Harnessing Generative AI to Overcome Executive Dysfunction in Higher Education: A Case Study
Bibliographic record
Abstract
Generative tools such as ChatGPT, Perplexity and Google Gemini have rapidly transformed educational practices across campus environments by providing personalized academic support. This qualitative longitudinal case study examines how such tools can in fact help mitigate challenges linked to a neurodivergent condition called executive dysfunction—a condition that impairs focus, organization, planning, and time management. Using the case study of a student at the University of Toronto (pseudonym "T"), it details the integration of AI for lecture transcription, task management, idea creation, and writing support. The results indicate that when AI is employed as an assistive scaffold rather than a replacement for critical thought, the student demonstrates enhanced metacognitive awareness, more efficient task breakdown, and steadily improved academic performance. These outcomes support the notion that responsible, ethically integrated AI applications can foster inclusive, patient, and personalized learning environments that address individual cognitive profiles and promote independent, self-regulated learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".