Exploring Teaching Methods for Conceptual Analysis and Problem‑Solving Seminars in Engineering Thermodynamics with Generative AI
Due to abstract concepts, rigorous logic, and complex engineering contexts, Engineering Thermodynamics has long been plagued by teaching difficulties, including obstacles in conceptual analysis and low‑efficiency problem‑solving seminars. Through an in‑depth analysis of its existing teaching dilemmas, this paper puts forward reform strategies such as multi‑representation generation and concept comparison, cognitive‑conflict induction and misconception correction, Socratic guidance and step‑by‑step prompting, as well as contextualized exercise generation and dynamic feedback. It gives full play to the auxiliary teaching functions of generative AI, so as to provide references for the teaching reform of Engineering Thermodynamics and professional talent cultivation.
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