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.

翻译：支持学生发展诊断推理能力是教育领域的一项关键挑战。新手常面临认知偏差（如过早闭合、过度依赖启发式策略），且难以将诊断策略迁移至新病例。通过融合学习分析（LA）和大语言模型（LLM）的场景化学习（SBL），有望结合真实案例经验与个性化支架构建，但不同支架方法对推理过程的塑造作用仍未被充分探究。本研究引入PharmaSim Switch——面向药学技术人员培训的SBL环境，并集成基于LA与LLM的药剂师智能体，该智能体实施两种理论驱动的支架构建方法：\emph{结构化}与\emph{问题化}，同时追踪学生学习轨迹。在组间实验中，63名职业学生在两种支架条件下分别完成学习场景、近迁移场景与远迁移场景。结果表明：两种支架方法均能有效支持诊断策略运用；任务表现主要受场景复杂性影响，而非学生先前知识或所用支架方法。结构化方法与更准确的主动式与互动式参与相关，而问题化方法则激发更多建构性投入。这些发现强调了在设计基于LA与LLM的支架系统时，融合多种支架方法的价值，以有效促进诊断推理能力的发展。