| 摘要: | 在AI與教育深度融合的趨勢下，現行學習管理系統（LMS）多著重於知識面的學習支持，卻普遍忽略學生在情意層面，如時間管理、學習動機與面對挫折的支持需求。為回應此挑戰，本研究設計並實作一套結合「未來自我」與「專家外衣」理論之AI數位學習教練系統。該系統整合劇本式提示設計、提示工程技術與檢索增強生成（Retrieval-Augmented Generation, RAG）機制，提供具備情境脈絡與長期學習歷程的個人化教練回饋，以強化學生的學習投入與自我調節能力。
本研究採準實驗設計，招募101位大學生修習日式餐旅實務課程，並隨機分為實驗組與對照組。兩組皆使用相同LMS平台進行學習，惟實驗組額外使用本研究設計之AI學習教練模組。透過前後測評量、問卷與系統紀錄分析，研究結果顯示：實驗組學生在學習成效、時間管理、恆毅力與成長型思維等情意面向均顯著優於對照組，並展現出更高的參與度與持續性。
本研究提出之「雙重身份共振模型」，結合「現在的專業角色」與「未來的專業自我」的雙重激勵，透過AI教練角色設計實踐於LMS中，不僅提升學生的學習動機與管理能力，更提供教育現場一套可行且具擴充性的AI輔助教學解方，具有理論創新與實務應用之價值。
;With the deep integration of artificial intelligence (AI) and education, current learning management systems (LMS) primarily focus on supporting cognitive learning but often overlook students’ affective needs, such as time management, learning motivation, and coping with setbacks. To address this gap, this study designs and implements an AI-based digital learning coach system that integrates the theories of Future Self and Mantle of the Expert (MoE). The system incorporates script-based prompt design, prompt engineering techniques, and a Retrieval-Augmented Generation (RAG) mechanism to provide personalized coaching feedback embedded within contextual and longitudinal learning trajectories, thereby enhancing students’ learning engagement and self-regulation skills.
A quasi-experimental design was adopted, involving 101 undergraduate students enrolled in a Japanese hospitality practice course, who were randomly assigned to either the experimental or control group. Both groups used the same LMS platform; however, the experimental group had access to the AI learning coach module developed in this study. Data were collected through pre- and post-tests, questionnaires, and system log analysis. The results indicate that students in the experimental group significantly outperformed those in the control group in learning outcomes, time management, grit, and growth mindset, while also demonstrating higher levels of engagement and persistence.
This study proposes the Dual Identity Resonance Model (DIRM), which combines the motivation derived from students’ “current professional roles” and their “future professional selves.” By embedding this dual-identity framework into the LMS via the AI coach design, the system not only enhances students’ learning motivation and self-management abilities but also provides a scalable and practical AI-assisted teaching solution. The findings contribute both theoretical innovation and practical implications for the integration of AI into education. |
