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TRUVACE RECORD VERSION
record: TRV-2026-2744
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:17:55.080596Z
status: published
lens: trace
sector: health
headline: Can AI-driven games enhance social skills for autistic children? — A three-level meta-analysis
dek: 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…
gain_title: 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.
problem_title: 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).
trace_subject: AI-driven games for autistic children's social skills
gain_reading: 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.
gain_evidence: 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. | the intervention targeting the dual development of attention and social skills reached a large effect size ( Hedges’g = 0.963)
problem_reading: 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).
problem_evidence: the intervention effects for anxiety (DN) and interaction skills (DI) did not reach the desired level, with effect sizes significantly lower than expected | neurofeedback technology ( g  = 0.199, p  > 0.05) exhibited a low and non-significant effect size
quick_read: A three-level meta-analysis pooled 14 publications (37 effect sizes) from nine databases searched up to March 2026 and found that AI-driven games improved social skills in autistic children by a moderate amount (Hedges' g = 0.511, 95% CI 0.346 to 0.677, p < 0.001).

Results varied by goal and technology. Games targeting emotion recognition and expression gave g = 0.536 and games targeting attention plus social skills gave g = 0.963. Adaptive learning (g = 0.780) and computer vision with deep learning (g = 0.952) did best; neurofeedback (g = 0.199) was low and non-significant.

The authors call the evidence preliminary. Only 14 studies qualified, the included designs varied from randomized trials to single-group pre-post studies, and games for anxiety and real-world interaction skills fell short, suggesting skills learned on screen may not carry over to everyday life.
limitation: 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.
tag: Dual reading
key_points: The meta-analysis screened 1,326 records and kept 14 studies published 2019 to 2025, yielding 37 effect sizes. | Overall effect on social skills was g = 0.511 (95% CI 0.346 to 0.677); heterogeneity came mainly from differences between studies (I² = 52.53%). | Dropping the pre-post studies raised the pooled estimate to g = 0.552 (95% CI 0.310 to 0.795) and cut heterogeneity. | Games aimed at emotion recognition and expression gave g = 0.536; attention plus social skills gave g = 0.963. | Only AI technology type was a significant moderator (Q(5) = 7.481); adaptive learning (g = 0.780) and computer vision with deep learning (g = 0.952) did best. | Feedback (85.71%), rewards (71.43%) and level design (57.14%) were the most common of 14 gamification elements. | Medium-frequency sessions (g = 0.692) outperformed low-frequency (g = 0.413) and high-frequency (g = 0.401) ones in subgroup analysis.
rundown: The team searched Scopus, PubMed, ScienceDirect, Web of Science, IEEE Xplore, Springer Nature Link, Engineering Village, ERIC and Google Scholar. Of 1,326 records, 789 were inaccessible and 108 duplicates, and after screening 14 studies with calculable effect sizes remained. Included designs were randomized controlled trials, non-randomized controlled trials and single-group pre-post studies.

Subgroup results differed by goal: attention (g = 0.644), communication (g = 0.650) and daily living skills (g = 0.618) were moderate but their confidence intervals included or approached zero, while anxiety and real-world interaction outcomes fell short. The authors say most games train interaction only in virtual or lab settings, leaving a gap in transfer to real life.

Moderator results by technology: adaptive learning g = 0.780, computer vision with deep learning g = 0.952, machine learning g = 0.412, standalone computer vision g = 0.295 and neurofeedback g = 0.199. The authors attribute the neurofeedback result partly to noisy EEG signals and fatigue from brain-computer interface devices in children.

The authors flag risks including sensory overload from flashing visuals and loud sounds, reduced real-life contact from screen-based training, data privacy concerns from stored video and clinical records, and high development costs. They call for more high-quality randomized trials and more objective outcome measures.
sources:
- peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2026.1847426 | 2026-07-03
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