Exploration of Artificial Intelligence Practice Course Reform under the Background of Large Models
Keywords:
Big model technology, Artificial intelligence education, Reform of practical coursesAbstract
With the rapid development of artificial intelligence technology, the rise of large-scale models has brought new opportunities and challenges to artificial intelligence education. This article deeply analyzes the current situation and existing problems of artificial intelligence practical courses, especially the needs and challenges faced by the application of large models in practical teaching. On this basis, a series of curriculum reform strategies were discussed. One is to update the course content and integrate theoretical teaching and practical operations related to big models, so that students can master cutting-edge knowledge and skills; The second is to strengthen interdisciplinary cooperation, encourage students to apply artificial intelligence technology in different fields, and broaden their knowledge horizons and application abilities; The third is to build an open learning platform, providing rich online resources and tools to support students' independent learning and innovative practice. Through the analysis of reform practice cases, this article highlights the necessity of integrating industry and education in artificial intelligence practice courses, and looks forward to future development directions. The results of this research can provide valuable insights and reflections for the curriculum design and teaching reform of higher education institutions in the field of artificial intelligence.
References
Ge, J. (2024). Technologies in Peace and Conflict: Unraveling the Politics of Deployment. Review International Journal of Research Publication and Reviews (IJRPR), 5(5), 5966-5971.
Wang, H. (2024). The Restriction and Balance of Prior Rights on the Right of Enterprise Name.
Wu, W. (2025). Fault Detection and Prediction in Models: Optimizing Resource Usage in Cloud Infrastructure.
Zhao, S., Lu, Y., Gong, C., & Xu, Q. (2025). Research on Labour Market Efficiency Evaluation Under Impact of Media News Based on Machine Learning and DMP Model.
Chen, K., Zhao, S., Jiang, G., He, Y., & Li, H. (2025). The Green Innovation Effect of the Digital Economy. International Review of Economics & Finance, 103970.
Meng, Q., Wang, J., He, J., & Zhao, S. (2025). Research on Green Warehousing Logistics Site Selection Optimization and Path Planning based on Deep Learning.
Wang, Y., Yang, T., Liang, H., & Deng, M. (2022). Cell atlas of the immune microenvironment in gastrointestinal cancers: Dendritic cells and beyond. Frontiers in Immunology, 13, 1007823.
Li, X., Wang, J., & Zhang, L. (2025). Gamifying Data Visualization in Smart Cities: Fostering Citizen Engagement in Urban Monitoring. Authorea Preprints.
