Knowledge Tracing (KT) is a research field that aims to estimate a student's knowledge state through learning interactions-a crucial component of Intelligent Tutoring Systems (ITSs). Despite significant advancements, no current KT models excel in both predictive accuracy and interpretability. Meanwhile, Large Language Models (LLMs), pre-trained on vast natural language datasets, have emerged as powerful tools with immense potential in various educational applications. This systematic review explores the intersections, opportunities, and challenges of combining KT models and LLMs in educational contexts. The review first investigates LLM applications in education, including their adaptability to domain-specific content and ability to support personalized learning. It then examines the development and current state of KT models, from traditional to advanced approaches, aiming to uncover potential challenges that LLMs could mitigate. The core of this review focuses on integrating LLMs with KT, exploring three primary functions: addressing general concerns in KT fields, overcoming specific KT model limitations, and performing as KT models themselves. Our findings reveal that LLMs can be customized for specific educational tasks through tailor-making techniques such as in-context learning and agent-based approaches, effectively managing complex and unbalanced educational data. These models can enhance existing KT models' performance and solve cold-start problems by generating relevant features from question data. However, both current models depend heavily on structured, limited datasets, missing opportunities to use diverse educational data that could offer deeper insights into individual learners and support various educational settings.

翻译：知识追踪（Knowledge Tracing，KT）是一个旨在通过学习交互来评估学生知识状态的研究领域，是智能导学系统（Intelligent Tutoring Systems，ITSs）的关键组成部分。尽管取得了显著进展，但目前尚无任何KT模型能在预测准确性和可解释性两方面均表现卓越。与此同时，在大量自然语言数据集上预训练的大型语言模型（Large Language Models，LLMs）已成为强大工具，在各种教育应用中展现出巨大潜力。本系统综述探讨了KT模型与LLMs在教育背景下的交叉点、机遇与挑战。综述首先探究了LLMs在教育中的应用，包括其对领域特定内容的适应性以及支持个性化学习的能力。随后，综述考察了KT模型从传统方法到先进方法的发展历程与现状，旨在揭示LLMs可能缓解的潜在挑战。本综述的核心聚焦于LLMs与KT的整合，探索了三大主要功能：应对KT领域的普遍关切、克服特定KT模型的局限性，以及作为KT模型本身执行任务。我们的研究发现，LLMs可通过情境学习、基于智能体的方法等定制化技术，针对特定教育任务进行定制，有效处理复杂且不均衡的教育数据。这些模型能够通过从问题数据生成相关特征，提升现有KT模型的性能并解决冷启动问题。然而，当前两类模型都严重依赖于结构化、有限的数据集，错失了利用多样化教育数据的机会，而这些数据本可为个体学习者提供更深入的洞见并支持多样化的教育场景。