70/100

Moderate evidence

Trace: a gain and a problemPublished recordHealthThe Trace

Can AI-driven games enhance social skills for autistic children? — A three-level meta-analysis

Truvace record
TRV-2026-2744raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
sha256
dadcc8cd1e04…check it

Background Gamified interventions for autistic children are becoming increasingly diverse, enhancing their presence, immersion, and sense of accomplishment to improve various social skill indicators. Existing systematic reviews have primarily evaluated intervention effectiveness from the perspectives of game design and intervention objectives. However, these findings are inconsistent and fail to inspect specific improvements in social skills among autistic children. Methods To address this gap, this study adopts…

61Gain side

How the two sides scoreClose call

57Problem side

The gain

A meta-analysis of 14 studies (37 effect sizes) found AI-driven games improved autistic children's social skills by a moderate amount (Hedges' g = 0.511, 95% CI 0.346 to 0.677), with the largest gain (g = 0.963) for games targeting attention and social skills together.

The problem

In the same meta-analysis of 14 AI-game studies for autistic children, games aimed at anxiety and at real-world interaction skills showed effects well below the overall moderate gain, and neurofeedback-based games had a small, non-significant effect (g = 0.199).

A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.

What the scores mean

G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.

P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.

What this doesn’t fix

Only 14 studies were included, designs ranged from randomized trials to single-group pre-post studies, heterogeneity was significant, and the authors describe the moderation analysis as exploratory and underpowered; behavioral observation measures may carry assessor bias.

Sources

1 on file
  1. Frontiers in Psychologydoi:10.3389/fpsyg.2026.1847426
    Peer-reviewed2026-07-03

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.

  • Emotion recognition and expression in autistic children after AI-driven games

    Hedges' g 0.536 (95% CI 0.221 to 0.850)

    14 studies · search to March 2026

    The review’s own words
    An effect size analysis for different intervention targets showed that, with emotional recognition and expression (DE) as the baseline, the effect size was g = 0.536 (95% CI: 0.221, 0.850, p < 0.01), indicating that AI-driven games produce a moderate and significant effect on this target.
  • Social skills of autistic children after AI-driven gamified interventions

    Hedges' g 0.511 (95% CI 0.346 to 0.677)

    14 studies · search to March 2026

    The review’s own words
    The overall effect size of AI-driven gamified interventions on the social skills of autistic children was Hedges’g = 0.511 (95% CI [0.346, 0.677], p < 0.001), indicating a moderate positive effect.
  • Social skills of autistic children after AI-driven games, excluding pre-post studies

    Hedges' g 0.552 (95% CI 0.310 to 0.795)

    14 studies · search to March 2026

    The review’s own words
    The pooled effect size estimate changed from 0.511 (95% CI: 0.346–0.6767, p < 0.001) to 0.552 (95% CI: 0.310–0.795, p = 0.0001).
  • Combined attention and social skills in autistic children after AI-driven games

    Hedges' g 0.963 (95% CI 0.165 to 1.760)

    14 studies · search to March 2026

    The review’s own words
    Studies targeting the dual development of attention and social skills (DA + DS) showed a large effect size ( g = 0.963, 95% CI [0.165, 1.760]).

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

Cite this record

v1

Truvace Impact Record TRV-2026-2744, v1: “Can AI-driven games enhance social skills for autistic children? — A three-level meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2744 (accessed on the day you copy it). sha256 dadcc8cd1e041b94…

Change history

1 version

Every change made to this record since it was first published, and why. Nothing has changed since it went up.

  1. Publishedv1dadcc8cd1e04…

    Certified into the record

Check this record hasn’t been altered

Check this record hasn’t been altered
How to check it yourself, without trusting this page

Download any version from /api/record/TRV-2026-2744 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.