Evolving intersections: AI, disability, and academic integrity in higher education
Open accessAbstract
In this article, we investigate the critical intersections of AI, academic integrity, and disability in the context of a large undergraduate course. Our aim was to adapt the course to respond to generative AI (GenAI) to avoid entrenching barriers for students, and instead teach them how to use GenAI tools in ways that deepen their learning and uphold academic honesty. Grounded in disability justice and access pedagogies, we outline five design goals centered on guidelines for AI usage, education on responsible AI use, revised assessments, support for teaching assistants (TAs), and accessible materials. These activities are detailed in our methodology. In our findings, we provide a critical reflection of the course adaptation, taking up issues such as varying levels of familiarity with GenAI, students’ capacity to engage with course changes, resistance to GenAI, instructors’ relational shifts to AI, and feelings of demoralization among the teaching team. We conclude by offering practical recommendations for educators, calling for learning communities to view this disruption as an invitation to listen to disabled students.
AI snapshot
Written from the paper’s full text.
TL;DR
This article critically reflects on adapting a large undergraduate health studies course (approximately 150–200 students per semester) to generative AI through disability justice and access pedagogies, guided by five design goals. The authors report themes around uneven TA familiarity, limited student capacity for collective work, some students’ refusal of GenAI, shifts in instructor relationality and trust, and increased assessment complexity, concluding that one-off course adaptations cannot address structural pressures.
Key findings
- The adaptation was organized around five design goals: clear AI-use expectations, online modules on responsible AI use, TA support with educative approaches, AI-integrated accessible assessments, and accessible learning management platform/materials.
- After one term, Jessica identified five themes: uneven TA familiarity with GenAI; little student uptake of multiplayer assessment options; some students explicitly did not want to use GenAI to avoid dependency; an unsettling relationality shift in which she wondered whether students had used GenAI; and adapted assessments becoming more complex and time-consuming.
- When students used GenAI outside the provided guidelines, the teaching team felt “acutely demoralizing”; the authors argue structural pressures such as full-time employment, care work, long commutes, housing and food precarity push students toward GenAI, so systemic institutional responses are needed rather than one-off course adaptations.
- The authors recommend differentiated or two-track assessments, communities of practice, and listening to disabled students, while resisting returns to pen-and-paper or standardized assessment.
Methods
- Design: a twofold methodology—first adapting the course for AI integration guided by five design goals, then engaging in critical reflection using Morley’s (2008) approach to examine underlying assumptions, contradictions, and inconsistencies. The work is framed by a pedagogy of care, access, and disability justice.
- Data or sample: Jessica’s large undergraduate health studies course at York University, with enrollment of approximately 150–200 students per semester, taught in Fall 2023 and Winter 2024; the teaching team consisted of the professor and four to eight TAs. Jessica also completed a three-week AI education course through her institution’s Teaching Commons.
- Analysis: first-person critical reflection after teaching one term, including collaborative reflection between the authors. Student and TA check-ins occurred but are not reported because ethics approval could not be arranged in time.
Limitations
- Stated: discussions with students and TAs about their experiences are outside the scope of the article because the authors could not arrange ethics in time to report on those data.
- Stated: the findings come from critical reflection after one term of one course adaptation; the authors note that one course adaptation is not powerful enough to urge students away from GenAI given structural pressures, and systemic institutional responses are needed.
- Evident: the article reports no formal outcome data on student learning or the effectiveness of the adaptations, and the single-course, single-instructor context limits generalizability.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
7 of 34 cited sources checked · 3 supported · 1 flagged · accuracy not yet measured
- Premisecitation
AI systems have not been tested with disabled users.
“Because these systems have not been tested with disabled users (Smith & Smith, 2021)”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Smith & Smith, 2021 source
The Conclusions (page unavailable) say it is not enough to wait until testing or evaluation to involve disabled people; they call for involvement from the start of design. The Literature review also describes robotic-assisted training evaluated using data from a person with a spinal cord injury. The paper criticizes when disabled people are involved, rather than saying AI systems have not been tested with them. (LLM, conf=0.78)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
- Premisecitation
AI tools can create pathways to access for disabled and other students.
“Importantly, AI tools can also create pathways to access for disabled and other students.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Marino et al. (2023) source
- Premisecitation
A study by Pennsylvania State University on 13 AI language models revealed that the models consistently created negative associations with disability-related terms, highlighting explicit and implicit biases in the training data.
