AI 中文总结
本研究构建基于双加工理论风险框架的多智能体AI模拟课堂，用于培养未来物理教师回应学生推理的能力，发现其能显著提升诊断分数，且可揭示教师知与行的发展差距，为物理教师培养提供支持。
AI 中文摘要
对学生实际产生的推理进行实时有效回应，是物理教师最难掌握、习得最慢的技能之一，而未来教师在进入课堂前几乎没有练习机会。在这项试点研究中，我们描述了一个包含5名AI学生的模拟课堂，每名AI学生都始终表现出从物理教育研究的双加工理论（DPT）“风险”框架中提取的不同推理模式，并报告其在面向未来物理教师的大学课程中的首次应用情况。15名研究生完成了关于DPT和提问策略的教学序列，随后以交叉安排的方式，在与模拟课堂互动（针对静摩擦力问题）前后，诊断两组平行的书面 vignettes（案例）。数据集还包括个人书面假设与反思、会话日志以及简短反馈问卷。诊断分数从PRE（前测）到POST（后测）显著提升（n=11对样本，Wilcoxon检验p=0.014，r=0.79；评分范围为0-18分）。然而，在模拟互动过程中，参与者主要提出统一的引导性问题，他们所学的DPT词汇仅出现在71次教师实质性发言中的2次里，而13份POST（后测）问卷中有7份已熟练使用该词汇。我们将这种知与行的差距并非视为失败，而是每位参与者在对学生想法的回应能力发展轨迹上所处位置的快照，该模拟以转录本级别的粒度使这一轨迹变得可见。我们讨论了物理教师培养的设计、发现及意义。
英文摘要
Responding productively, in real time, to the reasoning students actually produce is among the most difficult and slowest to acquire of the skills physics teachers develop, and prospective teachers get few opportunities to practise it before entering a classroom. In this pilot study, we describe a simulated class of five AI students, each consistently enacting a distinct reasoning pattern drawn from the dual-process theory (DPT) "hazards" framework of physics education research, and report on its first use in a university course for prospective physics teachers. Fifteen graduate students worked through an instructional sequence on DPT and questioning strategies. They then diagnosed two parallel sets of written vignettes, in a crossover arrangement, before and after interacting in pairs with the simulated class on a static-friction problem. The data set is completed by individual written hypotheses and reflections, the session logs and a short feedback questionnaire. Diagnostic scores improved significantly from PRE to POST (n = 11 paired, Wilcoxon p = 0.014, r = 0.79; on a 0-18 scale). During the simulation itself, however, participants asked predominantly uniform guiding questions, and the DPT vocabulary they had been taught appeared in only 2 of 71 substantive teacher turns, while seven of thirteen POST sheets used it readily. We read this knowing-doing gap not as a failure but as a snapshot of where each participant stands on the developmental trajectory of responsiveness to student ideas, a trajectory the simulation makes visible with transcript-level granularity. We discuss design, findings and implications for physics teacher preparation.
Comments16 pages, 4 figures, 3 tables
