Keywords
Engineering Thermophysics
Graduate training
Submitted : 2026-03-09
Accepted : 2026-03-24
Published : 2026-04-08
Abstract
Against the backdrop of the in-depth integration of the new round of technological revolution and industrial transformation, artificial intelligence (AI) technology has become an important driving force for innovative development in the energy and power sector, and also provides a brand-new opportunity for the reform of the training model of graduate students in Engineering Thermophysics at colleges and universities. Combining the existing problems in the training of graduate students in Engineering Thermophysics and the adaptability of AI technology in this training process, this paper systematically explores the practical paths of integrating AI technology into the entire process of graduate curriculum teaching, scientific research innovation, practical training, and quality evaluation. It aims to provide a theoretical reference for cultivating compound Engineering Thermophysics talents with solid theoretical foundations, innovative capabilities, and engineering literacy.
References
Tian BZ, Fan QH, 2025, Research on the Training Mechanism of Interdisciplinary Graduate Students Under the Background of Artificial Intelligence. Journal of Higher Education, 11(30): 162–166.
Zhao CY, Li XY, Lin PY, et al., 2025, Exploration of Graduate Training Model Based on New Engineering Information Technology. Technology Wind, (18): 48–50.
Hu SH, Gao LL, Wang XF, et al., 2025, Exploration and Application of Graduate Training in Applied Universities Under the Background of Artificial Intelligence. Journal of Applied Higher Education Research, 10(03): 71–77 + 84.
Li P, Li J, 2025, Research on the Relationship Between the Cultivation of Innovative Talents and International Scientific Research Cooperation for Graduate Students in Physics. Journal of Innovation and Entrepreneurship Education, 16(02): 125–131.
Zhao L, Li C, 2025, The Application of Generative Artificial Intelligence in the Training of Master’s Students. Journal of Tongling University, 24(02): 101–106.
Yang YZ, Yang J, 2024, Exploration of Graduate Training Methods for Engineering Majors Under the Background of Higher Engineering Education in the New Era. Education and Culture Forum, 16(06): 71–77.
Zhang W, Liu JH, Lian YS, et al., 2024, Discussion on the Reform of Classified Practical Teaching for Graduate Students Based on Virtual Simulation Platform. Research on Printing and Digital Media Technology, (03): 97–102.
Ye DD, Liao Q, Fu Q, et al., 2023, Exploration on the Reform of Training Methods and Support Systems for Graduate Students in New Energy Interdisciplinary Directions. Research in Higher Education of Engineering, (S1): 43–45 + 61.
Wang JY, Zhou T, Li XQ, 2023, Exploration on the Innovation of Graduate Teaching Management Mechanism Under the Background of “Double First-Class” Construction—Taking the School of Physical Science and Technology of Southwest Jiaotong University as an Example. Heilongjiang Education (Theory & Practice), (12): 65–67.
Chen YL, Yang J, Yan J, et al., 2022, Research on the Construction of Modern Physics Experiment Courses Based on Improving Graduate Students’ Scientific Research Capabilities. Physics and Engineering, 32(04): 64–67 + 73.
Han DJ, Huang YB, Xu T, et al., 2022, Exploration on the Improvement of Graduate Training Quality and Evaluation Mechanism Based on In-depth Integration of Information Technology Under the Background of “New Engineering.” Computer Education, (01): 42–46.
Zhong YH, Huang XB, 2022, The Value Orientation and Implementation Path of Graduate Training Goals in the Era of Artificial Intelligence. Journal of Guizhou Normal University (Social Sciences Edition), (06): 55–66.
Jiang S, Su F, Xiao B, 2022, Research on the Construction of Interdisciplinary Graduate Training Mechanism Empowered by Artificial Intelligence. Industry and Information Technology Education, (09): 36–40 + 47.
Liao XY, Zhu HF, 2022, The Reconstruction of Graduate Competence Training in the Era of Artificial Intelligence—Reflection Based on Moravec’s Paradox. Academic Degrees & Graduate Education, (08): 13–20.
Li P, Liu QJ, 2022, Research and Practice on the Training Model of Innovative and Top-Tier Talents for Graduate Students in Minor Disciplines—Taking High-Pressure Physics as an Example. Guangxi Physics, 43(02): 49–52.