Student Data Paradox And Curious Case Of Single Student-tutor Model: Regressive Side Effects Of Training Llms For Personalized Learning

Abstract

The pursuit of personalized education has led to the integration of Large Language Models (LLMs) in developing intelligent tutoring systems. To better understand and adapt to individual student needs, including their misconceptions, LLMs need to be trained on extensive datasets of student-tutor dialogues. Our research uncovers a fundamental challenge in this approach: the ``Student Data Paradox.''