学习参与助手（LEA）：智能AI辅导系统的跨课程可扩展性与课堂评估
Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System
- Faculty of Design, Informatics and Business（设计、信息学与商业学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究LEA智能AI辅导系统，首次在真实学生中进行课堂部署并测试跨课程可扩展性，发现模拟与实际有差异，基于RAGAS评估答案相关性等指标，表明编排层无需改，下游组件课程无关性待进一步研究。
AI中文摘要：
本文是在ICAART 2026会议上发表论文的扩展。之前介绍了LEA，它是一个自适应AI辅导代理，整合聊天、辅导和测验模式，结合特定课程的检索增强生成（RAG）和结构化知识组件（KC）模型。之前仅通过模拟验证了LEA在单个STEM课程（CMP511）上的效果。本文首次在真实学生（n = 8，CMP511）中进行课堂部署，并对其跨课程可扩展性进行实证测试，将系统部署在跨越两个学术层次和两个学科领域的三门课程中。研究发现模拟预测与实际情况存在差异，基于RAGAS的跨课程可扩展性评估（660个问题）表明答案相关性和上下文精度在各课程中大致稳定，忠实度随与原课程的课程距离下降。这些发现表明编排层无需修改，所有下游组件的完全课程无关性还需进一步研究。
英文摘要：
This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes. That prior work validated LEA on a single STEM course (CMP511) exclusively through simulation, using synthetic learner agents. This paper extends that work by reporting the first classroom deployment of LEA with real students (n = 8, CMP511) and the first empirical test of its cross-course scalability, deploying the system across three courses spanning two academic levels and two disciplinary domains. The study reveals a divergence from simulation predictions across modes, showing that synthetic evaluation alone cannot anticipate all aspects of real deployment. A RAGAS-based cross-course scalability evaluation (660 questions) finds Answer Relevancy and Context Precision broadly stable across courses (0.88-0.94 and 0.88-0.90 respectively), while Faithfulness declines with curriculum distance from the system's original course (0.69 to 0.50), a preliminary finding that may reflect generation logic tuned to the system's original subject rather than a scalability limitation. These findings suggest that while the orchestration layer requires no modification, full course-agnosticism of all downstream components requires further investigation.
