标题： 通过与人工智能聊天机器人的互动分析本科生的物理问题解决过程 显示英文标题

标题： Analyzing Undergraduate Problem-Solving in Physics Through Interaction With an AI Chatbot

摘要： 为大规模物理问题解决提供个性化的支架仍然是一项教学挑战。 我们研究了（1）学生对苏格拉底人工智能（AI）聊天机器人的问题解决技能和自信心影响的感知，以及（2）辅导过程中学生问题的具体性如何与表现相关。 我们在一所中西部公立大学的一门大班次入门力学课程中部署了一个定制的苏格拉底AI聊天机器人，记录了150名一年级STEM专业学生的完整对话记录。 互动后的调查结果显示，知识技能的中位评分为4.0/5，整体效果为3.4/5。 对话分析显示，问题的具体性从第一轮的约10-15%上升到最后一轮的100%，具体性与自我报告的预期课程成绩呈正相关（皮尔逊r = 0.43）。 这些发现表明，基于人工智能的苏格拉底对话不仅促进了类似专家的推理，还为物理学教育研究生成了细粒度的分析数据，建立了一个可扩展的双重用途工具，用于教学和学习分析。 显示英文摘要

摘要： Providing individualized scaffolding for physics problem solving at scale remains an instructional challenge. We investigate (1) students' perceptions of a Socratic Artificial Intelligence (AI) chatbot's impact on problem-solving skills and confidence and (2) how the specificity of students' questions during tutoring relates to performance. We deployed a custom Socratic AI chatbot in a large-enrollment introductory mechanics course at a Midwestern public university, logging full dialogue transcripts from 150 first-year STEM majors. Post-interaction surveys revealed median ratings of 4.0/5 for knowledge-based skills and 3.4/5 for overall effectiveness. Transcript analysis showed question specificity rose from approximately 10-15% in the first turn to 100% by the final turn, and specificity correlated positively with self reported expected course grade (Pearson r = 0.43). These findings demonstrate that AI-driven Socratic dialogue not only fosters expert-like reasoning but also generates fine-grained analytics for physics education research, establishing a scalable dual-purpose tool for instruction and learning analytics.