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TRUVACE RECORD VERSION
record: TRV-2026-2819
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:29:07.353859Z
status: published
lens: g_space
sector: education
headline: AI-Enhanced Skill Assessment in Higher Vocational Education: A Systematic Review and Meta-Analysis
dek: This study synthesizes empirical evidence on AI-supported skill assessment systems in higher vocational education through a systematic review and meta-analysis. Despite growing interest in generative AI within higher education, empirical research on AI-enabled assessment remains fragmented and methodologically uneven, particularly in vocational contexts. Following PRISMA 2020 guidelines, 27 peer-reviewed empirical studies published between 2010 and 2024 were identified from major international and Chinese databa…
gain_title: A meta-analysis of 27 peer-reviewed studies found AI-supported skill assessment in higher vocational education was associated with moderately better skill-related learning outcomes (Hedges' g = 0.72).
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 27 peer-reviewed studies found AI-supported skill assessment in higher vocational education was associated with moderately better skill-related learning outcomes (Hedges' g = 0.72).
gain_evidence: the meta-analysis indicates a moderate positive association between AI-supported assessment systems and skill-related learning outcomes (Hedges’ g = 0.72)
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis pooled 27 peer-reviewed empirical studies, published between 2010 and 2024, on AI-supported skill assessment in higher vocational education. A random-effects model found a moderate positive association with skill-related learning outcomes, with a Hedges' g of 0.72.

The studies were drawn from major international and Chinese databases under PRISMA 2020 guidelines. Results varied considerably across study designs, outcome measures and implementation contexts, and the authors say regional and institutional subgroup differences should be read cautiously because of small samples and mixed methods.

From the evidence the authors propose a conceptual framework for AI-supported skill assessment. It separates empirically grounded components from forward-looking extensions tied to generative AI, and is meant as a baseline for future research and system design rather than a prescription.
limitation: The authors report substantial heterogeneity across study designs, outcome measures and contexts, and say subgroup variation should be interpreted cautiously given small sample sizes and diverse methods.
tag: Evidence-backed gain
key_points: The review followed PRISMA 2020 guidelines and included 27 peer-reviewed empirical studies from 2010 to 2024. | Studies came from major international and Chinese databases. | A random-effects meta-analysis found a pooled effect of Hedges' g = 0.72 for skill-related learning outcomes. | The authors describe this as a moderate positive association, not proof of cause. | Heterogeneity was substantial across study designs, outcome measures and implementation contexts. | Subgroup analyses suggested differences by region and institution, but small samples limit what can be concluded. | The proposed framework separates empirically grounded components from generative AI extensions that are still forward-looking.
rundown: The authors say research on AI-enabled assessment is fragmented and methodologically uneven, particularly in vocational settings, despite growing interest in generative AI in higher education. This review sought to consolidate the empirical evidence.

Twenty-seven studies met the inclusion criteria. The random-effects model gave a pooled Hedges' g of 0.72 for skill-related outcomes, a moderate positive association, though effects varied widely between studies.

Subgroup analyses suggested differences across regional and institutional settings. The authors caution that small sample sizes and diverse methods limit interpretation of these differences.

The conceptual framework they propose is evidence-informed but not prescriptive. It is intended to support future research, system design and responsible integration of generative AI in assessment, and the authors call for more theory-informed and transparent studies.
sources:
- peer_reviewed | Informatics | https://doi.org/10.3390/informatics13020020 | 2026-01-28
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