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Artificial intelligence in vocational education and training: A systematic review of educational purposes, theoretical conceptualizations, and empirical effectiveness

● high · 2026-08-01professional-training, efficacy-study, intelligent-tutoring, generative-ai, adaptive-learning, simulation-based-learning, regulation, constructivist

Authors: Viola Deutscher, Herbert Thomann, Olga Zlatkin-Troitschanskaia, Ulrike Weyland, Stephan Abele, Amory H. Danek, Samuel Greiff, Andreas Rausch, Susan Seeber, Jürgen Seifried, Esther Winther Source: Computers and Education: Artificial Intelligence, Vol 11, 100628 — Open Access (CC BY 4.0)

First systematic review of AI in vocational education and training, identifying 26 empirical studies (2015–2026) via ERIC, Web of Science, and Elicit, analyzed with a theory-informed coding scheme under PRISMA guidelines.

Key Findings

  • This is the first systematic review of AI in vocational education and training (VET), identifying 26 empirical studies published between 2015 and 2026 through ERIC, Web of Science, and Elicit, analyzed with a theory-informed coding scheme following PRISMA guidelines.
  • The corpus spans 9 technical-domain studies, 3 in health, 5 in business administration and services, and 9 domain-general studies; settings were 6 classroom, 8 online, 4 blended, and 8 simulation-based — with no study conducted in workplace settings despite VET's work-based character.
  • Research is geographically concentrated: 17 of 26 studies originated in Asia (China, South Korea, Singapore, Taiwan, Thailand, Indonesia), with the remainder from European contexts (Germany, the Netherlands, Norway) and isolated contributions (New Zealand, Saudi Arabia, Turkey); the field is also fragmented, as only 9 studies shared at least one reference and none cited each other directly.
  • Intelligent Extended Reality (XR) shows consistent positive effects on procedural competence, practical skills, and learner motivation — e.g., a randomized pre-post comparison (iXR n = 14 vs. traditional group task n = 15) found both groups gained knowledge but gains were significantly higher with iXR.
  • Intelligent Tutoring Systems foster declarative and procedural knowledge, while AI chatbots show promising effects on self-regulation and task performance — including a grounded-theory study of 408 polytechnic students tracing a self-regulatory arc (goal setting → performative interaction → reflection), though the only randomized chatbot trial (n = 50, posttest-only) reported advantages on seven competencies without effect sizes, pretests, or baseline checks.
  • The evidence base is methodologically constrained: only five randomized experimental studies were identified among the 26, 21 of 26 rely on pre-experimental or quasi-experimental designs, and most measure outcomes immediately after the intervention; affective and meta-cognitive outcomes rest predominantly on self-report, raising novelty-effect concerns.
  • Only three studies explored AI-empowered designs that grant learners an active role; meta-cognitive goals such as self-regulated learning are frequently espoused but rarely implemented through genuinely learner-empowered systems.
  • Across applications, AI is predominantly implemented through behaviorist or cognitively oriented instructional designs that emphasize drill-and-practice and adaptive feedback, while approaches fostering learner agency, critical reflection, and autonomous decision-making remain underrepresented.
  • Current research largely reflects a generalized "success narrative"; the authors call for future studies of failure cases, contextual moderators, and boundary conditions to develop a more differentiated understanding of effectiveness.

    • Study Design & Method

    This is a PRISMA-guided systematic review of 26 empirical studies (2015–2026) identified through ERIC, Web of Science, and Elicit, with Scopus added as a supplementary domain-specific database, and coded with a theory-informed scheme that distinguishes AI-directed, AI-supported, and AI-empowered human-AI interaction paradigms and underlying learning theories; all included studies were independently double-coded, with coding documented in a publicly available dataset (Appendix 2). Methodologically, quantitative designs dominate (14 studies, surveys most frequent), followed by mixed methods (10) and two qualitative case studies; sample sizes range from 9–15 VET learners (qualitative) to 20–3,518 (quantitative). Among the quantitative studies, 4 employ quasi-experimental approaches and 4 use randomized experiments per the methodological breakdown, and 2 rely exclusively on self-report while 5 use only objective measures (performance tests or log data).

    The Constructivist Paradox and the Turing Trap

    The review documents a notable paradox: constructivist theories are espoused in VET discourse while behaviorist AI implementations dominate in practice. The authors warn against an educational "Turing Trap" — the danger of using AI to replicate human instruction rather than to augment human judgment. Realizing the transformative potential of AI in VET, they argue, requires learning environments that augment human judgment, strengthen learner agency, and support teachers, rather than systems that merely automate existing instructional patterns.

    Implications for AI in Education

    For educators and developers, the review offers a map of what the evidence currently supports: XR for procedural and practical skills, ITS for declarative and procedural knowledge, and chatbots for self-regulation support — with the most consistent benefits emerging when AI augments authentic, practice-proximal environments, especially Intelligent Tutoring-style simulations in technical domains. The scarcity of randomized experiments flags the need for stronger causal designs, and the dominance of drill-and-practice implementations suggests that Professional Training contexts are under-serving the learner-agency goals that VET espouses; delayed post-tests, objective performance-based assessments, and analyses of transfer to workplace contexts are largely absent from the literature. The Turing Trap framing connects directly to Research Methods AIED debates and to Constructivist design commitments, and the call for reporting failure cases is a useful corrective to the field's prevailing success narrative — while the heavy reliance on self-report for Self Regulated Learning outcomes should temper claims about chatbots' regulatory effects.

    Limitations

    The review restricted its search to English-language, peer-reviewed journal articles, likely excluding grey literature and non-English work — a notable gap given the applied, project-based, and often locally documented nature of VET interventions. The database-dependent search may underrepresent regions with distinct publication cultures (partly explaining the Asia/Europe concentration), and the use of Elicit as an AI-assisted discovery tool constrains full reproducibility because retrieved outcomes depend on probabilistic ranking mechanisms and database coverage changes. Finally, the heterogeneity of included studies and frequent lack of transparency about AI implementations and instructional designs required interpretive judgment in coding, despite double-coding and consensus-based resolution of discrepancies.

    Connected Concepts

  • Professional Training
  • Research Methods AIED
  • Intelligent Tutoring
  • Lifelong Learning
  • Self Regulated Learning
  • Constructivist
  • Affective Tutoring
  • Open Source

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    • Citation

    Deutscher, V., Thomann, H., Zlatkin-Troitschanskaia, O., Weyland, U., Abele, S., Danek, A. H., Greiff, S., Rausch, A., Seeber, S., Seifried, J., & Winther, E. (2026). Artificial intelligence in vocational education and training: A systematic review of educational purposes, theoretical conceptualizations, and empirical effectiveness.