發展一套基於建構主義與認知負荷理論之NotebookLM教學框架：「功能—歷程—策略」

A NotebookLM Instructional Framework Based on Constructivism and Cognitive Load Theory: Function–Process–Strategy Integration

本研究以建構主義與認知負荷理論為基礎，採文件分析與概念模型建構，提出Google NotebookLM於中高等教育之發展「技術功能—學習歷程—教學策略」三階整合架構。研究結果顯示，NotebookLM的來源追蹤可降低外在負荷並促進批判思考；筆記釘選與多模態有助知識建構；亦能輔助差異化教學與形成性評量。相較於TPACK與SAMR模型，本架構更聚焦學習者認知歷程，並與GPT教學模型具高度契合性。然而，實務應用仍需考量內容正確性、學術倫理與數位落差等挑戰。本研究之貢獻為建構以認知理論為基礎之NotebookLM教學應用框架，填補生成式AI工具教學整合理論缺口，並提供教育工作者提供理論基礎與實踐方向。未來研究應進行實證驗證並探索跨領域應用模式。

This study is grounded in constructivism and cognitive load theory and employs document analysis and conceptual model construction to propose a three-layer integrative framework—“technological functions–learning processes–instructional strategies”—for the application of Google NotebookLM in secondary and higher education. The findings indicate that NotebookLM’s source-tracking feature can reduce extraneous cognitive load and promote critical thinking. In addition, functions such as note pinning and multimodal interaction support knowledge construction, while also facilitating differentiated instruction and formative assessment. Compared with the TPACK and SAMR models, the proposed framework places greater emphasis on learners’cognitive processes and demonstrates a high degree of alignment with GPT-based instructional models. However, practical implementation still requires careful consideration of challenges related to content accuracy, academic integrity, and the digital divide. The primary contribution of this study is the development of a cognitively grounded instructional framework for the pedagogical use of NotebookLM, which helps address the theoretical gap in integrating generative AI tools into teaching and learning. Furthermore, it provides educators with both theoretical foundations and practical directions for instructional design. Future research should conduct empirical validation and explore interdisciplinary application models.