How Can AI Support Disabled and Neurodivergent Learners in My Course?

You are the instructor who just received an accommodation letter, the accessibility lead deciding whether to approve a license, or the disability services coordinator who has to answer a tool request by Friday. You do not need a literature review. You need a defensible decision: what to adopt, what to refuse, and how to tell whether the thing you approved actually helped the student in front of you.

The bottom line, with its caveats left in place: AI can support disabled and neurodivergent learners, but what has been measured is narrow. Most of the evidence concerns tools that remove one functional barrier at a time; the strongest quantitative result comes from PK-12 special-education settings using mostly pre-generative AI; and the higher-education picture is a map of what has been tried rather than a measurement of what works. Nothing here justifies blanket adoption. Everything here supports a narrow, instructed, barrier-specific adoption — and names several practices that go wrong.

Start with the barrier, not the diagnosis

Most of what goes wrong for disabled and neurodivergent students happens at the level of a task rather than a diagnosis: a reading that assumes print, a video that assumes sustained attention, a group project that assumes unspoken social rules. Those barriers recur across diagnoses, so the productive question is not which tool suits which condition but which functional barrier a tool removes. Every recommendation below is organized that way, and so should your procurement be.

What to adopt, ordered by how strong the evidence is

1. Barrier-removing tools with a measured effect — medium confidence, but PK-12 and mostly older AI. The strongest quantitative result on this page is also the most general. Zhang, Carter, Liu and Peng (2024) synthesized 29 (quasi-)experimental studies of AI-based interventions for students with disabilities — 239 effect sizes from 41 independent samples — and found a statistically significant medium overall effect on learning outcomes, Hedge's g = 0.588 (95% CI [0.349, 0.826], p < .01), with high between-study heterogeneity. Academic performance was the largest outcome subgroup (k = 80, g = 0.929) and social-emotional skills the smallest (k = 144, g = 0.382). Computer software — speech recognition, expert systems, intelligent tutoring systems — performed strongly (k = 74, g = 0.959) and robots moderately (k = 144, g = 0.509), while intelligent VR (g = 0.528) was not significant. Publication bias was present (Egger's β = 2.837, p < .001); trim-and-fill reduced the estimate to g = 0.269, still significant. Read the confidence label honestly: every study was PK-12, 96.6% used step-based instruction in special-education or clinical settings, and most used older rule-based AI rather than generative AI. This is grounds for cautious adoption of structured, instructional software — not a warrant for the chatbot you were pitched.

2. Concrete single-barrier tools with small studies — low confidence, small samples. Three results show what an individual tool can do, each tested on a handful of learners. For attention, Pimenova, Begel and colleagues (2026) segmented instructional videos post hoc into single-instruction chunks with fixed pauses; with 17 learners with ADHD and 10 without, it improved everyone and brought the ADHD participants' errors and hesitations to parity — an equalizing effect from a lightweight transformation rather than a diagnosis-specific product. For language access, Chen et al. (2026) added Visual questions (timestamps where visual information is likely to be misread) and Emotion questions (timestamps where prior Deaf and Hard of Hearing learners reported frustration) to an Large Language Models (LLMs) baseline set, producing 30 questions, ten per strategy; with 16 learners, self-efficacy averaged 5.70 (SD = 1.12) on a seven-point scale, and visual questions were chosen more by Deaf than hard-of-hearing participants. For reading and writing, Rezazadegan et al. (2026) mined dyslexic learners' own forum discussions and found they value AI for literacy support while reporting uneven output quality and a lack of equitable accommodations — the inconsistency is itself a barrier. Adopt these as low-cost, reversible experiments, not as fixed infrastructure.

3. The higher-education inventory — a map, not a measurement. For higher education the picture is a map rather than a measurement. Rempel, Heimann and Prilop (2026) screened 766 records from five databases down to 40 included studies published between 2015 and 2025. Generative AI appeared in 15 of the 40 and virtual reality in 11. Barrier coverage was uneven: reading and writing (n = 13) and study management (n = 12) dominated, attention (n = 4) and social communication (n = 5) were neglected, and only 6 studies addressed more than one barrier. The review covers higher education only — it excluded K-12 and specialized-institution settings by design, so it is not evidence about younger learners. Use it to see which barriers have been addressed at your level and which have barely been touched.

4. Structure and universal design, which are evidence-backed and free. Learners' stated preferences supply the design brief. Zastudil et al. (2026) surveyed 24 neurodivergent computing students (autistic and/or ADHD) and 20 neurotypical peers with four follow-up interviews: the neurodivergent group was uncomfortable with unstructured or ambiguous assignments and strongly preferred smaller teams that work together consistently with explicitly defined roles, coping by self-selecting roles and disclosing strategically. Their warning for AI-mediated work is that a tool's interaction model can reproduce the same structural ambiguity. Structure is the cheapest intervention on this list, and the one most often omitted.