“For example, a study by Pennsylvania State University on 13 AI language models revealed that the models consistently created negative associations with disability-related terms, highlighting explicit and implicit biases in the training data (Venkit & Wilson, 2021).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Venkit & Wilson, 2021 source
The authors’ affiliation is Pennsylvania State University (p. 1), and the Abstract (p. 1) reports strong negative bias toward sentences mentioning disability. But it identifies four sentiment-analysis tools and two toxicity models, not 13 language models. The Discussion and Conclusion (p. 10) links the findings to non-inclusive training, but the paper does not establish both explicit and implicit biases in the training data as claimed. (LLM, conf=0.91)
Experimental — accuracy not yet measured
- Premisecitation
AI has transformative potential to support disabled students with daily tasks that may otherwise be difficult due to impairments or injuries.
“Marino et al. (2023) highlighted AI's transformative potential to support disabled students with daily tasks that may otherwise be difficult due to impairments or injuries.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Marino et al. (2023) source
- Findingown data
There was very little uptake from students on the multiplayer options for assessments.
“There was very little uptake from students on the multiplayer options for assessments.”
Quote in the paperSubstantial claim
- Findingown data
Some of the adapted assessments became more complex and time-consuming for students, with more lengthy steps to complete and things to consider.
“When we designed the course adaptation and assessments, some of the assessments did become more complex and time-consuming for students.There were more lengthy steps to complete and things to consider.”
Quote in the paperSubstantial claim - Findingown data
Some students preferred to complete assessments without GenAI, wanting to develop their writing and research skills and avoid dependency on GenAI.
“Some students were explicit in their desire to work on their writing and research skills without the use of GenAI.”
- Also stated in: § Some students not wanting to use GenAI · plain
“Several students said they were not interested in using GenAI and wanted to complete assessments without it, stating that they did not want to develop a dependency on GenAI.”
Quote in the paperNot a substantial claim - Also stated in: § Some students not wanting to use GenAI · plain
- Interpretationreasoning
Reliance on biased data sets and AI-based decisions can create new forms of technological oppression.
“Consequently, reliance on biased data sets and AI-based decisions can create new forms of technological oppression.”
Quote in the paperSubstantial claim - Premisecitation
AI involves exploitation in dataset management.
“exploitation in dataset management (Rowe, 2023)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Rowe, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
AI applications in sectors such as healthcare and policing have been shown to exhibit racial biases that disproportionately disadvantage Black communities.
“Similarly, AI applications in sectors such as healthcare and policing have been shown to exhibit racial biases that disproportionately disadvantage Black communities (Fountain, 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Fountain, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Concerns about surveillance, discrimination, and bias have been historically linked to academic integrity policies in higher education.
“These issues have also been historically linked to academic integrity policies in higher education (Brown, 2020).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Brown, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The low uptake of multiplayer options reflects structural determinants such as full-time employment, care responsibilities, and economic pressures.
“the structural determinants of ways students can engage in course work that takes time, when they are juggling full-time employment, care responsibilities, and economic pressures and injustices.”
Quote in the paperSubstantial claim - Interpretationreasoning
These pressures push students to engage with GenAI.
“These are the very pressures that push students to engage with GenAI.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 31
- May combine two claims. 15
- The quote only partly states this claim. 1
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationGagné (2023) advocated moving away from competitive, isolationist learning environments and instead educating students about the importance of academic integrity as a relational process between themselves and those they are learning alongside.
“To uphold a culture of integrity in classrooms, Gagné (2023) advocated moving away from competitive, isolationist learning environments and instead educating students about the importance of academic integrity as a relational process between themselves and those they are learning alongside.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Gagné (2023) source
May combine two claims.
PremisecitationAI systems are often trained on datasets with limited representations of race and gender and exclude bodies that do not fit a 'normal' standard, including disabled bodies.
“AI systems are often trained on datasets with limited representations of race and gender and exclude bodies that do not fit a”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Gagné, 2023 source
In “What We Know: eProctoring Is Ableist” (page unavailable), Gagné says proctoring software is rarely tested on disabled learners and that its coding of bodies as “normal” or suspicious creates inequities for disabled students. The section also describes racial bias, but it does not say that AI systems generally are trained on datasets with limited race and gender representation. That training-data explanation and the extension beyond proctoring software are not supported by the copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationcitationBecause AI systems have not been tested with disabled users, they cannot account for the different ways students live and learn and can be punitive against those falling outside of the 'norm'.
