Exploration and Reflection on the Reform of Teaching Administration Mode in Colleges for Talent Cultivation Empowered by AI

This study explores artificial intelligence (AI)-driven reform in teaching administration and talent cultivation at the University of Shanghai for Science and Technology. AI enhances management efficiency through automated workflows, supports personalized learning via adaptive systems, and enriches teaching with virtual simulation and interdisciplinary training. While AI addresses scalability and innovation challenges, issues like data privacy and teacher training require further attention. Collaborative efforts are essential to achieve sustainable, high-quality educational development.

References

Barde A, Thakur R, Patel S, et al., 2024, AI-Based Smart Education System to Enhanced the Learning of Students, 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET).

Li Y, Wang Q, Li X, et al., 2025, AI Empowers the Construction and Teaching Reform of Oil and Gas Field Chemistry Courses. International Journal of Social Science and Education Research, 8(5): 365–370.

Deep S, Athimoolam K, Enoch T, 2024, Optimizing Administrative Efficiency and Student Engagement in Education: The Impact of AI. International Journal of Current Science Research and Review, 07(10).

Yarlagadda KC, 2025, AI in Education: Personalized Learning and Intelligent Tutoring Systems. European Journal of Computer Science and Information Technology, 13(32): 15–27.

Callegari Felippe K, de Souza DC, Melo Junior HG, et al., 2024, The Use of AI in Custom Learning Environments. Revista Caderno Pedagogico, 21(5): 1–23.

Sun X, 2024, The Application of Artificial Intelligence in Education. Transactions on Computer Science and Intelligent Systems Research, 5: 953–959.

Kai Y, 2024, Paths to Explore the Integration of Education and Teaching in Science and Engineering Colleges Driven by Artificial Intelligence. College and Job, 13(3): 711–715.

Zhou QS, Liu HW, 2016, Reform of Curriculum System and Teaching Methods for Energy and Power Engineering Major. Science & Technology Vision, (14): 192.

Shairy E, 2023, Unleashing the Power and Assessing the Impact of Artificial Intelligence in Teaching and Learning. International Journal for Research in Applied Science and Engineering Technology, 11(XI): 251–254.

Xue Y, Wang Y, 2022, Artificial Intelligence for Education and Teaching. Wireless Communications and Mobile Computing, 2022: 4750018.

Hu S, 2024, The Effect of Artificial Intelligence-Assisted Personalized Learning on Student Learning Outcomes: A Meta-Analysis Based on 31 Empirical Research Papers. Science Insights Education Frontiers, 24(1): 3873–3894.

European Digital Education Hub (EDEH), 2024, Teaching with AI – Assessment, Feedback and Personalisation (Briefing report No. 7), European Commission.

Liu B, Liu JM, Yin YL, et al., 2020, Research on Virtual Simulation Experiment Assisting Physics Experiment Teaching. Physics Experimentation, 40(10): 40–46.

Zhu YK, Wen MN, Li Y, 2025, Application and Optimization of Virtual Simulation Teaching Assisted by Artificial Intelligence in Ideological and Political Courses in Universities, Proceedings of the International Conference on Artificial Intelligence and Machine Learning Research (CAIMLR 2024), Vol. 13635, 1363510.

Cui Z, Ma W, 2023, Research on the Reform of IPTTIAC of AI Empowerment, Theory and Practice of Education, 43(12): 33–36.

Fu W, Wang L, 2023, Application of Tool Empowerment in the Teaching Process of Thermal Power Plants in the Era of Artificial Intelligence. Journal of Education and Educational Research, 6(1): 195–197.

Dai L, Yu L, Chen J, 2019, Thinking and Practice of Virtual Simulation Platform for Intelligent Control in Undergraduate Teaching, 2019 International Conference on Management Innovation, Education Reform and Applied Social Science (MIERASS 2019), 162–164.

Previous article in this issue

Next article in this issue