This scoping review maps the extent, range and nature of evidence on how artificial intelligence (AI)–powered assistive technologies support disabled students in higher education, identifying the types of tools in use, the disability groups served, the benefits and barriers reported, and the priority research gaps that emerge from the evidence.
Following the PRISMA-ScR framework (Peters et al., 2020), seven academic databases were searched for peer-reviewed studies published between January 2020 and February 2026. Search terms were developed iteratively using Medical Subject Headings, the Education Resources Information Center (ERIC) thesaurus and the ACM Computing Classification System, with pilot searches to confirm recall. From 1,847 initial records, 47 studies met all inclusion criteria after title/abstract and full-text screening.
Six AI tool categories are identified and mapped to disability groups and study counts: large language models (n = 14), speech and language processing (n = 11), intelligent tutoring systems (n = 9), computer vision tools (n = 7), adaptive learning platforms (n = 4) and affective/behaviour recognition systems (n = 2). Benefits include personalised support, reduced cognitive load, improved writing and psychosocial gains. Barriers include algorithmic bias, cost-driven digital divides, institutional policy vacuums and exclusion of disabled students from AI design. Seven priority research gaps emerge from the review findings.
The review is restricted to peer-reviewed, English-language publications, which limits conclusions about research in non-English-speaking contexts and excludes grey literature. The rapidly evolving pace of AI development means some findings may be superseded. These limitations are discussed in a dedicated Limitations section.
Universities should develop disability-inclusive AI policies that explicitly recognise generative AI as a legitimate form of assistive technology, ensure equitable access regardless of cost and integrate AI tools within universal design for learning frameworks.
AI holds significant potential to narrow the persistent graduation gap between disabled and non-disabled university students. Realising this requires inclusive design, equitable access and genuine participation by disabled students in shaping the technologies that affect them.
This is the first scoping review in the Journal of Enabling Technologies comprehensively mapping AI-powered assistive technologies for disabled students across all disability categories within higher education. It synthesises the most recent 2024–2026 empirical evidence, presents a PRISMA-ScR flowchart and proposes a structured seven-gap research agenda grounded in the review findings.
