Deepen Students’ Learning by Leveraging Generative Artificial Intelligence
- DOI
- 10.2991/978-2-38476-475-4_39How to use a DOI?
- Keywords
- Generative artificial intelligence; Deep learning; Personalized learning
- Abstract
Generative Artificial Intelligence (AIGC) is reshaping the educational ecosystem through personalized learning path design, interdisciplinary knowledge integration, and real-time dynamic feedback. Based on the generative learning theory and constructivist educational perspective, this article systematically explores how AIGC supports students’ active construction of deep learning, the development of higher-order thinking, and the transfer to real situations. AIGC has significant advantages in personalized resource delivery, interdisciplinary project design, and the cultivation of metacognitive abilities. Its application requires integration with teacher professional development, ethical framework design, and teaching mode innovation. The future breakthrough points lie in the upgrade of multimodal interaction, the optimization of metacognitive scaffolds, and the construction of an agile governance system, providing core support for cultivating top-notch innovative talents.
- Copyright
- © 2025 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.
Cite this article
TY - CONF AU - Jingting Hu AU - Guixia Sui PY - 2025 DA - 2025/11/11 TI - Deepen Students’ Learning by Leveraging Generative Artificial Intelligence BT - Proceedings of the 2025 10th International Conference on Modern Management, Education and Social Sciences (MMET 2025) PB - Atlantis Press SP - 332 EP - 337 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-475-4_39 DO - 10.2991/978-2-38476-475-4_39 ID - Hu2025 ER -