“Because these systems have not been tested with disabled users (Smith & Smith, 2021), they cannot account for the different ways students live and learn and can be punitive against those falling outside of the”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Smith & Smith, 2021 source
The citation is attached to the assertion that the systems have not been tested with disabled users. The Conclusions (section 6; page unavailable) urge involving disabled people from the design stage rather than waiting until testing or evaluation, but do not establish whether the systems in question were tested with disabled users. A search for that testing claim found no passage that establishes it. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone givenIn Fall 2023, students seemed to be struggling more and attendance was lower than it had been in previous terms.
“Students also seemed to be struggling more and attendance was lower than it had been in previous terms.”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown dataThe teaching team varied in their knowledge and comfort with GenAI tools such as ChatGPT.
“What was immediately clear when we met for our presemester meeting was the diversity in TAs' knowledge and comfort with GenAI tools such as ChatGPT.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningThese issues create significant concern about AI use in research.
“These issues create significant concern about AI use in research.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
PremisecitationInstitutions need to be held accountable to help facilitate these initiatives and be at the forefront of developing digital justice principles to prevent potential harms of these technologies.
“However, as Sweetman and Djerbal (2023) have argued, institutions need to be held accountable to help facilitate these initiatives and be at the forefront of developing digital justice principles to prevent potential harms of these technologies.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Sweetman and Djerbal (2023)
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PremisecitationMorley's (2008) approach to critical reflection is centered on an introspective analysis that challenges conventional thinking and practices and examines underlying assumptions in research narratives to reveal contradictions and inconsistencies.
“In this study, we drew upon Morley's (2008) approach to critical reflection, which is centered on a introspective analysis that challenges conventional thinking and practices and examines underlying assumptions in research narratives to reveal contradictions and inconsistencies.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Morley (2008)
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PremisecitationMorley's approach values first-person accounts of experiences and collaborative reflection methods to access deeper and more diverse understandings.
“This approach values first-person accounts of experiences and collaborative reflection methods to access deeper and more diverse understandings.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Morley (2008)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSome AI models struggle to differentiate between scholarly and nonscholarly sources and have been known to falsify information.
“They also commented on how some AI models struggle to differentiate between scholarly and nonscholarly sources and have been known to falsify information.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Sweetman and Djerbal (2023)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationcitationExisting biases in data sets shape information generated by AI systems, creating issues with ethics and reliability of its use.
“These existing biases in data sets also shape information generated by AI systems, creating issues with ethics and reliability of its use.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationDrawing on Cariaga (2021) and Shalaby (2021), El Sabbagh and Schwarz recommended centering students' humanity, providing noncarceral and nonpunitive ways for students to engage in learning together, and creating spaces for collective accountability.
“Drawing on the works of Cariaga (2021) and Shalaby (2021), they recommended centering students' humanity, providing noncarceral and nonpunitive ways for students to engage in learning together, and creating spaces for collective accountability.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Cariaga (2021)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Shalaby (2021)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
PremisecitationEducation scholars have advocated for teaching academic honesty as an ongoing skill that students develop throughout their academic journeys.
“Education scholars have advocated for teaching academic honesty as an ongoing skill that students develop throughout their academic journeys (Eaton, 2023).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Eaton, 2023 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationScholars including Henry and Oliver (2022) have cautioned against being hasty to address these issues with more technological surveillance, and instead see this time as an 'important opportunity for dialogue that opens up new ethical and pedagogic forms of academic practice'.
“Yet scholars including Henry and Oliver (2022) have cautioned against being hasty to address these issues with more technological surveillance, and instead see this time as an”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Henry and Oliver (2022)
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PremisecitationAbleism has broad impact such that one does not have to be disabled to experience ableism.
“Further, we work from Lewis's 2021 definition of ableism which indicates its broad impact:”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lewis's 2021
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PremisereasoningSpeech-to-text technology can support students who face challenges with writing.
“For instance, speech-to-text technology can support students who face challenges with writing.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationAddressing the challenges posed by AI in higher education requires a comprehensive, strategic approach that is not limited to the classroom.
