Volume 3,Issue 7
Exploring Transformation of AI-Empowered Teaching Models in Higher Education: A Case Study of New Energy Science and Engineering
Against the backdrop of the global digital transformation in education and the deep integration of artificial intelligence (AI) technology, AI-empowered higher education has become a significant trend in international educational reform. Based on the demands of global energy transition and industrial transformation, this paper focuses on the interdisciplinary field of New Energy Science and Engineering, systematically reviewing the international research context, current developments, and practical challenges of AI-empowered teaching. Taking the “Hydrogen Energy and Fuel Cell Technology” course as an example, a teaching reform path centered on “value integration, environment reconfiguration, and role transformation” is proposed. By promoting systematic innovation in teaching philosophy, learning environment, and the roles of teachers and students, this study explores the construction of a human-centered, human-machine collaborative educational paradigm, providing theoretical reference and practical cases for the digital development and teaching model transformation of global higher education.
[1] Tarrago J, Galvez D, Jalca J, et al., 2025, Artificial Intelligence and Soft Skills in Civil Engineering Education: A Latin American Curriculum Gap with Global Implications. Research in Globalization, 11(2025): 100307.
[2] Díaz B, Nussbaum M, 2024, Artificial Intelligence for Teaching and Learning in Schools: The Need for Pedagogical Intelligence. Computers & Education, 217(2024): 105071.
[3] Fütterer T, Goldberg P, Bühler B, et al., 2025, Artificial Intelligence in Classroom Management: A Systematic Review on Educational Purposes, Technical Implementations, and Ethical Considerations. Computers and Education: Artificial Intelligence, 9(2025): 100483.
[4] Lin Q, Luo Z, Du-Ikonen L, et al., 2025, Generative Artificial Intelligence: Pioneering a New Paradigm for Research and Education in Smart Energy Systems. Energy and AI, 22(2025): 100610.
[5] Skowronek M, Gilberti R, Petro M, et al., 2022, Inclusive STEAM Education in Diverse Disciplines of Sustainable Energy and AI. Energy and AI, 7(2022): 100124.
[6] Tan X, Cheng G, Ling M, 2025, Artificial Intelligence in Teaching and Teacher Professional Development: A Systematic Review. Computers and Education: Artificial Intelligence, 8(2025): 100355.
[7] Zhuang M, Long S, Martin F, 2025, The Affordances of Artificial Intelligence (AI) and Ethical Considerations across the Instruction Cycle: A Systematic Review of AI in Online Higher Education. The Internet and Higher Education, 67(2025): 101039.
[8] Cao X, Huang Z, Li M, et al., 2026, Teachers’ AI-TPACK as a Tangible Outcome in the Digital Transformation of Education: A Machine Learning-Based Multilevel Approach. Teaching and Teacher Education, 169(2026): 105270.
[9] Okoye K, Campos E, Das A, et al., 2025, Impact of Digitalized-Education upon Sustainable Education and Practice: A Systematic Review and Meta-Analysis of Literature based on Pre-Intra-and-Post Pandemic and Rural Education Development. Sustainable Futures, 10(2025): 100851.
[10] Zhang Y, Xu C, Zhou Y, 2025, Optimal Operation of Wind-Solar-Storage-Hydrogen System Considering Multi-Scale Forecasting of Source-Load. Energy Conversion and Management, 344(2025): 120296.
[11] Zhao Y, Zhao Y, Cao S, et al., 2025, Capacity Configuration and Control Optimization of Off-Grid Wind Solar Hydrogen Storage System. Energy, 324(2025): 136002.
[12] Chang W, Sun J, 2026, Empowering Bilingual Teachers with Dynamic GenAI: Adaptive Design and Implementation of Multimodal Instructional Strategies. Computers & Education, 241(2026): 105490.
[13] Xiang H, Li X, Dou E, 2025, The Application of Basic-Element Learning Method in Industrial Engineering Courses. Procedia Computer Science, 266(2025): 753–761.