Volume 8,Issue 9
Exploration and Practice of Generative‑AI‑Empowered Teaching for Systems Engineering Course
The systems engineering course features both theoretical and practical properties. Its methodological attributes lead to abstract teaching content and difficulties in student comprehension. Traditional teaching modes can hardly effectively cultivate students’ systems‑thinking ability and competence to solve complex problems. The rapid development of generative‑AI technology provides brand‑new paths for the teaching reform of systems engineering courses. Based on the disciplinary characteristics of the systems engineering course — “holism, relevance and dynamics”, combined with the technical strengths of generative AI in knowledge generation, scheme deduction and model construction, this paper puts forward a trinity teaching mode of “AI‑assisted autonomous study — classroom collaborative inquiry — system scheme verification”. Under this mode, teachers design systematic learning outlines, students complete system‑analysis tasks with the help of generative AI, and multi‑scheme comparison and critical verification are carried out in class, so as to realize the coordinated development of systems‑engineering thinking and AI‑application capabilities. Taking the teaching unit of “system evaluation methods” as an example, this paper illustrates the implementation path of this mode. Research shows that this mode helps stimulate students’ learning initiative, improve their systems‑thinking ability and human‑AI collaboration literacy, and provides references for the teaching reform of engineering‑oriented courses in the generative‑AI era.
[1] Liu C, Chen Y, Wang H, 2026, AIGC-Empowered Engineering Curriculum Design: Opportunities, Challenges and Countermeasures. Chinese University Science & Technology, 2026(6): 34–40.
[2] Ning D, Wei T, 2026, Coupling Mechanism, Risk Examination and Promotion Path of Generative-AI-Empowered College Classroom Teaching Reform. University Teaching in China, 2026(1): 77–83.
[3] Zhan Z, Kong J, Ji Y, Lan M, 2026, AI-Driven Multi-Agent Training Paradigm for Engineering-Thinking Competence — Development and Application of Bai-Gong AI Platform for Engineering-Competence Education. Research in Higher Engineering Education, 2026(2): 23–30.
[4] Gao J, Tang L, 2026, From “Human-AI Q&A” to “Human-AI Co-Thinking”: Research on Students’ Question-Raising Dilemmas and Teaching Interventions in AI-Generative Learning. University Teaching in China, 2026(1): 91–96.
[5] Guo J, Shao Z, 2026, First Principles of University-Teaching Innovation in the Artificial-Intelligence Era. China Higher Education Review, 23(1): 21–43.
[6] Mu S, Chen X, Zhou D, 2025, Analysis of Generative-AI-Empowered Teaching-Design: Demands, Methods and Development. Open Education Research, 31(1): 61–72.
[7] Su F, 2025, Risks and Governance of Generative Artificial Intelligence Promoting Higher-Education Development From the Perspective of Complex Systems. Research in Higher Engineering Education, 2025(4): 112–117.
[8] Li H, Wang W, 2025, Human-AI Debate Inquiry Method: Exploration on a Training Mode for Students’ Higher-Order-Thinking Competence Supported by Argumentative Intelligent Chatbots. E-Education Research, 2025(9): 89–97.
[9] Xu X, 2025, Generative-AI-Empowered Software-Engineering Education and Improvement of Student Competencies. Computer Education, 2025(8): 1–8.
[10] Zhu L, 2026, Generative-Artificial-Intelligence-Assisted Cultivation of Engineering Thinking: Five-Step Learning Method and Practice. Digital Teaching in Primary and Secondary Schools, 2026(4): 71–75.
[11] Zhao R, Han X, Zhang Y, et al., 2026, Practical Path and Innovation Exploration of Artificial-Intelligence-Empowered Mechanical-Oriented Courses. Equipment Manufacturing Technology, 2026(1): 119–121+125.
[12] Zhao Y, Guo M, Cao X, et al., 2026, Research on Blended-Teaching Mode for the Course “Principles and Applications of Programmable Controllers” Based on Knowledge Graphs. Heilongjiang Education (Theory & Practice), 2026(5): 54–57.
[13] Xu J, Li Y, Sun D, Ma X, Chen J, 2026, Research on Practice and Effects of Human-AI Collaborative Design Teaching Integrated With Reflection Strategies. Modern Distance Education, 2026(1): 57–67.
[14] Yin J, Meng G, Wang K, 2026, Risk-Assessment Method for Bayesian-Network-Based Smart Teaching Space Under the Background of Digital-Intelligent Education Transformation. Educational Information Technology, 2026(5): 71–76.
