On the Theoretical Construction of Smart Teaching Mode of Management Curriculum Driven by Knowledge Graph and AI Teaching Assistant
- DOI
- 10.2991/978-94-6463-750-2_37How to use a DOI?
- Keywords
- Knowledge graph; AI teaching assistant; management courses; smart teaching mode; digital transformation of education
- Abstract
This study focuses on the smart teaching mode of management courses driven by knowledge graph and AI teaching assistant. On the basis of analyzing the theoretical inevitability of digital transformation of education, it explores the adaptation model of technology-empowered education, builds the theoretical framework of intelligent teaching mode, expounds its role in innovation and reconstruction of management education, and has an outlook into the future. The research shows that this smart teaching mode can effectively cope with the dilemma of management teaching, promote the evolution of management knowledge system, breakthrough of management ability training paradigm and curriculum ecological reconstruction, and provide theoretical support and practical guidance for the digital transformation of higher education.
- 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 - Qianqian Tang PY - 2025 DA - 2025/06/15 TI - On the Theoretical Construction of Smart Teaching Mode of Management Curriculum Driven by Knowledge Graph and AI Teaching Assistant BT - Proceedings of the 2025 4th International Conference on Educational Innovation and Multimedia Technology (EIMT 2025) PB - Atlantis Press SP - 376 EP - 384 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-750-2_37 DO - 10.2991/978-94-6463-750-2_37 ID - Tang2025 ER -