Construction and Practical Verification of an Intelligent Teaching Model Based on Generative AI

DOI

10.2991/978-94-6239-630-2_15How to use a DOI?

Keywords

AI-assisted teaching; Large Language Model; Learning Analytics; Selective Flipped Classroom; Tri-Method Integrated Teaching Approach; Empirical Research

Abstract

Addressing the core challenge in current educational practice—the difficulty of balancing scalable instruction with personalized cultivation—this study proposes an innovative teaching paradigm deeply integrated with Generative Artificial Intelligence. This framework is structured around a “Selective Flipped Classroom” and a tri-method pedagogical integration of “PBL+TBL+CCBL.” It establishes a closed-loop teaching system characterized by “data-driven decision-making, intelligent resource generation, and dynamic methodological synergy,” powered by learning analytics as the diagnostic core and large language models as the generative core. The paper systematically elaborates the theoretical framework, design principles, and a comprehensive implementation pathway covering the entire “pre-class, in-class, and post-class” process. Empirical validation through a rigorous parallel-class controlled experiment demonstrates that the experimental class employing this model achieved significant improvement in overall academic performance (with an average score increase of 4.6 points and a 100% pass rate), particularly in preview effectiveness and knowledge transfer capability (p < 0.01). These results confirm the model’s effectiveness in promoting precision teaching, process optimization, and educational equity. This research provides a comprehensive solution with both theoretical innovation and practical feasibility for constructing a learner-centered intelligent teaching paradigm in the era of “digital-intelligent integration.”

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hongmei Pei
AU  - Meili Zhang
AU  - Wei Xing
PY  - 2026
DA  - 2026/04/23
TI  - Construction and Practical Verification of an Intelligent Teaching Model Based on Generative AI
BT  - Proceedings of the 2025 International Conference on Educational Technology and Management Information Systems (ETMIS 2025)
PB  - Atlantis Press
SP  - 146
EP  - 155
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-630-2_15
DO  - 10.2991/978-94-6239-630-2_15
ID  - Pei2026
ER  -