“Addressing the challenges posed by AI in higher education requires a comprehensive, strategic approach that is not limited to the classroom (Pagaling et al., 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Pagaling et al., 2022
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PremisecitationEl Sabbagh and Schwarz (2023) offered ways to create inclusive spaces through 'interdependent classrooms' that consider an individual student's learning as connected to every student's learning.
“El Sabbagh and Schwarz (2023) offered ways to create these spaces through”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites El Sabbagh and Schwarz (2023) source
- Rests on an assumption: Educators are increasingly calling for pedagogies that are inclusive, accessible, and trauma aware.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationGenerative models such as ChatGPT can create original and realistic outputs and have been gaining prevalence in educational contexts.
“Generative models such as ChatGPT (https://chat.openai.com)can create original and realistic outputs, and have been gaining prevalence in educational contexts (Sullivan et al., 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Sullivan et al., 2023
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PremisecitationLiterature on the ethics of AI use has noted significant concerns about surveillance, discrimination, and bias.
“For example, literature on the ethics of AI use has noted significant concerns about surveillance, discrimination, and bias (Evans et al., 2023;Siau & Wang, 2020)”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Evans et al., 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Siau & Wang, 2020 source
- Rests on an assumption: The use of AI-based proctoring software to surveil students has faced criticism for perpetuating racism, ableism, and gender discrimination.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAI involves environmental impacts of data storage centers.
“environmental impacts of data storage centers (Li et al., 2023)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Li et al., 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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PremisecitationThe dominance of Western-centric data in AI training can exclude or misrepresent non-Western perspectives, histories, and knowledge, bringing into question the fairness of AI systems.
“Sweetman and Djerbal (2023) critiqued the dominance of Western-centric data in AI training that can exclude or misrepresent non-Western perspectives, histories, and knowledge, bringing into question the fairness of AI systems.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Sweetman and Djerbal (2023)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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PremisereasoningLanguage translation tools can be a valuable resource to support English language learners.
“Additionally, language translation tools can be a valuable resource to support English language learners.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPopular writing tools such as Grammarly can assist students with correcting grammar and spelling errors, while tools like Quillbot and Copy.ai can support paraphrasing and creating essay outlines.
“Popular writing tools such as Grammarly (https://www.grammarly.com)can assist students with correcting grammar and spelling errors, while tools like Quillbot (https://quillbot.com) and Copy.ai (https://www.copy.ai)can support paraphrasing and creating essay outlines (Malik et al., 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Malik et al., 2023 source
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationStudents need to have meaningful, and multiple, ways to express their learning and meet the learning outcomes of a course.
“In addition, students need to have meaningful, and multiple, ways to express their learning and meet the learning outcomes of a course (Kumar et al., 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kumar et al., 2023 source
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationCritical reflection is a valuable method in education research, particularly when exploring one's professional practice.
“Critical reflection is a valuable method in education research, particularly when exploring one's professional practice (Fook, 2011).”
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PremisecitationWork by Currie (2023) on engaging students in learning that relies on mad-positive methods and community building is another site of generative thinking that invites educators to teach to all students for another world.
“Work by Currie (2023) on engaging students in learning that relies on mad-positive methods and community building is another site of generative thinking that invites educators to teach to all students for another world.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Currie (2023)
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InterpretationreasoningThe low uptake of multiplayer options reflects little time to do this work in the short hour assigned to tutorials.
“little time to do this work in the short hour assigned to tutorials”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationCommunities of practice are spaces that can support counter-cultural work.
“Communities of practice are spaces that can support the kinds of counter-cultural work we discuss throughout this article (Cambridge & Suter, 2005;Johnson, 2022;Wenger, n.d.).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Cambridge & Suter, 2005
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Johnson, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Wenger, n.d.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningStudent and TA discussions about their experience are outside the scope of this article because ethics approval could not be arranged in time to report on these data.
“As a part of iterative course check-ins, Jessica also asked students and TAs about their experience; however, these discussions are outside the scope of this article, as we could not arrange ethics in time to report on these data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningThe pressures that students live with include full-time employment, care work, long commutes, housing, food precarity and injustice, and the pathologies of poverty.
