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2026 ASEE Annual Conference & Exposition

Qualitative Analysis of Neurodivergent Students’ Experience in Engineering

Presented at CEED Technical Session 1: Student Engagement, Inclusion & Success in STEM Pathways

While students with learning differences enter college in rising numbers, they have not seen tangible improvement in their post-secondary education and career outcomes. Across engineering programs, neurodivergent students tend to be under-represented, under-performing, and under-employed, despite their engineering-friendly traits. Whilst helpful, university accommodations, such as note-taking, speech-to-text, exam-time extension, etc., do not address their deficiency in higher-level cognitive skills (e.g., planning, time management, communication), except at a handful of colleges that have special programs offering coaching and social integration for neurodivergent students.
This paper presented a thematic study based on interviews conducted on nine neurodivergent students enrolled in engineering and physics at a four-year university on the West Coast. The paper summarized the challenges they have experienced, the strengths they possessed, and how challenges and strengths may interact and evolve in different learning contexts. The results could identify gaps and inform inclusive teaching strategies, especially in areas involving higher-level cognitive skills that tend to be overlooked by existing accommodations and pedagogical practices. Reframing their education experience in both challenges and strengths could combat the stigma associated with deficit-based models. Moreover, by looking into the duality between challenges and strengths, the results of this study could help students identify the conditions that turn their challenges into strengths and vice versa. Such self-awareness skill is fundamental to developing self-efficacy, leveraging one’s strengths, and seeking the help that one needs via self-advocacy.
Key words: neurodiversity, challenge, strength, engineering, self-awareness, executive function

Authors

Yiyi Wang San Francisco State University [biography]

Yiyi Wang is an associate professor of civil engineering at San Francisco State University. In addition to engineering education, her research also focuses on the nexus between mapping, information technology, and transportation and has published in Accident Analysis & Prevention, Journal of Transportation Geography, and Annuals of Regional Science. She served on the Transportation Research Board (TRB) ABJ80 Statistical Analysis committee and the National Cooperative Highway Research Program (NCHRP) panel. She advises the student chapter of the Society of Women Engineers (SWE) at SFSU.
MARY K REQUA http://orcid.org/https://0000-0002-1936-386X San Francisco State University [biography]

Mary K. Requa received her PhD from the UC Berkeley/San Francisco State Joint Doctoral Program in Special Education. She is an associate professor and program coordinator in the mild/moderate support needs program. Her primary teaching responsibilities include courses related to understanding, assessing, and instructing individuals with mild and moderate support needs including methods and materials to promote successful learning outcomes for typical and atypical learners of all ages. Dr. Requa’s primary research interests include pre-service teacher education and inclusive practices. She is currently co-principal investigator on an NSF Improving Undergraduate STEM Education (IUSE) grant. This project aims to develop a prototype of an AI-driven teamwork platform and explore its feasibility in (1) providing neurodiversity-informed content to students and (2) assisting professors triage or mentor students during teamwork and increasing neurodiversity awareness among faculty and students.
Dr. Zhuwei Qin San Francisco State University [biography]

Dr. Zhuwei Qin is currently an assistant professor in the School of Engineering at San Francisco State University (SFSU). His research focuses on designing efficient AI computing algorithms and human-centered AI systems for real-world deployment, with applications such as health technology, rehabilitation, and AI education. Dr. Qin serves as the director of the Mobile and Intelligent Computing Laboratory (MIC Lab) at SFSU. Dr. Qin's research endeavors are dedicated to addressing the inherent challenges related to efficiency and robustness in the practical application of deep learning within real-world environments. A central focus of MIC Lab is to achieve computational acceleration for deep learning, including deep neural networks and large language models, on low-power hardware without reliance on heavy compute or storage resources. His research addresses the efficiency, reliability, and security challenges in the algorithm design and system optimization, as well as application development to create intelligent mobile edge computing systems.
Adrian Arnaud San Francisco State University

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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