Significant advancements in artificial intelligence (AI) capabilities have been demonstrated recently by OpenAI, with ChatGPT’s educational applications particularly standing out. AI assistance offers individualized learning plans and real-time replies and has been incorporated into power supply design education at every step of the experimental design process, from circuit concept analysis to experimental execution and result analysis. A paradigm shift in classroom evaluation has resulted from this integration, emphasizing students’ higher-order thinking abilities and various assessment points. We used the causal inference approach, particularly the front-door criterion, to evaluate a course and determine how instructional tactics precisely affected course effectiveness. The benefit of the causal inference approach over conventional course evaluation methods is that this approach successfully eliminates extraneous variables that might affect students’ experimental performance, accurately measuring the actual contribution of AI-assisted instructional strategies to achieve course objectives. An accurate way to evaluate the efficacy of this teaching strategy is to compare the changes in course objective achievements before and after removing confounding variables, as well as the differences in course objective achievements between instructional tactics with and without AI assistance. This validation shows how the use of AI assistance has improved students’ ability in all areas of laboratory course study, resulting in a marked improvement in the quality of education.