Cynthia Zastudil, Srishty Muthusekaran, Rayhona Nasimova, Stephen MacNeil (2026) — Temple University. arXiv preprint (cs.HC).
This study surveyed 24 neurodivergent computing students (autistic and/or ADHD) and 20 neurotypical peers, supplemented by 4 in-depth interviews, to understand how collaborative active learning structures affect comfort and accessibility. Three key findings emerge: (1) Neurodivergent students experience significant discomfort with assignments that lack clear structure or have ambiguous expectations — the title quote reflects a common frustration with unspoken social norms in teamwork. (2) They strongly prefer smaller teams that work together consistently, with explicitly defined roles, minimizing the cognitive load of social negotiation. (3) Common coping strategies include self-selecting roles and strategic self-disclosure of neurodivergence.
The findings connect to broader concerns in equity-in-ai-education and inclusive-ai: as AI tutors and collaborative AI tools enter computing classrooms, their interaction models may inadvertently replicate the same structural ambiguities that disadvantage neurodivergent learners. The preference for defined roles and predictable structures mirrors themes in special-education about explicit scaffolding. For cs-education, the study provides actionable design recommendations: instructors should provide structured assignments, use smaller consistent teams, and allow role self-selection. While preliminary (n=24), this is among the first studies to center neurodivergent voices in computing education research, contributing to more student-centered approaches to collaborative-learning.
Related Pages
- special-education — Survey and interview study of autistic/ADHD computing students
- student-experience — Documents discomfort with unstructured collaborative assignments
- cs-education — Active learning design recommendations for neurodivergent learners
- equity-in-ai-education — Accessibility gaps in collaborative pedagogical structures
- inclusive-ai — Informs how AI tools should accommodate neurodivergent collaboration styles
- collaborative-learning — Smaller teams with defined roles preferred by neurodivergent students
- embodied-string-learning-blindness-low-vision-musicians — Designing for What Cannot Be Seen: Supporting Embodied String Learning for Musicians with Blindness and Low-Vision
- adhd-video-segmentation-computing-education — Automatically segmenting instructional videos into single-instruction chunks with pauses equalizes p
Citation
APA: Cynthia Zastudil, Srishty Muthusekaran, Rayhona Nasimova, Stephen MacNeil (2026). "I can't read your mind": A Study of Neurodivergent Computing Students' Experiences with Collaborative Active Learning. arXiv:2605.23823. arXiv preprint (cs.HC).