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Disability Evasiveness: Disability Representation in AI-Generated Flash Fiction.

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Title: Disability Evasiveness: Disability Representation in AI-Generated Flash Fiction.
Authors: BARRON, KATHERINE1, COLLIS, RYAN B.1, RICHMOND, AARON J.1, VAN BERKEL, ELLOUISE1
Source: Harvard Educational Review. Winter2025, Vol. 95 Issue 4, p613-639. 27p.
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 OpenAI's 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 AIgenerated 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 Connor's (2017) lists of positive and negative disability representations and Landrum's (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 set tings. This research con trib utes to dis abil ity stud ies in edu ca tion schol arship and to ongo ing dis cus sions of the use of AI in K-12 class rooms. [ABSTRACT FROM AUTHOR]
Copyright of Harvard Educational Review is the property of Harvard Education Publishing Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Harvard+Educational+Review%22">Harvard Educational Review</searchLink>. Winter2025, Vol. 95 Issue 4, p613-639. 27p.
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  Data: 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 OpenAI's 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 AIgenerated 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 Connor's (2017) lists of positive and negative disability representations and Landrum's (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 set tings. This research con trib utes to dis abil ity stud ies in edu ca tion schol arship and to ongo ing dis cus sions of the use of AI in K-12 class rooms. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Harvard Educational Review is the property of Harvard Education Publishing Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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