1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
2. Undergraduate School, Wuhan University of Technology, Wuhan 430070, China
| Abstract: | This study explores the role of educational large language models in the digital transformation of engineering courses. A four-in-one transformation framework is constructed, covering teaching content, teaching modes, practical components, and teaching evaluation. The core course, Facility Planning in the Industrial Engineering program, is selected for instructional experimentation. In terms of teaching content, a knowledge graph is generated through a large language model, and industry-oriented frontier cases are dynamically produced. In teaching modes, the instructional workflow before, during, and after class is reshaped through an AI-supported dual-teacher collaboration mechanism. In the practical component, a facility-planning system integrating the DeepSeek model and the Systematic Layout Planning (SLP) method is developed. Natural-language interaction enables the full workflow from data analysis to solution optimization, supporting intelligent design training that follows the sequence of input, analysis, generation, and optimization. In teaching evaluation, a comprehensive system is adopted by combining process-based data with multi-dimensional capability assessment. Empirical results from one semester indicate significant improvements in students’ engagement, abilities to solve complex engineering problems, and systems thinking. Meanwhile, the teacher’s role gradually shifts from knowledge transmitter to learning facilitator and context designer. The proposed framework and its implementation path may serve as a reference for digital reform in other engineering courses. Future work extends to domain-specific model development, cross-course application, and the construction of shared resource ecosystems. |
| Keywords: | Educational Large Language Models; Engineering Courses; Digital Transformation; Facility Planning; Instructional Reform |
| DOI: | 10.57237/j.edu.2026.01.001 |
| 1. | Hubei Provincial Teaching Reform Research Projects: Research on Experimental Teaching Mode Innovation and Evaluation System Reform Based on "Data and Intelligence-Driven, Virtual-Real Synergy" (2024113) |
| 2. | Wuhan University of Technology Teaching Reform Research Project: Research on the Digital Construction and Reform Exploration of the "Facility Planning" Course Based on the Large Educational Model (W2024103) |
| 3. | Research on the Reconstruction and Path Design of the Cultivation System for Top-notch Innovative Talents in the Outstanding Experimental Class of Mechanical Engineering (W2025098) |
| [1] | Chiu, T. K. F. The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney [J]. Interactive Learning Environments, 2024, 32(10): 6187-6203. |
| [2] | Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix [J]. Sustainability, 2023, 15(15): 11524. |
| [3] | Escobar-Castillejos, D., Sigüenza-Noriega, I., Noguez, J., Escobar-Castillejos, D., & Berumen-Glinz, L. A. Enhancing methods engineering education with a digital platform: usability and educational impact on industrial engineering students [J]. Frontiers in Education, 2024, 9: 1438882. |
| [4] | Bewersdorff, A., Hartmann, C., Hornberger, M., Seßler, K., Bannert, M., Kasneci, E., Kasneci, G., Zhai, X., & Nerdel, C. Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education [J]. Learning and Individual Differences, 2025, 118: 102601. |
| [5] | Topali, P., Cobos, R., Agirre-Uribarren, U., Martínez-Monés, A., & Villagrá-Sobrino, S. ‘Instructor in action’: Co-design and evaluation of human-centred LA-informed feedback in MOOCs [J]. Journal of Computer Assisted Learning, 2024, 40(6): 3149-3166. |
| [6] | Sertu, B. B., Chakraborty, S., & Galatro, D. Assessing the online learning experience in the undergraduate engineering education domain: Challenges and opportunities [J]. Technology, Knowledge and Learning, 2025. https://doi.org/10.1007/s10758-025-09917-8 |
| [7] | Jiang, L. Factors influencing EFL teachers’ implementation of SPOC-based blended learning in higher vocational colleges in China: A study based on grounded theory [J]. Interactive Learning Environments, 2024, 32(3): 859-878. |
| [8] | Doulougeri, K., Vermunt, J. D., Bombaerts, G., & Bots, M. Challenge-based learning implementation in engineering education: A systematic literature review [J]. Journal of Engineering Education, 2024, 113(4): 1076-1106. |
| [9] | Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. Practical and ethical challenges of large language models in education: A systematic scoping review [J]. British Journal of Educational Technology, 2024, 55(1): 90-112. |
| [10] | Yang, Q., & Liang, C. A second-classroom personalized learning path recommendation system based on large language model technology [J]. Applied Sciences, 2025, 15(14): 7655. |
| [11] | Guo, K., Zhang, E. D., Li, D., & Yu, S. Using AI-supported peer review to enhance feedback literacy: An investigation of students' revision of feedback on peers' essays [J]. British Journal of Educational Technology, 2025, 56(4): 1612-1639. |
| [12] | Kang, S., Chen, B., Yoo, S., & Lou, J.-G. Explainable automated debugging via large language model-driven scientific debugging [J]. Empirical Software Engineering, 2024, 30(2): 45. |
| [13] | Pereira, A. F., & Mello, R. F. A systematic literature review on large language models applications in computer programming teaching evaluation process [J]. IEEE Access, 2025, 13: 113449-113460. |
| [14] | Tsai, M.-L., Ong, C. W., & Chen, C.-L. Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT [J]. Education for Chemical Engineers, 2023, 44: 71-95. |
| [15] | Liang, X., Wang, Z., & Liu, J. Towards a self-cognitive complex product design system: A fine-grained multi-modal feature recognition and semantic understanding approach using large language models in mechanical engineering [J]. Advanced Engineering Informatics, 2025, 65: 103265. |
| [16] | Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. ChatGPT for good? On opportunities and challenges of large language models for education [J]. Learning and Individual Differences, 2023, 103: 102274. |
| [17] | Jeon, J., & Lee, S. Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT [J]. Education and Information Technologies, 2023, 28(12): 15873-15892. |
| [18] | Oprea, S. V., & Bâra, A. Transforming education with large language models: Trends, themes, and untapped potential [J]. IEEE Access, 2025, 13: 87292-87312. |
| [19] | Hu, Y., Xu, Y., & Wu, B. A dual-pathway model of teacher-AI collaboration based on the job demands-resources theory [J]. Education and Information Technologies, 2025, 30(11): 15125-15146. |
| [20] | Liu, X., Li, W., Pan, C., & Zhou, S. AI-DTCEM: A capability ecology framework for dual-qualified teacher team construction [J]. Applied Sciences, 2025, 15(21): 11392. |
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