From Blended Intelligence to “Dual-Teacher Classrooms”: Building and Applying AIGC-Empowered Collaborative Learning Paradigms
- DOI
- 10.2991/978-2-38476-611-6_36How to use a DOI?
- Keywords
- AIGC; dual-teacher classroom; collaborative learning; higher education
- Abstract
This study proposes an AIGC-supported “dual-teacher classroom” collaborative learning paradigm for higher education. The paradigm redefines the dual-teacher model as human-AI collaboration between human teachers and AIGC tools, which integrates dynamic content generation, personalized learning support, and real-time assessment and feedback into three stages: preparation, collaboration, and summary. Teaching practices in several higher education courses show that this model can improve students’ knowledge mastery, learning engagement, and critical thinking skills. The study provides a practical framework for applying AIGC to collaborative learning while emphasizing the irreplaceable role of human teachers in guidance, emotional support, and pedagogical judgment.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 3.0 International License (http://creativecommons.org/licenses/by-nc/3.0/), 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.
Cite this article
TY - CONF AU - Wenyang Wang AU - Yuxin Wu AU - Fang Peng PY - 2026 DA - 2026/09/07 TI - From Blended Intelligence to “Dual-Teacher Classrooms”: Building and Applying AIGC-Empowered Collaborative Learning Paradigms BT - Proceedings of the 2026 12th International Conference on Digital Humanities and Frontiers in Social Sciences (DHFSS 2026) PB - Atlantis Press SP - 322 EP - 327 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-611-6_36 DO - 10.2991/978-2-38476-611-6_36 ID - Wang2026 ER -