Against global challenges like climate change and biodiversity loss, sustainable development is the core orientation of engineering education transformation. Cultivating talents with interdisciplinary perspectives, systemic thinking and AI literacy is crucial for implementing the UN 2030 Sustainable Development Agenda. However, AI education focuses on seniors or graduates, with freshmen’s use of AI acting as “cognitive partners” for knowledge construction and complex problem-solving understudied, constraining AI’s potential in fostering early systemic thinking. We present a novel teaching practice integrating generative AI into an “AI-Environmental System Analysis” module, with Sousa chinensis habitat conservation as the case. Using a design-based research paradigm, we evaluated 24 student groups via system analysis briefs, AI usage reflections and course assessment data. Results show that the module effectively guided students to establish preliminary system analysis frameworks, with over 70% of groups identifying complex interactions among environmental factors. Students’ AI applications ranged from information retrieval to scenario simulation, initially forming systemic thinking and responsible AI literacy for sustainable development. This study provides a replicable paradigm for integrating AI and sustainable development education, clarifies the key role of structured instructional scaffolding, and enriches sustainable development-oriented engineering education pathways.
Lin et al. (Tue,) studied this question.
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