Leveling the Playing Field: Temporal Video Segmentation for Individuals with ADHD in Computing Education

Veronica Pimenova, Chris Lee, Baramee Bhakdibhumi, Simon Chu, Andrew Begel (2026) — Carnegie Mellon University. arXiv preprint (cs.HC).

📄 Full text (arXiv)

Pimenova, Begel and colleagues evaluate a post-hoc video processing intervention that segments instructional videos into single-instruction chunks with fixed pauses, reducing extraneous cognitive load for learners with ADHD. In a within-participants study (17 ADHD, 10 non-ADHD), the intervention improved everyone but had an equalizing effect: ADHD participants' errors and hesitations fell to parity with non-ADHD peers. This is strong evidence for Universal Design for Learning via automated content transformation — a task well-suited to AI pipelines. It extends neurodivergent-computing-students and accessible-learning, complements simulation work like llm-educational-simulation-adhd, informs video-based learning design in engagement-assessment-video and instructional-design, within cs-education.

Related Pages

• neurodivergent-computing-students

• accessible-learning

• llm-educational-simulation-adhd

• engagement-assessment-video

• instructional-design

• cs-education

Citation

APA: Veronica Pimenova, Chris Lee, Baramee Bhakdibhumi, Simon Chu, Andrew Begel (2026). Leveling the Playing Field: Temporal Video Segmentation for Individuals with ADHD in Computing Education. arXiv:2607.24612.