Results of the Survey "The Use of Artificial Intelligence Technologies in the Vocational Training of Students (Automation and Electronics; Electric Power Engineering; Electrical Engineering)"

This dataset contains the preliminary results of an anonymous survey conducted in 2026. The survey covered respondents from the vocational education system and focused on analyzing the intensity of use, key areas of application, and overall effectiveness of artificial intelligence tools in the educational environment and professional activities within the following fields of study: Automation and Electronics; Electrical Power Engineering; Electrical Engineering. The data obtained allow for an assessment of the subjective attitudes of participants in the educational process toward innovations, as well as the identification of the main risks and barriers that arise during the implementation of AI. The structure of the sample, totaling 293 individuals, is representative of various categories of participants. The largest group consists of students – 203 individuals (69.3% of the total). The share of vocational training instructors is 48 people (16.4%), and that of teachers is 36 people (12.3%). The remaining 6 participants (2.0%) belong to other professional categories. This distribution allows for a comparison of the views of both students and those who facilitate educational and practical processes. The research methodology was based on the use of the Google Forms online tool. The survey consisted of 24 questions, grouped into six strategic sections. These covered the respondents’ demographic profile and digital experience, their direct practice of using AI, and an assessment of its impact on a Likert scale from 1 to 5. Particular attention was paid to the comparative effectiveness of working with and without AI, the analysis of potential threats, and the identification of prospects for the further integration of these technologies into educational programs. The prospects for analyzing this data are quite broad and hold scientific significance. In particular, they provide a foundation for conducting a correlation analysis between users’ level of digital literacy and their degree of trust in AI outcomes. In addition, the dataset allows for comparing attitudes toward technology across different age and professional groups, identifying priority areas for the implementation of generative models, and developing predictive models for improving the quality of professional training through the integration of intelligent tools.