2026 ASEE Annual Conference & Exposition

Pre- and Post-AI Adoption: Performance in Introductory Online Engineering Courses

Presented at The Impact of AI on Engineering Education Practice

Generative AI is reshaping how students plan, draft, and refine un-proctored coursework, yet evidence on independent (student-initiated) AI use and its relationship to performance remains limited. This brief research paper focuses on two questions in online engineering courses: (1) How did performance on un-proctored activities change from a pre-AI period (2022–2023) to a post-AI period (2024–2025)? and (2) How is independent AI use related to students’ performance i.e. percentage scores and mastery rates (≥80%) on discussions and group assignments?   We disaggregate all outcomes by student demographics (age bands, gender, and available institutional categories such as ethnicity).
This study provides early empirical evidence on how independent use of generative AI is transforming student learning performance in online engineering education, offering insights into when independent AI use aligns with measurable gains in un-proctored coursework and indicators of engagement. 

Authors
  1. Julianna Gesun Orcid 16x16http://orcid.org/0000-0002-0084-951X Embry–Riddle Aeronautical University, Daytona Beach [biography]
Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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For those interested in:

  • engineering
  • engineering technology