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TRUVACE RECORD VERSION
record: TRV-2026-2808
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:27:02.553754Z
status: published
lens: g_space
sector: education
headline: The effects of gamified AI-supported digital learning environments on personalized learning and student engagement in school education: a systematic review and meta-analysis
dek: Background Gamified, AI-enabled digital learning environments (GAI-DLEs), integrating adaptive or generative AI with game-based design, are increasingly used in school science education to support personalized learning. However, consolidated evidence on their effectiveness, implementation models, and regional distribution remains limited, particularly in Central Asia. Methods Following PRISMA 2020 guidelines, a systematic search of Scopus and complementary databases identified peer-reviewed empirical studies pub…
gain_title: A systematic review and meta-analysis of school studies found that students using gamified, AI-enabled learning environments scored markedly higher in science than peers in non-AI instruction (SMD = 1.01; 95% CI 0.69–1.33).
problem_title: (none)
trace_subject: (none)
gain_reading: A systematic review and meta-analysis of school studies found that students using gamified, AI-enabled learning environments scored markedly higher in science than peers in non-AI instruction (SMD = 1.01; 95% CI 0.69–1.33).
gain_evidence: SMD = 1.01; 95% CI: 0.69–1.33; p < 0.001
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers from Kazakhstan reviewed 81 peer-reviewed studies published between 2015 and 2025 on gamified, AI-enabled digital learning environments in school science. The meta-analysis of experimental and quasi-experimental studies found a pooled standardized mean difference of 1.01 (95% CI 0.69–1.33) in science learning outcomes versus non-AI instructional conditions.

The effect was larger in secondary education (SMD 1.12) than in primary education (SMD 0.80), and studies using adaptive or generative AI tended to report bigger effects. Evidence on student engagement was generally positive but varied widely between settings.

The evidence base is uneven geographically, with Central Asia, including Kazakhstan, substantially underrepresented. The authors call for longitudinal, theory-driven studies to test which combinations of AI features, game mechanics and classroom integration produce lasting results.
limitation: The authors say the global evidence base is geographically imbalanced, with Central Asia substantially underrepresented, and that engagement results varied substantially by context.
tag: Evidence-backed gain
key_points: The search covered six databases (Web of Science, Scopus, ERIC, PsycINFO, PubMed and IEEE Xplore) for studies from January 2015 to December 2025, starting from 1,284 records. | After deduplication, 962 unique records were screened, 150 full texts were assessed and 81 studies were included in the final synthesis. | Eligible studies had to combine gamification and AI that used learner-generated data in a single integrated approach, enrol primary or secondary students, and compare against at least one comparison group. | The pooled effect on science learning was SMD = 1.01 (95% CI 0.69–1.33; p < 0.001) against non-AI instruction. | Secondary students showed a larger effect (SMD = 1.12; 95% CI 0.69–1.55) than primary students (SMD = 0.80; 95% CI 0.35–1.25). | Studies with adaptive or generative AI systems tended to report larger effect sizes. | Engagement findings were generally positive but varied considerably by context.
rundown: The review followed PRISMA 2020 guidelines. Two reviewers screened titles, abstracts and full texts independently, with a third reviewer settling disagreements and Cohen's kappa used to check agreement between reviewers. The final search ran in January 2026.

To count, an intervention had to deliver gamification (points, badges, quests, progress indicators) and AI personalization as one integrated approach inside a digital learning environment. Studies where the two were separate components, or that covered higher education or informal learning, were excluded.

The meta-analysis used random-effects models, with subgroup analyses by education level, type of AI technology, publication period, study quality and region. The pooled estimate was 1.01 against non-AI conditions, with secondary schools at 1.12 and primary schools at 0.80.

The authors say the global evidence is geographically imbalanced, with Central Asia substantially underrepresented despite national digitalization agendas in Kazakhstan. They recommend longitudinal designs to test how AI functions, game mechanics and classroom integration combine to produce scalable outcomes.
sources:
- peer_reviewed | Frontiers in Education | https://doi.org/10.3389/feduc.2026.1754080 | 2026-04-15
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