70/100

Moderate evidence

A review of 16 studies finds large gains for students with disabilities using AI

Study title: Effectiveness of intervention using artificial intelligence for students with disabilities: systematic review and meta-analysis

Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
Published
3 min read

Study title: Effectiveness of intervention using artificial intelligence for students with disabilities: systematic review and meta-analysis

A systematic review and meta-analysis by researchers at Jeonju University in South Korea pooled 16 studies of AI-based interventions for students with disabilities. The overall effect was large (Hedges' g = 1.263, 95% CI 0.950–1.576), and 14 of the 16 studies (87.5%) reported statistically significant improvements.

Read the study abstract

Background Algorithmic systems do not sufficiently capture the contextual nuances of disability, thereby risking model bias and reducing ecological validity. Hence, this review aims to investigate the various pedagogical and ethical concerns that have resulted from the integration of artificial intelligence (AI) into special education. Methods We conducted a systematic review and meta-analysis to investigate the effects of intervention using AI on students with disabilities. For this purpose, 16 papers were sele…

The gain

A meta-analysis of 16 studies of students with disabilities found AI-based interventions linked to large improvements (Hedges' g = 1.263, 95% CI 0.950–1.576), with 14 of 16 studies showing significant effects, though only one study was randomized.

In brief

Effects differed by outcome and group. Learning (ES 1.99) and cognitive outcomes (ES 1.92) improved most, and students with intellectual disabilities, learning disabilities and ADHD did better than students with autism (ES 0.59). Social outcomes showed the smallest gain (ES 0.43). Interventions delivered by teachers (ES 1.60) and researchers (ES 1.58) outperformed those run by caregivers (ES 0.41).

The evidence is thin. Only one of the 16 studies was a randomized trial, 15 used pre-post designs, and none met What Works Clearinghouse standards without reservation. The authors say the results do not establish a causal effect, and prediction intervals were wide in most outcome domains.

Main points

  1. The review searched eight databases from inception to October 13, 2025, and pooled 16 English-language studies with quantitative outcomes.
  2. The pooled effect was large (Hedges' g = 1.263, 95% CI 0.950–1.576); 14 of 16 studies were significant, 13 of them with large effects and one with a small effect.
  3. Learning (ES 1.99) and cognitive (ES 1.92) outcomes improved most; social outcomes (ES 0.43) and self-efficacy (ES 0.75) improved least.
  4. By disability, students with intellectual disabilities (ES 1.56) and ADHD (ES 1.92) showed larger effects than students with autism (ES 0.59).
  5. Interventions led by teachers (ES 1.60) or researchers (ES 1.58) had larger effects than those led by caregivers (ES 0.41); the authors say this cannot be put down to implementer type alone.
  6. Only one study was a randomized trial; 15 relied on pre-post designs, and 10 of 16 failed the What Works Clearinghouse design standards.
  7. Learning disabilities were the most common target (five studies, 31.3%), followed by autism (four studies) and intellectual disabilities (three studies).
  8. Only one study reported implementer training and one reported a fidelity check, so how well the interventions were delivered is largely undocumented.

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Outcomes for students with disabilities after AI-based interventions (pre-post change)

    Hedges' g 1.263 (95% CI 0.950 to 1.576)

    16 studies

    The review’s own words
    The overall ES for AI-based interventions was large (Hedges’ g = 1.263, 95% CI = 0.950–1.576).

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

The full story

The included studies covered a wide mix of AI tools: adaptive arithmetic practice, a cognitive retraining platform, ChatGPT-based reading and conversation exercises, a text-to-speech reader, wearable glasses giving emotion cues, facial-expression feedback games, a mental health chatbot and an AI-supported swimming simulation. Sample sizes ranged from 6 to 205 participants, aged 2 to 19.3 years. In the 12 studies reporting gender, 706 participants were 55.7% male and 44.3% female.

Settings were mostly schools, classrooms and computer labs (eight studies), with three studies in centers and one at home. Session length ran from 4 to 60 minutes and duration from 1 to 48 weeks, most often 3 to 12 weeks. Researchers delivered the interventions in nine studies, teachers in three and caregivers in two.

The authors judged methodological quality using What Works Clearinghouse standards: six studies met them with reservation, 10 did not, and none met them fully. Publication-bias checks, including Egger's regression (p = 0.346), found no sign of bias, and a random-effects model was used because of significant heterogeneity (Q = 171.212).

The authors recommend randomized trials, larger moderator analyses and better documentation of training and fidelity. They also suggest that social skills, particularly for students with autism, may need human mediators rather than automation alone.

Sources

1 on file
  1. Frontiers in Public Healthdoi:10.3389/fpubh.2026.1936957
    Peer-reviewed2026-11-06

Talk it over

Four questions to start on
Who gets this benefit, and who was left out of the study or story?

Start with a question

A review of 16 studies finds large gains for students with disabilities using AIhttps://truvace.com/item/effectiveness-of-intervention-using-artificial-intelligence-for-students-with-di

  1. Who gets this benefit, and who was left out of the study or story?

    The gain A meta-analysis of 16 studies of students with disabilities found AI-based interventions linked to large improvements (Hedges' g = 1.263, 95% CI 0.950–1.576), with 14 of 16 studies showing significant effects, though only one study was randomized.

    Answer in the thread
  2. How do we know? What can this kind of evidence show, and what can’t it?

    The evidence This rests on a systematic review or meta-analysis, from Frontiers in Public Health.

    Answer in the thread
  3. What does it leave open? What else would you want to know before acting on it?

    The record’s own caveat Only one of the 16 included studies was a randomized trial and most used non-randomized pre-post designs, so the results do not establish a causal effect; the interventions and populations varied widely, and subgroup findings are exploratory.

    Answer in the thread
  4. Have you seen AI used this way where you study, work or live? Did it go the same way?

    Answer in the thread

The questions are the same on every record. The details under them are taken from this record as written.

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