“In short, the pressures that students live with include but are not limited to full-time employment, care work, long commutes, housing, food precarity and injustice, and the pathologies of poverty.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe low uptake of multiplayer options reflects students' limited capacity to engage in collective work, which is more laborious in time and negotiation.
“We think this finding reflects a few things: student capacity to engage in collective work that is more laborious in terms of time and negotiating all that comes with group work”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenIf students have greater capacity to engage in meaningful assessment opportunities across their course load, then they can truly begin to benefit from the access and care afforded by initiatives like course adaptations.
“It is clear that if students have greater capacity to engage in meaningful assessment opportunities across their course load, then they can truly begin to benefit from the access and care afforded by initiatives like course adaptations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningWorking enough in paid employment to stay housed and fed necessarily takes precedence over completing an assignment that ChatGPT can complete in minutes.
“Working enough in one's paid employment to stay in one's home and have enough to eat necessarily takes precedence over completing an assignment that ChatGPT can complete in minutes.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningOne course adaptation is not powerful enough to urge students not to generate assessments using GenAI because the structural pressures in their lives are too large.
“We reflected on how one course adaptation is not powerful enough to urge students not to generate assessments using GenAI; the structural pressures in their lives are too large.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCurrie's (2023) work on digital space-making explores how to engage with one another and build community online and offline in ways that honor all students.
“Importantly, this activity was informed by Currie's (2023) work on digital space-making to explore how we could engage with one another and build community online and offline in ways that honored all students.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Currie, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningCommunities of practice can resist the isolationist academic work the academy structures for people and where ableism thrives.
“Communities of practice can resist the isolationist academic work the academy structures for people and where ableism thrives.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
AI tools are rapidly changing the landscape of higher education and are being used for teaching and research in ways that are generative for students' learning.
“AI tools are rapidly changing the landscape of higher education and are being used for teaching and research in ways that are generative for students' learning.”
The paper relies on this without support.
Many responses to AI have been ableist, racist, and rooted in surveillance.
“However, many responses to AI have been ableist, racist, and rooted in surveillance.”
The paper relies on this without support.
Ableist systems are harmful for all of us.
“Recognizing that ableist systems are harmful for all of us, we assert the need for collective responsibility and action to cultivate more generative systems that resist ableism and allow all students to thrive.”
The paper relies on this without support.
Black, Brown, Indigenous, and disabled students experience harm and exclusion at the margins of education systems.
“In my research, I confront these issues, including the harm and exclusion of Black, Brown, Indigenous, and disabled students (among others) at the margins of education systems.”
The paper relies on this without support.
ChatGPT and other AI-based chatbots can be used as responsive tutoring systems to provide real-time feedback and guidance by analyzing students' performance, identifying areas of improvement, and suggesting relevant supports.
“For example, this tool and other AI-based chatbots can be used as responsive tutoring systems to provide real-time feedback and guidance by analyzing students' performance, identifying areas of improvement, and suggesting relevant supports.”
The paper relies on this without support.
ChatGPT is an example of a tool that can support students' learning experiences in multiple ways.
“ChatGPT is an example of a tool that can support students' learning experiences in multiple ways.”
The paper relies on this without support.
The use of AI-based proctoring software to surveil students has faced criticism for perpetuating racism, ableism, and gender discrimination.
“For example, the use of AI-based proctoring software to surveil students has faced criticism for perpetuating racism, ableism, and gender discrimination.”
The paper relies on this without support.
Educators are increasingly calling for pedagogies that are inclusive, accessible, and trauma aware.
“Educators are increasingly calling for pedagogies that are inclusive, accessible, and trauma aware.”
The paper relies on this without support.
No one can learn if they cannot access spaces of learning.
“No one can learn if they cannot access spaces of learning; therefore, a commitment to access for all students is necessary.”
The paper relies on this without support.
AI involves surveillance.
“surveillance”
The paper relies on this without support.
AI involves privacy concerns.
“privacy concerns”
The paper relies on this without support.
AI involves unpaid user labor in product development.
“unpaid user labor in product development”
The paper relies on this without support.
AI involves biases that perpetuate racism, ableism, and gender discrimination.
“biases that perpetuate racism, ableism, and gender discrimination”
The paper relies on this without support.
Technology designed without considerations of those marginalized by institutions has limitations.
“we emphasized the limitations of technology that is designed without considerations of those marginalized by institutions.”
