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Lessons from Implementing AI in Flipped Engineering Education for a Fundamentals of Electricity Course
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2026 ASABE Annual International Meeting 2600638.(doi:10.13031/aim.202600638)Authors: Bo Liu
Keywords: artificial intelligence, AI literacy, flipped classroom, engineering education, self-regulated learning, cognitive apprenticeship, MASTER Framework
Abstract. Generative artificial intelligence gives engineering students immediate access to explanations, worked examples, and troubleshooting support, but access alone does not guarantee learning. This teaching-method study evaluates am implementation of an AI-enhanced flipped format in BRAE 216-Fundamentals of Electricity at Cal Poly. The Fall 2025 redesign was introduced in response to student requests from the prior traditional offering for more examples, solution checks, and learning support outside class. Student-level means from the AI-enhanced cohort (Fall 2025, n=27) were compared with a traditional lecture-centered cohort (Fall 2024, n=33). The quantitative analysis was limited to weekly problem-solving work, labs, and exams. Weekly problem-solving work changed format across years: Fall 2024 used outside-class homework, whereas Fall 2025 used in-class assessments. Fall 2025 scores were lower on assignments (79.41% versus 90.42%, p<.001) and labs (89.14% versus 95.16%, p<.001), while exams showed no statistically significant difference (67.33% versus 70.74%, p=.462). A simple sentiment analysis of open-ended course evaluations showed a slightly larger share of negative comments in 2025, especially in improvement and additional-comment responses.
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