iLLuMinaTE：一个利用社会科学解释理论向可操作的学生表现反馈的LLM-XAI框架
iLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback
AI总结：
提出iLLuMinaTE框架，利用社会科学解释理论生成可操作的学生反馈，经多模型多方法评估，89.52%用户偏好，具泛化潜力。
摘要：
教育领域可解释人工智能（XAI）的最新进展凸显了一个关键挑战：确保最先进人工智能模型的解释对于教育者和学生等非技术用户是可理解的。为此，我们引入了iLLuMinaTE，一个受Miller认知解释模型启发的零样本、提示链LLM-XAI流水线。iLLuMinaTE旨在为在线课程的学生提供基于理论、可操作的反馈。iLLuMinaTE导航三个主要阶段——因果连接、解释选择和解释呈现——并借鉴八种社会科学理论（如异常条件、Pearl的解释模型、必要性和稳健性选择、对比解释）的变体。我们广泛评估了从三个LLM（GPT-4o、Gemma2-9B、Llama3-70B）中提取的21,915条iLLuMinaTE自然语言解释，这些解释使用三种不同的底层XAI方法（LIME、Counterfactuals、MC-LIME），覆盖来自三个不同在线课程的学生。我们的评估包括对解释与社会科学理论的一致性分析、解释的可理解性分析，以及一项包含114名大学生和一种新颖可操作性模拟的真实世界用户偏好研究。我们发现，学生在89.52%的情况下更偏好iLLuMinaTE的解释而非传统解释器。我们的工作为在教育中有效传达混合XAI驱动的见解提供了一个稳健、即用型框架，并具有对其他以人为中心的领域的显著泛化潜力。
英文摘要：
Recent advances in eXplainable AI (XAI) for education have highlighted a critical challenge: ensuring that explanations for state-of-the-art AI models are understandable for non-technical users such as educators and students. In response, we introduce iLLuMinaTE, a zero-shot, chain-of-prompts LLM-XAI pipeline inspired by Miller's cognitive model of explanation. iLLuMinaTE is designed to deliver theory-driven, actionable feedback to students in online courses. iLLuMinaTE navigates three main stages - causal connection, explanation selection, and explanation presentation - with variations drawing from eight social science theories (e.g. Abnormal Conditions, Pearl's Model of Explanation, Necessity and Robustness Selection, Contrastive Explanation). We extensively evaluate 21,915 natural language explanations of iLLuMinaTE extracted from three LLMs (GPT-4o, Gemma2-9B, Llama3-70B), with three different underlying XAI methods (LIME, Counterfactuals, MC-LIME), across students from three diverse online courses. Our evaluation involves analyses of explanation alignment to the social science theory, understandability of the explanation, and a real-world user preference study with 114 university students containing a novel actionability simulation. We find that students prefer iLLuMinaTE explanations over traditional explainers 89.52% of the time. Our work provides a robust, ready-to-use framework for effectively communicating hybrid XAI-driven insights in education, with significant generalization potential for other human-centric fields.
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。
