, skills, performance metrics and so on.
AI can magnify efficiency in “daily operations”, by modelling complex data. For example, an ML algorithm can be used to minimise food wastage in the canteen by analysing the amount of food required based on historical consumption data on different days and ordering raw materials accordingly.
“Facial recognition” on CCTV cameras can identify bullying or other abuses, and the perpetrators can be dealt with in real-time, bolstering student security. Automating administrative activities like coordination and control, data-driven complex decision making about costs and revenues and so on, can free up management’s time so it is better utilised at other tasks that a human can do better.