Evaluating Generative AI as a Real-Time Troubleshooting Assistant in Practical Electronics Training

Basic Vocational Training (FPB) students face substantial learning difficulties in electronics, which leads to a high dependency on the instructor and creates bottlenecks in practical workshops. This study aims to evaluate the effectiveness of generative artificial intelligence (AI) as a support tutor in practical electronics workshops by comparing its effects on conceptual learning, technical performance, motivation, and learner autonomy against a traditional instructional approach. To this end, a quasi-experimental design was implemented with a pilot group of eight Basic Vocational Training students, divided into an AI-assisted group (n = 4) and a traditional control group (n = 4), using pre-test and post-test assessments to measure cognitive progress. The results reveal that the use of AI allows for maintaining performance in conceptual learning despite tasks of increasing difficulty, while significantly increasing students’ motivation and perceived autonomy. Although a drastic reduction in the frequency of technical assembly errors was not recorded, AI proved to be an effective support for resolving procedural questions in real time. It is concluded that the integration of generative AI offers positive implications for Vocational Training, functioning as a supplementary tutor that fosters student independence and optimizes technical classroom dynamics.