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Education Models for the Future
Education Models for the Future
Education has evolved substantially since the days when all kids in India were sent to a playschool at the age of three and graduated to nursery, kindergarten and finally to a regimented school curriculum designed for rote academic learning rather than any personalised learning discovery process. Content and pedagogy designed for the average student tended to exclude the bored intelligent lot and the weak learners. Indian readers may recall that it is only after the movie Taare Zameen Par hit Indian screens that challenges like dyslexia became known to the average Indian parent.3 In this context, there is an enormous added value that AI can provide in the planning and delivery of personalised education.
In the classroom, AI can provide valuable assistance in grading. Ganesh remembers that in his tenure at Harvard Business School, the professor disappeared after every class to individually grade students on class participation. When learning moved to a synchronous online mechanism called HBX with seventy students arrayed on a screen in front of the professor, it made it possible for an intelligent AI eye to observe each student and potentially give assessment recommendations to the professor. This assistance provided by AI to grade finalisation automatically frees up more time for course preparation and instruction.
Classroom organisation, too, can be transformed through algorithms that can ensure that similar profiles are matched and learning opportunities are created from peers with different yet interesting experiences. While most of us may have grown up with a ‘best friend’ who stuck with us through school and sometimes college, progressive schools like the United Nations International School in New York experiment with changing classmates at regular intervals and also matching kids of different nationalities to give the children a truly global experience.4 In packed classrooms like the ones we have in municipal schools in India, such assortments may be difficult to achieve, but AI algorithms can help in identifying pairing possibilities based on class performance and a ‘big brother is watching you’ approach to enhance classroom behaviour!
Classroom content generation and test paper generation is a very easy application of technology and may not require AI, but generating additional content or easier explanations with otherwise abled children in the classroom may make the learning experience more homogenous. AI can also assist in predictive and prescriptive analytics based on classroom behaviour and test result patterns. The real value of an AI-assisted approach through ten to twelve years of a student’s progress through school education would be to have an assistive friend who monitors, provides nudges when necessary and makes the entire learning process more personalised, adaptive, pleasurable and productive!
It could be argued, and indeed we, the authors, believe, that extensive AI usage might quell the desire in children to seek out the truth on any subject on their own and reduce the pleasure of search and discovery that is so much a part of the learning process. However, the history of technology has shown that every advancement and aid to the capabilities of the human brain results in new explorations and the use of the brain in newer avenues of pursuit. The learning process has to be more expansive.
The test papers have to probe intelligence rather than memory, and new pathways to learning will need to emerge to tease the imagination of the young learner.
练习题
According to the passage, what was a major limitation of the traditional rote-based school model?
Which use of AI in education is described as directly freeing teachers' time for course preparation and instruction?
Which statements are supported by the passage about AI and classroom organisation?
The passage says that classroom content generation and test paper generation always require advanced AI systems.
The passage suggests that AI can generate easier explanations to support otherwise abled children and make learning more inclusive.
AI can assist in predictive and prescriptive analytics based on classroom behaviour and test result ___.
How does the passage describe the ideal long-term role of AI across a student's school journey?
According to the passage, one possible danger of extensive AI use in learning is that it may reduce children's desire for independent search and discovery.
Which statement best captures the passage's balanced view of technology in learning?
Why does the passage argue that future test papers should probe intelligence rather than memory? Answer using ideas from the text and connect it to the broader impact of AI on learning or work.
Which option best combines the passage's view of AI in education with the broader concern about AI's effects on human learning and interaction?
Which statements are supported when we compare AI-assisted education with the broader pattern of AI changing work? Select all that apply.
Because AI can support predictive and prescriptive analytics in education, the passage suggests that all forms of AI-based monitoring in classrooms are unambiguously positive and raise no broader concerns about surveillance.
The passage argues that future test papers should probe ___ rather than memory, which also reflects the broader hope that technology can open new avenues for imagination, innovation, and creativity.
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