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In intelligent tutoring systems (ITS), knowledge tracing (KT) is a fundamental requirement for effective education and data mining. The main objective of KT is to model and predict the evolving understanding level of a student on different educational tasks. Traditional KT methods, such as the factor analysis method (FAM), Bayesian KT (BKT), and deep KT (DKT) approaches, have achieved high-performance effectiveness but often fail to identify reasoning processes, diverse learning trajectories, and complex interdependence relationships between skills associated with educational questions. The existing challenges highlight the requirement for personalization and contextual adaptability for effective student modeling. Addressing these challenges is important for building an ITS that can provide personalized educational experiences and support lifelong learning for diverse students.

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Enhancing Knowledge Tracing with Large Language Models (LLMs)

  • 会议论文

  • 发表日期：

    2025年

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  • Duaa Baig,
  • Diana Nurbakova,
  • Sylvie Calabretto,
  • Baba MBaye

摘要

In intelligent tutoring systems (ITS), knowledge tracing (KT) is a fundamental requirement for effective education and data mining. The main objective of KT is to model and predict the evolving understanding level of a student on different educational tasks. Traditional KT methods, such as the factor analysis method (FAM), Bayesian KT (BKT), and deep KT (DKT) approaches, have achieved high-performance effectiveness but often fail to identify reasoning processes, diverse learning trajectories, and complex interdependence relationships between skills associated with educational questions. The existing challenges highlight the requirement for personalization and contextual adaptability for effective student modeling. Addressing these challenges is important for building an ITS that can provide personalized educational experiences and support lifelong learning for diverse students.