By Shikhar Sachan
From time to time, a new technology emerges, claiming to be so groundbreaking that it will change everything. Such technologies often find themselves as solutions in search of problems to solve. A decade back, big data was the buzzword; a few years ago, it was the blockchain and cryptocurrency; and now, it’s GenAI making waves. Though GenAI offers demonstrable, immediate results when compared to other seemingly groundbreaking technologies, the path to developing category-creation products remains a challenging endeavor.
One such idea of a category creation product being floated is that of an AI Tutor. Every child will have an AI Tutor who is infinitely patient, infinitely compassionate, infinitely knowledgeable, and infinitely helpful. It will be by their side at every step of their journey. While it’s important to not dismiss what might be possible, replacing teachers seems like an idea as remote as replacing one’s life partner, true love, or therapist.
If you have ever prepared for competitive exams, then you’d know that clearing these exams requires you to operate in beast mode. Have a strategy, break it down into a practical timetable, achieve your targets, and finally become competent to be in the race. This process has a lot of inefficiencies that GenAI can address and these can then be extrapolated to other forms of learning.
Students attempt hundreds of tests to familiarise themselves with the exam pattern, develop exam temperament, and know their relative standing among peers. More often than not, these aren’t effective as students don’t spend enough time assessing them. Effective assessment goes beyond tallying the right or wrong answers. It involves understanding the underlying reasons for errors. Was it a conceptual misunderstanding, lack of last-minute revision, or merely over-attempting difficult questions that one is required to pass? This comprehensive analysis is a must for targeted improvement. Unfortunately, this process requires significant time and effort, which students may lack or find mentally draining. Generative AI can do the heavy lifting here. Think of a system that tells you the reason why you got a question wrong and then suggests a short clip to revise that particular concept and attempt ten similar questions for better understanding.
When it comes to subjective papers like essays, humanities, etc. an AI evaluator grades answers based on exam-specific best practices rather than generic knowledge. This is particularly relevant in the context of board exams, and government entrance exams where answers are expected to follow a specific format and stick to core books. AI evaluators can mimic the best human evaluation at scale and point out precise feedback. It ensures consistency and fairness in grading, providing valuable feedback to improve the quality of answers.
GenAI reduces manual efforts by enabling advanced queries on standard books and previous year’s exam questions. It can analyze the frequency of questions on specific subtopics within a subject, determine which topics hold greater importance, and create sample questions that closely resemble those asked in previous exams.
While these might look like 3 distinct use-cases, they change how students prepare, ensuring the best utilization of their time and increasing their chances of success.
The author is co-founder of Habitat and an angel investor.
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