可解释的难度感知对话知识追踪
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
AI总结：
本文提出基于LLM的可解释难度感知对话知识追踪框架，通过建模学生能力与问题难度，提升对话场景下的个性化支持能力。
摘要：
近年来，大型语言模型（LLM）的进步推动了基于对话的智能辅导系统的发展。为了使这些系统能够提供个性化支持，必须在每一步评估学生表现，从而推动对话中的知识追踪（KT）。然而，现有的基于对话的KT方法往往忽视问题难度建模，依赖LLM的不可解释的潜在表示，阻碍了准确和可解释的预测。本文提出一个基于LLM的可解释难度感知对话KT框架，明确建模学生的能力和导师提出任务的难度。该框架结合原始文本问题和下一个导师提出任务，以估计学生知识状态和即将到来的步骤难度。此外，它整合项目反应理论，将LLM的输出映射到学生能力和问题难度参数，从而实现基于学习认知理论的可解释学生表现预测。我们在两个辅导-学生对话数据集上评估了该框架。定量和定性结果均显示，我们的框架优于现有KT基线，同时生成与认知理论一致的可解释输出。
英文摘要：
Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring systems to provide personalized support, it is essential to assess student performance at each turn, motivating knowledge tracing (KT) in dialogue settings. However, existing dialogue-based KT approaches often ignore question difficulty modeling and rely on opaque latent representations from LLMs, hindering accurate and interpretable prediction. In this work, we propose an interpretable difficulty-aware conversational KT framework built upon LLMs, which explicitly models students' abilities and the difficulty of tutor-posed tasks at each turn. The framework incorporates the original textual question and the next tutor-posed task to estimate the student's knowledge state and the difficulty of the upcoming turn. Furthermore, it integrates Item Response Theory to map LLM's outputs into student ability and question difficulty parameters, enabling interpretable prediction of student performance grounded in cognitive theories of learning. We evaluate the framework on two tutor-student dialogue datasets. Both quantitative and qualitative results show that our framework outperforms existing KT baselines, meanwhile generating interpretable outputs consistent with cognitive theory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