What to avoid, including accommodations that backfire

• Do not supply AI as an accommodation without teaching it. The most common failure is not the wrong tool but an untaught one. The case W.A. v. Clarksville/Montgomery County School System (2024) is the documented version: a student given AI as an accommodation graduated unable to read, and the court found that a free, appropriate public education had not been provided.
• Do not let a blanket AI prohibition remove an assistive tool. Wright (2026) argues that rules treating all "AI use" alike are over-inclusive because they do not separate speech-to-text transcription and OCR from generative drafting. Students with conditions affecting fine motor control, handwriting legibility or typing accuracy have relied on standalone voice-to-text products such as Dragon NaturallySpeaking and standalone OCR, several of which have been discontinued or degraded, with AI-powered transcription filling the functional gap — so a rule written as "no AI" can withdraw the student's primary means of producing legible work, and the scale of that displacement has not been measured. Treat transcription and OCR as accommodations to name explicitly in the policy rather than casualties of it; the exposure this creates belongs with Legal Issues and Risks.
• Do not organize support around diagnosis alone. The scoping review's central finding is a critique: the literature is dominated by individual accommodations and single-neurotype tools organized around formal diagnosis, while the functional differences they address cut across neurotypes and are hidden by diagnostic categorization. Dyslexic students tend to benefit from reduced reliance on written-only material, and both autistic students and students with ADHD can experience frequent fluctuation in focus, which makes performance sensitive to consistent structure and enough time for task switching. A tool matched to a label can miss the barrier the student actually has.
• Do not make self-identification the price of access. Accommodation requires the student to identify themselves as needing special provision, which carries stigma; universal design removes that step.
• Do not ignore inversion effects. Chatbot over-reliance and the cognitive overload of immersive environments are documented as inversion effects, where adding the technology lowers learning. A tool that only reproduces existing teaching in digital form should be expected to yield limited learning gains.
• Do not deploy tools whose reliability is unproven. Dyslexic students using AI for literacy support reported real value alongside unreliable output quality; a tool that cannot be relied on adds work instead of removing it.
• Do not accept products as they arrive. State universal design and participatory development as procurement requirements rather than accepting whatever is sold to you — the scoping review recommends building tools with neurodivergent students rather than for them, and its own sample shows how rare that is, with only 3 of 40 studies treating the environment or neurotypical peers as the object of intervention.
• Do not buy the most immersive option by default. Virtual reality is the most resource-intensive option in the set. Assistive technology needs devices and connectivity, and tools free in a pilot routinely move behind a paywall.
• Do not let automation bias stand in for judgment. LUDIA's authors name a limit that belongs on every deployment plan: automation bias means the educator most likely to accept a poor suggestion is the one who trusts the tool knows Universal Design for Learning. Nobody has tested its outputs for cultural or linguistic bias either.

How to tell whether a tool helps this student

Aggregate effects tell you a direction; they do not tell you whether the tool works for the student in your section next week. Three cautions should shape any trial.

First, the subgroup ordering is suggestive rather than established. The 2024 meta-analysis cannot say which design choice is better: no moderator of any kind reached significance, so rankings such as teachable agents (g = 1.100) ahead of social-emotional coaches (g = 0.336), or dyadic interaction ahead of triadic (g = 0.973 versus g = 0.385), should not be treated as settled. The defensible position is that the direction of effect is supported while the size, design optimum and durability of any gain are not.

Second, the corpus describes deployments and perceptions more than measured learning. Attention (n = 4) and social communication (n = 5) are the least-studied barriers, and only 6 of 40 studies served more than one. The scarcity of (quasi-)experimental and longitudinal designs, and the heterogeneity of tools and outcomes, are why the higher-education work is a scoping review rather than a meta-analysis. Coverage skews undergraduate and Global North, the search was English-language peer-reviewed work, and postgraduate students are under-represented. Treat engagement and satisfaction as weak indicators; measure the barrier you set out to remove.

Third, a tool that fixes one barrier may leave another. Only 6 of 40 studies served more than one barrier, so check the tool against the specific difficulty the student reports — reading and writing, study management, attention, or social communication — and evaluate against that barrier, not against a satisfaction survey. Ask the student; Zastudil et al.'s participants and Rezazadegan et al.'s forum posters both describe their needs more precisely than the products built for them.

