The integration of generative artificial intelligence (AI) tools in engineering education presents both opportunities and challenges for educators seeking to preserve academic standards while enhancing learning outcomes. This study addresses the research question: How can verification and validation principles be applied as a structured pedagogical framework to support the strategic integration of AI into engineering courses, improving learning while maintaining academic rigor? Using a qualitative use-case approach based on instructor reflections from three use cases spanning graduate and undergraduate engineering courses, we document pedagogical innovations that transform AI from an academic integrity concern into an explicit learning tool that scaffolds student development within the Zone of Proximal Development. Drawing on these verification and validation principles, along with the Definition, Abstraction, and Implementation framework, we examine AI integration from both instructor-centered (top-down) and student-centered (bottom-up) perspectives. The latter is represented through instructor observation of student work and engagement rather than direct student-reported data. Our findings suggest that strategic AI integration, when grounded in structured pedagogical frameworks and applied across both perspectives, shows promise for enhancing learning experiences and instructor teaching practices without compromising academic standards. These findings point toward directions for future empirical validation.
http://orcid.org/0000-0001-5645-4683
Purdue University – West Lafayette (College of Engineering)
[biography]
http://orcid.org/0000-0002-8455-8540
Purdue University at West Lafayette (COE)
[biography]
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