Evidence-governed artificial intelligence learning in Applied Optics
Abstract
Generative artificial intelligence can broaden design alternatives in engineering education, but it can also displace the reasoning and evidential judgment students are expected to develop. This study reports a practice-oriented design-based redesign of a 56-hour Applied Optics course. The course integrates a knowledge graph, project-based learning, professional optical-design software, virtual or physical experimentation, and generative artificial intelligence through an evidence-governed generate-verify-account mechanism. Students first form an initial problem representation, then use artificial intelligence to expand candidate solutions, verify key claims through disciplinary evidence such as optical theory, Ansys Zemax OpticStudio (Zemax) simulation, experiment, or traceable technical sources, and finally defend an accountable engineering decision. Implementation evidence was drawn from two successive cohorts, aggregated platform records, course-outcome attainment, and project artifacts. The final-examination failure rate decreased from 51.4% to 36.7%, while attainment of outcomes related to problem solving, research, and modern tools increased modestly; overall attainment remained approximately 0.81. Because cohorts, examinations, assessment structures, and the degree of artificial intelligence integration were not equivalent, these differences are interpreted descriptively rather than causally. The contribution is an operational framework for using artificial intelligence to expand the candidate space without lowering the evidence threshold for engineering conclusions or transferring final responsibility away from students.
Document Type: Original article
Cited as: Jin, J., Evidence-governed artificial intelligence learning in Applied Optics. Higher Engineering Education Transformation, 2026, 1(2): 24-32. https://doi.org/10.46690/heet.2026.02.02
DOI:
https://doi.org/10.46690/heet.2026.02.02Keywords:
Applied Optics, generative AI , human-AI collaboration , project-based learning , engineering problem , evidence governanceReferences
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