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AI literacy, generative AI, and pedagogical decision-making in workforce training: A scoping review toward a conceptual framework

Sep 2026 · Journal of Vocational, Informatics and Computer Education · 0 citations · 39 references

Abstract

Purpose – Generative Artificial Intelligence (Generative AI) is increasingly used in education, yet the relationship among AI literacy, Generative AI utilization, and pedagogical decision-making remains insufficiently examined. This scoping review maps the evidence and develops an evidence-informed conceptual proposition, with attention to vocational and workforce-oriented education.Methods – Following Joanna Briggs Institute guidance and PRISMA-ScR, a systematic Scopus search identified 261 records. After applying publication-year, article type, and language criteria, 88 records were screened. Full-text assessment resulted in 21 included studies. Evidence was charted by study characteristics, educational context, participants, focal constructs, AI applications, pedagogical practices, and findings.Findings – AI literacy is a professional capability involving understanding, critical evaluation, and responsible engagement with AI. Generative AI was mainly used for instructional planning, learning material development, assessment, feedback, and professional reflection. Instructors generally retain the responsibility of evaluating, adapting, or rejecting AI-generated outputs. However, none of the 21 studies empirically tested an integrated relationship among all three constructs, and the evidence did not establish any causal, mediating, or moderating effects.Research Implications – The review proposes a conceptual framework distinguishing AI literacy as an instructor capability, generative AI utilization as instructional behavior, and pedagogical decision-making as professional judgment. The framework is an evidence-informed proposition that requires empirical validation, particularly in vocational and workforce-training settings.Originality – This review integrates three dimensions that are often examined separately and emphasizes human evaluation of AI-generated outputs in AI-supported teaching.

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