Artificial Intelligence and Mechanical Engineering in Greek Vocational High Schools: Educational Perspectives and Institutional Barriers

This scoping review investigates the potential and challenges of integrating Generative Artificial Intelligence (GenAI) into Vocational Education and Training, with a specific focus on the Mechanical Engineering sector of Greek Vocational High Schools (EPA.L.). The purpose of this study is to analyze how AI can transform the teaching of mechanical engineering courses, bridging the gap between theoretical knowledge and laboratory practice in the context of Industry 4.0. The methodology involved a literature review of 26 scientific sources (2016-2026), utilizing thematic analysis guided by theoretical frameworks such as the Technology Acceptance Model (TAM) and teacher self-efficacy. Key findings indicate that the use of specialized, knowledge-enhanced Large Language Models (LLMs) significantly improves the structural integrity and technical accuracy of instructional materials, effectively reducing teacher preparation time. However, despite a high perceived usefulness, the adoption of AI by educators in public schools is severely hindered by the lack of adequate laboratory infrastructure, the absence of discipline-specific training, and heightened AI anxiety regarding ethical risks. In conclusion, the transition to an AI-enhanced school laboratory necessitates immediate upgrades to equipment and a shift from general ICT training to targeted, specialty-specific professional development programs, ultimately empowering teachers to become designers of digital learning experiences.