The paper relies on this without support.
Not everyone has access to equal learning opportunities.
“acknowledging that not everyone has access to equal learning opportunities.”
The paper relies on this without support.
Students have disparities in their experiences with citation and research before university.
“recognizing the disparities in students' experiences with citation and research before university”
The paper relies on this without support.
AI-generated content creates challenges of unreliable and false information.
“the challenges of unreliable and false information”
The paper relies on this without support.
AI-generated content creates complexities of attributing knowledge.
“the complexities of attributing knowledge in the age of AI-generated content”
The paper relies on this without support.
Different prompts influence the style and content of AI-generated output.
“they compared their AI-generated responses to see how different prompts influenced the style and content of the output”
The authors state this as an assumption.
If students were using GenAI to complete parts of the assessments, then the assessments needed to be more robust to assess learning outcomes.
“This design was done under the assumption that if students were using GenAI to complete parts of the assessments, then the assessments needed to be more robust to assess learning outcomes.”
The paper relies on this without support.
Long before GenAI, higher education institutions were failing students living at different intersections.
“Long before GenAI, higher education institutions were failing students living at different intersections.”
The paper relies on this without support.
Pen-and-paper learning and standardized assessments are exclusionary to many students, including disabled students.
“These kinds of assessments are exclusionary to many students, including disabled students.”
The paper relies on this without support.
Disabled students have long advocated that colonial institutions do not, and have not, served all learners.
“listen to disabled students who have long advocated that colonial institutions do not, and have not, served all learners.”
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Generative models trained on under-representative datasets are prone to producing generic outputs that fail the specific needs of marginalised users, increasing cognitive load.
“Generative models trained on under-representative datasets are prone to producing generic outputs that fail the specific needs of marginalised users (Bender, Gebru, McMillan-Major, & Shmitchell, 2021; Jafry & Vorstermans, 2024), increasing rather than reducing the cognitive load on those users (Botchu et al., 2023;Venkit & Wilson, 2021).”
From The Use of Generative AI by Students with Disabilities in UK Higher Education: A Mixed-Methods Survey of 125 Students (2025) · cites this paper as Jafry & Vorstermans, 2024
The Literature Review (page not provided) says that biased training data shape AI outputs and that Western-centric data can exclude or misrepresent non-Western perspectives. It does not say that generative models produce generic outputs that fail marginalised users’ specific needs. The sentence cites other works, not this paper, for the cognitive-load claim. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Cites
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Artificial Intelligence (AI) Ethics: Ethics of AI and Ethical AI
Siau, K., & Wang, W. (2020) · Journal of Database Management · doi:10.4018/jdm.2020040105 · Cited by 533 · see citing works
Skin, Tooth, and Bone – The Basis of Movement is Our People: A Disability Justice Primer
Invalid, S. (2017) · Reproductive Health Matters · doi:10.1080/09688080.2017.1335999 · Cited by 237 · see citing works
Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
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Flagged first, then by citations
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Cited by
Sorted by citations
The use of generative AI by students with disabilities in higher education
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“…variables • Exploratory factor analysis using principal axis factoring and varimax rotation to identify latent institutional characteristics influencing disability services These statistical techniques are consistent with prior research exploring institutional-level service outcomes [2,3].”
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- Authors
- Jafry, A., & Vorstermans, J.
- Affiliations
- Ontario College of Art and Design (CA)
- York University (CA)
- Corresponding author
- Jafry, Azeezah
- Venue
- New Directions for Teaching and Learning
- Publisher
- Wiley
- Published
- 2024-09-10
- Volume
- 2025
- Issue
- 182
- Pages
- 67-80
- ISSN
- 0271-0633 (ISSN-L)
- 1536-0768
- OpenAlex
- W4402449935
- Semantic Scholar
- fa7bc223f7bec20c7d94c653f439e50438ff08ed
- Cited by
- 6
- References
- 16
- Citations by year
- 2026: 3 · 2025: 3
- FWCI
- 2.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- New Directions for Teaching and Learning (publishedVersion, cc-by-nc)
- OCAD University Open Research Repository (OCAD University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Keywords
- generative AI, academic integrity, disability, disability justice, course adaptation, responsible AI use, assessment design, teaching assistants, accessible course materials, demoralization, undergraduate education, students with disabilities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-20.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-20.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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