Neurodiversity in Computing Education Research: A Systematic Literature Review

2025-04-17

AI Summary

This study addresses the lack of inclusivity for neurodiverse learners—including those with autism, dyslexia, and ADHD—in computing education. It conducts the first mixed-methods systematic literature review (SLR), rigorously adhering to the PRISMA framework to identify and analyze 14 eligible studies. Findings reveal a weak empirical foundation: only 57.1% of included works are empirical (predominantly interview-based), while 50% are opinion pieces; methodological reporting is inconsistent, most pedagogical recommendations lack empirical validation, and interventions remain largely confined to curriculum design. Key contributions include: (1) the first systematic research map of neurodiversity in computing education; (2) identification of three critical research gaps; and (3) proposal of three priority research directions—rigorous evaluation of active learning efficacy for neurodiverse students, in-depth investigation of their learning experiences and underlying cognitive mechanisms, and meaningful engagement of neurodiverse students as co-designers in pedagogical development and educational research.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Ensuring equitable access to computing education for all students-including those with autism, dyslexia, or ADHD-is essential to developing a diverse and inclusive workforce. To understand the state of disability research in computing education, we conducted a systematic literature review of research on neurodiversity in computing education. Our search resulted in 1,943 total papers, which we filtered to 14 papers based on our inclusion criteria. Our mixed-methods approach analyzed research methods, participants, contribution types, and findings. The three main contribution types included empirical contributions based on user studies (57.1%), opinion contributions and position papers (50%), and survey contributions (21.4%). Interviews were the most common methodology (75% of empirical contributions). There were often inconsistencies in how research methods were described (e.g., number of participants and interview and survey materials). Our work shows that research on neurodivergence in computing education is still very preliminary. Most papers provided curricular recommendations that lacked empirical evidence to support those recommendations. Three areas of future work include investigating the impacts of active learning, increasing awareness and knowledge about neurodiverse students' experiences, and engaging neurodivergent students in the design of pedagogical materials and computing education research.

Problem

• Ensuring equitable computing education for neurodiverse students
• Assessing current research on neurodiversity in computing education
• Identifying gaps and future directions for inclusive pedagogical practices

Innovation

• Systematic literature review on neurodiversity
• Mixed-methods analysis of research contributions
• Future work on active learning impacts