结构化与问题化：基于LLM的智能体如何在诊断推理中构建学习
Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning
AI总结：
本文探讨了基于LLM的智能体如何通过结构化和问题化策略促进诊断推理学习，研究发现两种策略在不同参与度指标上各有优势。
摘要：
支持学生发展诊断推理能力是教育领域的重要挑战。初学者常面临认知偏差，如过早封闭和过度依赖启发法，难以将诊断策略迁移到新情境。基于场景的学习（SBL）结合学习分析（LA）和大语言模型（LLM）提供了一种有前景的方法，通过真实案例经验与个性化支架教学相结合。然而，不同支架方法如何影响推理过程仍缺乏深入研究。本研究引入PharmaSim Switch，一个用于药剂师培训的SBL环境，扩展了LA和LLM驱动的药剂师智能体，该智能体基于两种理论驱动的支架方法：结构化和问题化，以及学生学习轨迹。在组间实验中，63名职业学生在两种支架条件下完成了学习场景、近迁移场景和远迁移场景。结果表明，两种支架方法均有效支持诊断策略的使用。表现结果主要受场景复杂性影响，而非学生先前知识或支架方法。结构化方法与更准确的主动和互动参与相关，而问题化方法则引发了更多建设性参与。这些发现强调了在设计LA和LLM系统时结合支架方法的价值，以有效促进诊断推理。
英文摘要：
Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as premature closure and over-reliance on heuristics, and they struggle to transfer diagnostic strategies to new cases. Scenario-based learning (SBL) enhanced by Learning Analytics (LA) and large language models (LLM) offers a promising approach by combining realistic case experiences with personalized scaffolding. Yet, how different scaffolding approaches shape reasoning processes remains insufficiently explored. This study introduces PharmaSim Switch, an SBL environment for pharmacy technician training, extended with an LA- and LLM-powered pharmacist agent that implements pedagogical conversations rooted in two theory-driven scaffolding approaches: \emph{structuring} and \emph{problematizing}, as well as a student learning trajectory. In a between-groups experiment, 63 vocational students completed a learning scenario, a near-transfer scenario, and a far-transfer scenario under one of the two scaffolding conditions. Results indicate that both scaffolding approaches were effective in supporting the use of diagnostic strategies. Performance outcomes were primarily influenced by scenario complexity rather than students' prior knowledge or the scaffolding approach used. The structuring approach was associated with more accurate Active and Interactive participation, whereas problematizing elicited more Constructive engagement. These findings underscore the value of combining scaffolding approaches when designing LA- and LLM-based systems to effectively foster diagnostic reasoning.
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。
