Comprehension without load, engagement without persistence
Measuring whether generative AI is accessible to disabled learners
DOI:
https://doi.org/10.17411/jacces.v16i2.788Abstract
Generative artificial intelligence (GenAI) tools are increasingly offered to disabled learners to make learning accessible. Accessibility is usually evaluated by conformance: checking an interface against the success criteria of a technical standard such as WCAG 2.2. Such checks show that content can be reached, not how much effort using it requires. This systematic analysis of 92 studies (2023 to March 2026) examines whether research measures how much mental effort GenAI-supported learning demands of disabled learners, whether individual learners keep using these tools across repeated sessions or stop, and whether their confidence as learners grows. Seventeen outcome constructs were coded against Cognitive Load Theory, the Technology Acceptance Model and Self-Determination Theory. Studies mostly measured immediate responses: comprehension (57.6%), engagement (53.3%) and attitudes (48.9%). Few measured the processes that decide whether these results last: cognitive load (10.9%), disengagement (4.3%) and self-efficacy (17.4%), giving asymmetries of 5.3:1, 12.3:1 and 2.8:1. Only 27.2% of studies followed learners beyond a single session. A second coder agreed poorly with the original coding and judged that 16 of 23 sampled studies did not meet all inclusion criteria, so these figures are upper estimates. Cognitive load is defined as the demand a task places on working memory; only the load that arises when the output format does not match a learner’s way of accessing information is treated as an accessibility issue. A three-axis framework adds evidence from use to conformance-based evaluation.
References
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
CAST. (2018). Universal Design for Learning guidelines version 2.2. http://udlguidelines.cast.org
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Feinstein, A. R., & Cicchetti, D. V. (1990). High agreement but low kappa: I. The problems of two paradoxes. Journal of Clinical Epidemiology, 43(6), 543–549. https://doi.org/10.1016/0895-4356(90)90158-L
Flake, J. K., & Fried, E. I. (2020). Measurement schmeasurement: Questionable measurement practices and how to avoid them. Advances in Methods and Practices in Psychological Science, 3(4), 456–465. https://doi.org/10.1177/2515245920952393
Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
Galasso, B. J. B. (2026). Visuo-spatial cognition and the redesign of human-AI interaction in generative artificial intelligence. Discover Artificial Intelligence, 6, 950. https://doi.org/10.1007/s44163-026-02040-2
Gwet, K. L. (2008). Computing inter-rater reliability and its variance in the presence of high agreement. British Journal of Mathematical and Statistical Psychology, 61(1), 29–48. https://doi.org/10.1348/000711006X126600
Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139–183). North-Holland. https://doi.org/10.1016/S0166-4115(08)62386-9
Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M.-C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. https://doi.org/10.3233/EFI-180221
Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? Educational Psychology Review, 23(1), 1–19. https://doi.org/10.1007/s10648-010-9150-7
Lazar, J., Feng, J. H., & Hochheiser, H. (2017). Research methods in human-computer interaction (2nd ed.). Morgan Kaufmann.
Leppink, J., Paas, F., van der Vleuten, C. P. M., van Gog, T., & van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133–144. https://doi.org/10.1177/1477878509104318
Oliver, M. (1990). The politics of disablement. Macmillan.
Paas, F., & van Merriënboer, J. J. G. (2020). Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4), 394–398. https://doi.org/10.1177/0963721420922183
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. ASCD.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Shakespeare, T. (2006). Disability rights and wrongs. Routledge.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
W3C. (2021). Making content usable for people with cognitive and learning disabilities (W3C Working Group Note, 29 April 2021). World Wide Web Consortium. https://www.w3.org/TR/coga-usable
W3C. (2023). Web Content Accessibility Guidelines (WCAG) 2.2. World Wide Web Consortium. https://www.w3.org/TR/WCAG22
World Health Organization. (2011). World report on disability. WHO.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Galasso B.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share or adapt the work with an acknowledgment of the work's authorship and initial publication in this journal. Use of the work for commercial purposes are not allowed.
- Authors are able to publish the journal's published version of the work in other media (e.g., post it to an institutional repository or publish it in a book), as far as they inform the Journal of Accessibility and Design for All of that fact. When publishing their work in other sources, authors must mention the name of the Journal of Accessibility and Design for All, its ISSN, the number and issue in which the article was published and a link to the main page of the Journal of Accessibility and Design for All. Optionally, they can also include a link to the article published in the Journal of Accessibility and Design for All.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website), as it can lead to productive exchanges, as well as earlier and greater citation of published work.
