An Analysis of the Application of Artificial Intelligence in Technical and Vocational Education

Document Type : Original Article

Authors

Ph.D. in Urbanism, Faculty of Architecture and Urbanism, Shahid Beheshti University, Tehran, Iran.

10.22091/jrim.2026.13806.1373

Abstract

With the rapid expansion of emerging technologies-especially artificial intelligence (AI)-technical and vocational education and training (TVET) systems are confronting profound opportunities and challenges. As the labor market increasingly requires advanced, technology-based skills, examining the role and capacities of AI in enhancing the quality, efficiency, and personalization of skills training has become imperative. Despite scattered studies in this field, a gap remains in deep understanding of the key themes and the conceptual relationships between AI’s technical components and the contextual requirements of Iran’s TVET. Accordingly, the present study aims to identify and analyze the underlying dimensions and latent themes related to AI applications in technical and vocational education.
This research employed a qualitative approach using thematic analysis. Data were collected through semi-structured interviews with 14 experts in AI, skills training, and educational technology. The data were coded and analyzed with MAXQDA, and EndNote was used to organize the scholarly sources. In total, the analysis yielded 47 initial codes, 8 central themes, and a single selective (overarching) theme.
The findings indicate that AI-through pillars such as personalized learning, educational data analytics, intelligent interactions, and emerging technologies, and with due consideration of cultural, ethical, and infrastructural challenges-can play a significant role in transforming technical and vocational education. Ultimately, by proposing a nested conceptual model, the study explicates the relationships between macro- and micro-level themes and offers an actionable framework for designing AI-enabled instructional programs.

Keywords

References

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History

  • Receive Date: 05 September 2025
  • Revise Date: 25 December 2025
  • Accept Date: 31 January 2026

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