Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908539980611584 |
|---|---|
| author | Chen, Si Molnar, Isabel R. Li, Peiyu Acunin, Adam Hua, Ting Ambrose, Alex Chawla, Nitesh V. Metoyer, Ronald |
| author_facet | Chen, Si Molnar, Isabel R. Li, Peiyu Acunin, Adam Hua, Ting Ambrose, Alex Chawla, Nitesh V. Metoyer, Ronald |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice Chen, Si Molnar, Isabel R. Li, Peiyu Acunin, Adam Hua, Ting Ambrose, Alex Chawla, Nitesh V. Metoyer, Ronald Human-Computer Interaction Artificial Intelligence 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. |
| title | Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12107 |