Modelling student knowledge is a key challenge when leveraging AI in education, with major implications for personalised learning. The Knowledge Tracing (KT) task aims to predict how students will respond to educational questions in learning environments, based on their prior interactions. Existing KT models typically use response correctness along with metadata like skill tags and timestamps, often overlooking the question text, which is an important source of pedagogical insight. This omission poses a lost opportunity while limiting predictive performance. We propose Next Token Knowledge Tracing (NTKT), a novel approach that reframes KT as a next-token prediction task using pretrained Large Language Models (LLMs). NTKT represents both student histories and question content as sequences of text, allowing LLMs to learn patterns in both behaviour and language. Our series of experiments significantly improves performance over state-of-the-art neural KT models and generalises much better to cold-start questions and users. These findings highlight the importance of question content in KT and demonstrate the benefits of leveraging pretrained representations of LLMs to model student learning more effectively.

翻译：学生知识建模是教育领域应用人工智能的关键挑战，对个性化学习具有重要影响。知识追踪（KT）任务旨在根据学生先前的交互记录，预测其在学习环境中对教育问题的回答情况。现有KT模型通常仅使用答题正确率及技能标签、时间戳等元数据，往往忽略问题文本这一重要的教学洞察来源。这种遗漏不仅错失了提升机会，也限制了预测性能。我们提出下一标记知识追踪（NTKT），这是一种将KT重构为使用预训练大语言模型（LLMs）的下一标记预测任务的新方法。NTKT将学生历史记录和问题内容均表示为文本序列，使LLMs能够同时学习行为模式和语言模式。我们的系列实验显著超越了最先进的神经KT模型性能，并在冷启动问题和用户场景中展现出更强的泛化能力。这些发现凸显了问题内容在KT中的重要性，并证明了利用LLMs预训练表征能更有效地建模学生学习过程。