2026 ASEE Annual Conference & Exposition

Concept-mapping the struggles and strengths of neurodiverse students in engineering: a comparison between expert- and AI-generated trees

Presented at DSAI-Session 13: Equity, Inclusion, and Diverse Learner Needs in Engineering Education

Engineering students with neurodivergent conditions (autism, ADHD, dyslexia, etc.) tend to have lower education and employment outcomes compared to their neurotypical counterparts. A plethora of research and increasing awareness of neurodiversity in education call for a synthesis of the strengths and challenges that neurodiverse students experience in engineering. This paper aims to address two questions: First, can concept maps be used to illustrate the nuanced challenges and strengths for neurodiverse students in engineering? Second, can large-language models (LLMs) appreciate their education experience with empathy and precision in ways similar to a human expert?
Concept maps are used to enhance meta-cognition skills by externalizing thoughts and lived experiences. This study cast concept maps into tree structures to illustrate the challenges, strengths, and their connections in education contexts. Two methods were used to develop those trees independently. First, knowledge/human experts constructed trees based on literature review and students’ interview transcripts to illustrate the main challenges, strengths, and their relationships. Second, LLMs were used to transform challenges and strengths definitions into high-dimensional embeddings, whereas hierarchical clustering was applied to these embeddings, resulting in AI-generated trees. The comparisons between AI-generated trees and expert trees demonstrated the potential of AI to convey the nuances in neurodiversity literature. The comparison also revealed discrepancy between the two types of trees, which could imply the limited empathy and precision of AI and a need for customized AI tools to support neurodiverse students, increase self-awareness, and challenge stigma and deficit-based perspectives.
Key words: concept maps, hierarchical clustering, text embedding, LLM, neurodiversity

Authors

Yiyi Wang San Francisco State University [biography]

Yiyi Wang is an assistant 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.
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.
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.
Chanel-Taylor Guevarra Medeiros

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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