vocational-education
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- PaperComputers and Education: Artificial Intelligence17 Jun 2026
Artificial intelligence in vocational education and training: A systematic review of educational purposes, theoretical conceptualizations, and empirical effectiveness
Viola Deutscher, Herbert Thomann, Olga Zlatkin‐Troitschanskaia, Ulrike Weyland et al.
A systematic review of 26 empirical studies found that AI interventions like intelligent extended reality and tutoring systems improve procedural competence and motivation in vocational education, but evidence is limited by a lack of randomized experiments and overreliance on behaviorist designs. The authors argue that current research portrays a generalized success narrative, neglecting failure cases and learner agency, and call for future work to design AI that augments human judgment rather than replicating instruction.
Original abstract
Background Artificial intelligence (AI) increasingly shapes vocational education and training (VET). Despite its transformative potential, a systematic overview of the design characteristics and empirical effectiveness of AI-based interventions in vocational contexts remains lacking. Aim This review synthesizes empirical research on AI-supported interventions in VET, focusing on educational purposes, theoretical conceptualizations of human-AI interaction, methodological designs, and evidence of learning outcomes. Method Following PRISMA guidelines, 26 empirical studies published between 2015 and 2026 were identified through ERIC, Web of Science, and Elicit, and analyzed using a theory-informed coding scheme. Findings Intelligent Extended Reality shows consistent positive effects on procedural competence, practical skills, and learner motivation; Intelligent Tutoring Systems foster declarative and procedural knowledge; AI chatbots show promising effects on self-regulation and task performance. However, the evidence base is methodologically constrained: only five randomized experimental studies were identified. Across applications, AI is predominantly implemented through behaviorist or cognitively oriented instructional designs that emphasize drill-and-practice and adaptive feedback. In contrast, approaches fostering learner agency, critical reflection, and autonomous decision-making remain underrepresented. Conclusion Current research largely reflects a generalized „success narrative” surrounding AI in VET. Future studies should investigate failure cases, contextual moderators, and boundary conditions more systematically to develop a more differentiated understanding of the effectiveness of AI interventions. To realize the transformative potential of AI in VET, research and practice must move beyond replicating human instruction—avoiding the Turing Trap—and instead design learning environments that augment human judgment, strengthen learner agency, and support teachers in empathetic and holistic guidance.
- PaperBritish Journal of Educational Technology10 Apr 2026
Scenario‐Based AI Literacy Scale ( SAILS ): Evidence for distinct instrumental and critical‐reflective AI skills and their difference from traditional digital skills
Christian Scheibenzuber, Olga Chernikova, Johanna Vejvoda, Michael Sailer et al.
Validates a Scenario-Based AI Literacy Scale (SAILS) for vocational learners, distinguishing between instrumental AI skills (e.g., using AI for a presentation) and critical-reflective AI skills (e.g., detecting deepfakes). With 420 police trainees, the scale showed high reliability and acceptable construct validity, and AI skills were found to be related to but distinct from traditional digital skills.
Original abstract
Given the increasing integration of Artificial Intelligence (AI) into everyday life and professional contexts, it is essential to investigate learners' existing capabilities regarding AI tools to inform possible interventions to equip them with necessary AI skills, but also advance the theoretical frameworks on digital skills measurement and development. In this vein, this study aims to validate a Scenario‐Based AI Literacy Scale (SAILS) tailored to vocational learners. In this study, we differentiate between instrumental (e.g., using AI to prepare a presentation) and critical‐reflective AI skills (e.g., recognizing AI‐generated deepfake content). The scale is operationalized through a scenario‐based self‐assessment approach, ensuring a context‐driven evaluation of AI skills. We validated the AI skills scale consisting of 12 scenarios (5 instrumental and 7 critical‐reflective) with a sample of police officers in training ( N = 420), investigating reliability and validity of the scale. Additionally, we compared SAILS with a traditional digital skills scale to investigate convergent and discriminant validity. The analysis resulted in excellent reliability of the subscales (Cronbach's α = 0.88 for critical‐reflective skills and 0.89 for instrumental skills) and the entire scale as a whole (Cronbach's α = 0.92 ). A 3‐factor‐model (including critical‐reflective and instrumental AI skills subscales as well as digital skills) shows an acceptable model fit (RMSEA = 0.06, TLI = 0.93) with the standardized factor loadings ranging from 0.56 to 0.81 indicating an acceptable construct validity. AI skills were not only found to be related to more general digital skills, but also exhibiting some unique features, emphasizing that AI use requires skills which were not yet covered by digital literacy. These results provide support for an easy‐to‐use template, to be used for additional research in different contexts where objective performance measurements cannot be used and with a broader array of learners. Practitioner notes What is already known about this topic Basic AI skills are important in an ever‐growing AI‐driven world. Currently available measurements for AI literacy include self‐assessments and objective performance‐based tests. AI literacy is closely linked with digital skills. What this paper adds AI literacy can be seen as a related but separate construct from traditional digital skills. AI literacy can be measured through a scenario‐based approach combining strengths from self‐assessment and objective measures. AI literacy skills can be empirically differentiated into instrumental and critical‐reflective skills, for example, recognizing AI‐generated content. Implications for practice and/or policy The SAILS instrument provides educators with a reliable tool to assess learners' AI literacy when objective measurement is problematic. AI literacy should be regarded—and measured—in more than one dimension, that is, differentiated between instrumental and critical‐reflective skills in order to identify learners' strengths and needs. SAILS can be adapted to a vast variety of thematic contexts and is thus applicable in many different venues of education.