Disability Evasiveness: Disability Representation in AI-Generated Flash Fiction
Bibliographic record
Abstract
Abstract In this research article, Katherine Barron, Ryan B. Collis, Aaron J. Richmond, and Ellouise Van Berkel explore disability representation in flash fiction generated by artificial intelligence (AI). The meteoric adoption of generative AI in K–12 education raises concerns about how tools like OpenAI9s ChatGPT might perpetuate and amplify biases present in their AI training data. In the educational sphere, there is a vital need for both teachers and students to develop critical literacy skills in order to resist discriminatory narratives about historically marginalized groups. The article aims to identify specific expressions of disability-related discrimination in AI-generated short stories. Using critical content analysis and critical disability theory, the authors analyze forty stories about disabled and neurodivergent children generated by ChatGPT-4. The analysis is guided by Connor9s (2017) lists of positive and negative disability representations and Landrum9s (2001) criteria for evaluating story elements. The authors first identify how the stories reflect and reinforce societal biases in the context of disability, ableism, and disableism. They then offer the term disability evasiveness to describe a process where nondisabled people claim to not “notice disability.” The article concludes with suggestions and resources for critical literacy instruction in K–12 settings. This research contributes to disability studies in education scholarship and to ongoing discussions of the use of AI in K–12 classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".