用AI助教分步引导或质疑，提升药剂师新手诊断能力。
Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning
- 设计两种AI助教：按步骤引导（结构化）或提出问题启发（问题化）
- 两种方法都有效，复杂场景下表现更差，与先验知识无关
- 结构化促主动参与，问题化促深度思考，适合不同学习目标
支持学生发展诊断推理能力是教育中的关键挑战。新手常受认知偏见影响，如过早定论和依赖经验法则，并难以将诊断策略迁移到新情境。基于情景的学习（SBL）结合学习分析（LA）与大语言模型（LLM），通过真实案例体验与个性化支架，提供了有前景的解决方案。然而，不同支架方式如何影响推理过程仍不明确。本研究引入PharmaSim Switch——一个用于药剂师技师培训的SBL环境，扩展了由LA和LLM驱动的药师代理，该代理基于两种理论驱动的支架策略：结构化与问题化，以及学生学习轨迹。在组间实验中，63名职业学生在一种支架条件下完成学习情景、近迁移情景和远迁移情景。结果表明，两种支架方式均能有效支持诊断策略的使用。性能主要受情景复杂性影响，而非学生先验知识或支架方式。结构化方式与更高程度的主动与互动参与相关，而问题化方式则引发更多建设性参与。这些发现强调，在设计基于LA和LLM的系统时，结合多种支架方式对有效培养诊断推理能力至关重要。
原文摘要 · Abstract (English)
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.
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