CIKT：一种基于大语言模型的协作迭代知识追踪框架
CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models
- School of Computer Science and Technology, East China Normal University（计算机科学与技术学院，东华大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出CIKT框架，利用大语言模型通过分析师与预测器的协同迭代优化，提升知识追踪的预测准确性、可解释性和可扩展性。
AI中文摘要：
知识追踪（KT）旨在随时间对学生学习状态进行建模，并预测其未来表现。然而，传统知识追踪方法在可解释性、可扩展性以及复杂知识依赖的有效建模方面常常面临挑战。虽然大语言模型（LLMs）为知识追踪提供了新的途径，但其直接应用往往难以生成结构化、可解释的学生表征，并且缺乏持续、任务特定的优化机制。为解决这些问题，我们提出了协作迭代知识追踪（CIKT）框架，该框架利用大语言模型来提升预测准确性和可解释性。CIKT采用双组件架构：分析师（Analyst）从学生历史回答中生成动态、可解释的用户画像，预测器（Predictor）利用这些画像预测未来表现。CIKT的核心是一个协同优化循环。在该循环中，分析师基于预测器（以生成的画像为条件）的预测准确性进行迭代优化，随后预测器使用这些增强后的画像进行重新训练。在多个教育数据集上的评估表明，CIKT在预测准确性方面取得了显著提升，通过动态更新的用户画像提供了更强的可解释性，并展现出更好的可扩展性。我们的工作为推进知识追踪系统提供了一个稳健且可解释的解决方案，有效弥合了预测性能与模型透明度之间的差距。
英文摘要：
Knowledge Tracing (KT) aims to model a student's learning state over time and predict their future performance. However, traditional KT methods often face challenges in explainability, scalability, and effective modeling of complex knowledge dependencies. While Large Language Models (LLMs) present new avenues for KT, their direct application often struggles with generating structured, explainable student representations and lacks mechanisms for continuous, task-specific refinement. To address these gaps, we propose Collaborative Iterative Knowledge Tracing (CIKT), a framework that harnesses LLMs to enhance both prediction accuracy and explainability. CIKT employs a dual-component architecture: an Analyst generates dynamic, explainable user profiles from student historical responses, and a Predictor utilizes these profiles to forecast future performance. The core of CIKT is a synergistic optimization loop. In this loop, the Analyst is iteratively refined based on the predictive accuracy of the Predictor, which conditions on the generated profiles, and the Predictor is subsequently retrained using these enhanced profiles. Evaluated on multiple educational datasets, CIKT demonstrates significant improvements in prediction accuracy, offers enhanced explainability through its dynamically updated user profiles, and exhibits improved scalability. Our work presents a robust and explainable solution for advancing knowledge tracing systems, effectively bridging the gap between predictive performance and model transparency.