Objections you will hear

"We cannot require a tool for everyone." You can, where the tool is a design feature rather than a personal accommodation. Video segmentation with fixed pauses improved all learners, not only those with ADHD, and bringing the ADHD participants' errors and hesitations to parity is exactly the universal-design outcome that removes the need for disclosure. Pimenova and Begel's result is an equalizing effect from a lightweight transformation rather than a diagnosis-specific product. Pair that with accessible defaults: read-aloud, OCR and text-leveling tools recur in the validated policy items of Shin et al. (2026), whose LLM-based topic modeling and two rounds of Delphi surveys of 17 experts across 12 U.S. AI-in-education policy documents from 2015 to 2025 found that only 2 of the 12 specifically address learning disabilities, while 18 topics present in general AI or other-disability policy — data protection, legal risk management, ethical guidelines among them — are missing from specific-learning-disability policy.

"Captioning and translation are not academic support." Captioning is a start, not the whole of language access. Text-based prompts mismatch sign-based first languages: pair transcripts with visual and emotional context cues and cut unnecessary complexity, as Chen et al.'s Visual and Emotion questions do. And the policy experts' own ranking disagrees with the objection: their 36 validated items fall into five themes — inclusive and personalized learning (11 items, 30.56%), ethics, equity and inclusion (9, 25.00%), student empowerment and AI literacy (6, 16.67%), assessment and research (6, 16.67%), and educator preparation (4, 11.11%) — and they ranked student empowerment and AI literacy as most essential: teaching these students to use AI responsibly and independently while guarding against over-reliance.

"Students must be independent." Independence is what the instruction is for, which is why the tool without the teaching is the failure mode. Shin et al.'s educator-preparation theme exists for the same reason, and LUDIA's authors name the obstacle as the "knowing-doing divide": guidance exists in abundance but is written in general terms, while a barrier is always particular to this room, this week. The scale of need is easy to understate: roughly 240 million children worldwide live with disability, about half of them out of school in low- and middle-income countries, and about a third of teachers across OECD systems report lacking the competencies to support students with specific needs. The legal frame is the U.S. Assistive Technology Act (2004) and IDEA (2004), which place assistive-technology evaluation inside each Individualized Education Program and require a free, appropriate public education, yet no formal evidence-based guidelines help educators or families implement AI for these learners. Independence built on an untaught tool is what the case law punishes.

"We cannot afford it." Then refuse the expensive default. The assistive-technology field is concentrated in the Global North, its most immersive tools are the least scalable, and virtual reality is the most resource-intensive option in the set. LUDIA is one worked version of the universal-design alternative: it connects educators with Universal Design for Learning while a design decision is still open, and removes access barriers by architecture — no cost, no accounts or stored chats, WCAG 2.2 Level AA conformance, and 13 machine-translated languages, though non-English versions may carry the assumptions of the English they came from. It is a thought partner rather than a solution engine, organizing its inquiry around "proof of trust" rather than proof of impact, and stating plainly that the authors hold no evidence the tool improves learning. Budget honestly for the other cost: Shin et al. flag the digital divide, since the price of advanced AI tools can widen disparities between wealthier and poorer schools, alongside algorithmic bias and teacher-training gaps.

Do this this week

• Name the functional barrier first. Reading and writing, study management, attention, social communication — then check whether the tool addresses that barrier or only a diagnosis.
• Use what is evidenced. Video segmentation with fixed pauses narrowed the ADHD performance gap to parity, and read-aloud, OCR and text-leveling tools recur in the validated policy items.
• Treat captioning as a start, not the whole of language access. Pair transcripts with visual and emotional context cues and cut unnecessary complexity.
• Give structure rather than asking students to infer it. Small consistent teams, defined roles, explicit expectations and role self-selection were the preferences neurodivergent computing students reported.
• Build accessibility into procurement. State universal design and participatory development as requirements, prefer free and privacy-preserving tools, and ask what happens when the free tier ends.
• Train people, not just deploy. Budget time for students to learn the tool and for staff to work it into the assignment; an accommodation without instruction failed in court.
• Design for overlap, and watch for inversion effects. Only 6 of 40 studies served more than one barrier; chatbot over-reliance and cognitive overload in immersive environments can reduce learning.
• Report outcomes honestly. Most of the evidence describes deployments and perceptions, and no design moderator has been established; treat engagement and satisfaction as weak indicators.
• Fund the training and workflow integration that turn an accessible tool into usable support, and write the cost of devices and connectivity into the plan.

Where to go next

This page is the practical, cross-learner entry point: Universal Design for Learning covers the design philosophy that prevents barriers before they appear, Assistive Technology the tool layer a student uses, Accessibility whether a format can be perceived and operated at all, and Neurodiversity the lens that treats difference as diversity rather than deficit — all within the umbrella of Inclusive Learning.

For the research-side obligations, see incorporating equity, accessibility, privacy, ethics and pedagogical safety into AIED research.