用对话式AI帮助教师提升教学，分苏格拉底式和叙事式两种策略。
Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice
- 设计双模式AI：苏格拉底式提问促反思，叙事式建议助实践。
- 41位教师参与，新手和乐观者更爱提问式，资深谨慎者偏爱建议式。
- 揭示教师经验与对AI态度影响互动偏好，指导AI教育工具设计。
大型语言模型（LLMs）通常直接给出答案，但正被广泛用于学习辅助。研究教师使用情况至关重要，因其在教学与引导教育中的人工智能应用方面起关键作用。我们设计并评估了专为教学目的开发的TeaPT，该模型通过两种对话方式支持教师专业发展：苏格拉底式（以引导性问题促进反思）和叙事式（提供详尽建议以扩展外化认知）。在包含41名高等教育教师的混合方法研究中，苏格拉底版本引发更高参与度，而叙事版本更受青睐于可操作指导。子群体分析显示，经验较少、对AI持乐观态度的教师更偏好苏格拉底式；经验较多、对AI持谨慎态度的教师则更倾向叙事式。本研究为教学型LLM的设计提供启示，表明自适应对话策略可支持不同背景的教师，并凸显教师对AI的态度与经验如何影响交互与学习效果。
原文摘要 · Abstract (English)
Large language models (LLMs) typically generate direct answers, yet they are increasingly used as learning tools. Studying instructors' usage is critical, given their role in teaching and guiding AI adoption in education. We designed and evaluated TeaPT, an LLM for pedagogical purposes that supports instructors' professional development through two conversational approaches: a Socratic approach that uses guided questioning to foster reflection, and a Narrative approach that offers elaborated suggestions to extend externalized cognition. In a mixed-method study with 41 higher-education instructors, the Socratic version elicited greater engagement, while the Narrative version was preferred for actionable guidance. Subgroup analyses further revealed that less-experienced, AI-optimistic instructors favored the Socratic version, whereas more-experienced, AI-cautious instructors preferred the Narrative version. We contribute design implications for LLMs for pedagogical purposes, showing how adaptive conversational approaches can support instructors with varied profiles while highlighting how AI attitudes and experience shape interaction and learning.
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