Development and Implementation of AI-Driven Learning Media for Low-Power Inverter Mastery Using the ADDIE Model

Rapid technological advancements in electrical engineering education require innovative learning media that bridge the gap between theoretical concepts and practical applications, especially for complex topics such as low-power inverters. This study developed and evaluated an AI-integrated learning media for low-power inverters, utilizing the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model. The media's effectiveness was assessed through a single-group pretest-posttest design involving 40 undergraduate electrical engineering students. Validation by content and media experts deemed the media highly feasible, with an average feasibility percentage exceeding 85%. The implementation results showed a significant improvement in students' cognitive competencies, with the average test score increasing from 59.75 (pretest) to 83.20 (posttest) and an N-Gain of 0.59, which is categorized as moderate. A paired-sample t-test confirmed a statistically significant difference (p < 0.05) between the pretest and posttest results. Furthermore, students' perceptions were very positive, especially regarding material understanding, learning motivation, interactivity, and self-directed learning. The novelty of these findings demonstrates that integrating artificial intelligence into learning media is not only technically and pedagogically feasible but also effective in improving the learning outcomes of electrical engineering students. The developed media has the potential for broader application in engineering education and supports continued innovation in smart technology-based learning environments.