METHODOLOGICAL PRINCIPLES OF PROFESSIONAL TRAINING OF AUTOMOTIVE INDUSTRY SPECIALISTS USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Abstract

The automotive industry is undergoing a rapid technological transformation driven by the widespread adoption of vehicle electrification, advanced driver assistance systems (ADAS), connected vehicles, and autonomous driving technologies. At the same time, artificial intelligence (AI) is becoming increasingly integrated into educational practice, creating new opportunities for the professional training of future specialists. However, the methodological foundations for applying AI technologies in the training of automotive specialists remain insufficiently developed, highlighting the need for their scientific substantiation.

The purpose of this study was to substantiate and develop the methodological foundations for the professional training of automotive specialists using artificial intelligence technologies, defining the objectives, content, forms, methods, and digital tools for their implementation in the educational process.

To achieve this purpose, a comprehensive set of complementary research methods was employed, including analysis, synthesis, systematization, and generalization of scientific literature and regulatory documents; pedagogical modeling; a survey involving 3,305 respondents; an experimental comparison of the performance of practical tasks by 112 students with and without AI technologies; and the analysis and descriptive statistics of open datasets compliant with the FAIR principles.

The findings demonstrate that the use of AI technologies improves the accuracy of practical task performance (4.09 vs. 3.51), enhances the quality of outcomes, reduces the number of errors, and shortens task completion time. At the same time, a slight decrease in students' autonomy was observed (3.83 vs. 4.25), whereas compliance with safety requirements remained virtually unchanged (4.02 vs. 4.03). These findings support the use of AI technologies as intelligent educational assistants operating under pedagogical supervision rather than as tools for fully autonomous learning.

The scientific novelty of the study lies in the fact that, for the first time, the methodological foundations for applying AI in the professional training of automotive specialists have been substantiated on the basis of empirical evidence. These foundations ensure a balance between improved learning effectiveness, the preservation of students' autonomy, and compliance with safety requirements. The proposed methodological foundations are structured into four interconnected components – goal-oriented, content, procedural, and instrumental – which specify learning topics, instructional formats, teaching methods, and digital tools.

The practical significance of the study lies in the applicability of the proposed methodological foundations for designing educational programs, curricula, teaching and learning materials, and digital educational resources for the professional training of automotive specialists. Future research should focus on experimentally validating the effectiveness of the proposed methodology across different vocational education institutions.