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 databases and included in the analysis. Using a random-effects model, the meta-analysis indicates a moderate positive association between AI-supported assessment systems and skill-related learning outcomes (Hedges’ g = 0.72), alongside substantial heterogeneity across study designs, outcome measures, and implementation contexts. Subgroup analyses suggest variation across regional and institutional settings, which should be interpreted cautiously given small sample sizes and diverse methodological approaches. Based on the synthesized evidence, the study proposes a conceptual AI-supported skill assessment framework that distinguishes empirically grounded components from forward-looking extensions related to generative AI. Rather than offering prescriptive solutions, the framework provides an evidence-informed baseline to support future research, system design, and responsible integration of generative AI in higher education assessment. Overall, the findings highlight both the potential and the current empirical limitations of AI-enabled assessment, underscoring the need for more robust, theory-informed, and transparent studies as generative AI applications continue to evolve.
Sun et al. (2026) studied this question.