AI Technology-Empowered Intelligent Course Design for Engineering Economics in the BOPPPS Teaching Model
- DOI
- 10.2991/978-2-38476-523-2_25How to use a DOI?
- Keywords
- AI; BOPPPS Teaching Model; Engineering Economics; Smart Courses; Instructional Design
- Abstract
This article addresses the shortcomings of traditional engineering economics instruction in personalized learning, real-time feedback, and handling complex case studies. Based on the BOPPPS teaching model framework, it designs an intelligent course teaching solution by deeply integrating artificial intelligence (AI) technology throughout the entire process. Leveraging the Fanya AI Workbench and other AI tools, this instructional design constructs “Four AI Graphs and One Scenario”, achieving structured organization and precise delivery of teaching resources. Practical implementation demonstrates that this model significantly enhances student engagement, goal attainment, and personalized learning experiences, thereby improving teaching effectiveness. This research provides an effective pathway for engineering economics teaching reform and offers practical insights for leveraging AI technology to empower educational innovation.
- 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 - Jie Chen PY - 2025 DA - 2025/12/29 TI - AI Technology-Empowered Intelligent Course Design for Engineering Economics in the BOPPPS Teaching Model BT - Proceedings of the 5th International Conference on New Media Development and Modernised Education (NMDME 2025) PB - Atlantis Press SP - 235 EP - 241 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-523-2_25 DO - 10.2991/978-2-38476-523-2_25 ID - Chen2025 ER -