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Disability Representation in AI-Generated Short Stories: Updating Frameworks and Introducing Disability Evasiveness

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Title: Disability Representation in AI-Generated Short Stories: Updating Frameworks and Introducing Disability Evasiveness
Language: English
Authors: Ryan Collis, Katherine Barron, Ellouise VanBerkel, Aaron Richmond
Source: AERA Online Paper Repository. 2025.
Availability: AERA Online Paper Repository. Available from: American Educational Research Association. 1430 K Street NW Suite 1200, Washington, DC 20005. Tel: 202-238-3200; Fax: 202-238-3250; e-mail: subscriptions@aera.net; Web site: http://www.aera.net
Peer Reviewed: Y
Page Count: 13
Publication Date: 2025
Document Type: Speeches/Meeting Papers
Reports - Research
Descriptors: Artificial Intelligence, Bias, Social Discrimination, Literary Genres, Disabilities, Attitudes toward Disabilities, Neurodevelopmental Disorders, Critical Literacy, Children, Models, Ethics
DOI: 10.3102/2183396
Abstract: With the rapid adoption of generative AI in education, concerns have emerged about how Artificial Intelligence (AI) can perpetuate and even amplify biases in their training data. This research aims to identify specific manifestations of disability-related discrimination in AI-generated short stories. Using critical content analysis and critical disability theory, we analyze 40 short stories about disabled and neurodivergent children generated by ChatGPT4. We identify biases in the context of disability, ableism, and disablism. Next, we build on two existing frameworks to address evolving manifestations of disability discrimination. Finally, we introduce the concept of disability-evasiveness to describe a process where non-disabled people claim to not "see disability." This research contributes to ongoing discussions of disability discrimination and ethical use of AI.
Abstractor: As Provided
Entry Date: 2026
Accession Number: ED678363
Database: ERIC
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  Data: AERA Online Paper Repository. Available from: American Educational Research Association. 1430 K Street NW Suite 1200, Washington, DC 20005. Tel: 202-238-3200; Fax: 202-238-3250; e-mail: subscriptions@aera.net; Web site: http://www.aera.net
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  Data: With the rapid adoption of generative AI in education, concerns have emerged about how Artificial Intelligence (AI) can perpetuate and even amplify biases in their training data. This research aims to identify specific manifestations of disability-related discrimination in AI-generated short stories. Using critical content analysis and critical disability theory, we analyze 40 short stories about disabled and neurodivergent children generated by ChatGPT4. We identify biases in the context of disability, ableism, and disablism. Next, we build on two existing frameworks to address evolving manifestations of disability discrimination. Finally, we introduce the concept of disability-evasiveness to describe a process where non-disabled people claim to not "see disability." This research contributes to ongoing discussions of disability discrimination and ethical use of AI.
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      – SubjectFull: Social Discrimination
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      – SubjectFull: Disabilities
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      – SubjectFull: Attitudes toward Disabilities
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      – SubjectFull: Neurodevelopmental Disorders
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      – SubjectFull: Critical Literacy
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      – SubjectFull: Children
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      – SubjectFull: Models
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      – SubjectFull: Ethics
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      – TitleFull: Disability Representation in AI-Generated Short Stories: Updating Frameworks and Introducing Disability Evasiveness
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              Y: 2025
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