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題名 How Graduate Students Engage with a Theory-Aligned Pedagogical AI Agent for Self-Regulated Learning
作者 江玥慧
Chiang, Yueh-hui Vanessa
貢獻者 資訊系
關鍵詞 Graduate Education; Multiple Case Study; Pedagogical AI Agent; Self-Regulated Learning
日期 2026-08
上傳時間 18-Sep-2026 09:38:11 (UTC+8)
摘要 This study examines how four graduate students engaged with a pedagogical AI agent explicitly designed to scaffold self-regulated learning (SRL) in a semester-long graduate course. Grounded in Zimmerman’s three-phase SRL framework, the agent was a course-specific ChatGPT workspace whose system instruction required labeling each interaction by SRL phase and guiding students through iterative plan-implement-reflect cycles. Using a multiple case study design, data were collected through five administrations of the Motivated Strategies for Learning Questionnaire (MSLQ), an SRLIS-based open-ended questionnaire, and end-of-semester semi-structured interviews. Findings reveal four distinct patterns of AI agent engagement: Organizational Assistant, Empathetic Listener, Functional Tool, and Iterative Thinking Partner. MSLQ trajectories show that students engaging in more dialogic, iterative use demonstrated more sustained SRL growth. These findings challenge assumptions of uniform AI adoption and suggest that pedagogical AI agents should accommodate diverse learner profiles and SRL orientations.
關聯 IIAI Letters on Informatics and Interdisciplinary Research, Vol.7, pp.1-8
資料類型 article
DOI https://doi.org/10.52731/liir.v007.641
dc.contributor 資訊系
dc.creator (作者) 江玥慧
dc.creator (作者) Chiang, Yueh-hui Vanessa
dc.date (日期) 2026-08
dc.date.accessioned 18-Sep-2026 09:38:11 (UTC+8)-
dc.date.available 18-Sep-2026 09:38:11 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2026 09:38:11 (UTC+8)-
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=185468-
dc.description.abstract (摘要) This study examines how four graduate students engaged with a pedagogical AI agent explicitly designed to scaffold self-regulated learning (SRL) in a semester-long graduate course. Grounded in Zimmerman’s three-phase SRL framework, the agent was a course-specific ChatGPT workspace whose system instruction required labeling each interaction by SRL phase and guiding students through iterative plan-implement-reflect cycles. Using a multiple case study design, data were collected through five administrations of the Motivated Strategies for Learning Questionnaire (MSLQ), an SRLIS-based open-ended questionnaire, and end-of-semester semi-structured interviews. Findings reveal four distinct patterns of AI agent engagement: Organizational Assistant, Empathetic Listener, Functional Tool, and Iterative Thinking Partner. MSLQ trajectories show that students engaging in more dialogic, iterative use demonstrated more sustained SRL growth. These findings challenge assumptions of uniform AI adoption and suggest that pedagogical AI agents should accommodate diverse learner profiles and SRL orientations.
dc.format.extent 102 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) IIAI Letters on Informatics and Interdisciplinary Research, Vol.7, pp.1-8
dc.subject (關鍵詞) Graduate Education; Multiple Case Study; Pedagogical AI Agent; Self-Regulated Learning
dc.title (題名) How Graduate Students Engage with a Theory-Aligned Pedagogical AI Agent for Self-Regulated Learning
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.52731/liir.v007.641
dc.doi.uri (DOI) https://doi.org/10.52731/liir.v007.641