AI-Based Smart Grid Simulation for Electrical Control System Practicum: A Case Study at SMKN 1 Tambun Utara - Annuur Azizah Mini Putri Putri; Nurulita Imansari; Ivan Hanafi; C. Rudy Prihantoro; Muhammad Nurtanto (International Journal of Management Science and Information Technology, 2026)
The rapid advancement of Artificial Intelligence (AI) and smart energy technologies has created new opportunities for enhancing vocational education, particularly in electrical engineering learning environments. Critical thinking skills are essential competencies for students in Electrical Installation Engineering programs, as they support problem-solving, decision-making, and analytical reasoning during practicum activities. However, electrical control system practicums in vocational schools are often conducted using conventional instructional approaches that provide limited opportunities for students to engage in higher-order thinking processes. This study aims to investigate the potential implementation of AI-Based Smart Grid Simulation as an innovative learning medium to enhance students’ critical thinking skills in electrical control system practicum learning. A qualitative case study approach was employed at SMKN 1 Tambun Utara, Indonesia. Data were collected through classroom observations, semi-structured interviews with teachers and students, and analysis of learning documents. The collected data were analyzed using the Miles and Huberman interactive model, including data reduction, data display, and conclusion drawing. The findings indicate that current practicum activities primarily rely on jobsheets and conventional laboratory equipment, which tend to emphasize procedural execution rather than analytical problem-solving. Teachers and students expressed the need for interactive learning media capable of visualizing complex electrical systems and simulating real-world operational scenarios. The study reveals that AI-Based Smart Grid Simulation has significant potential to support critical thinking development by enabling students to analyze system conditions, evaluate alternative solutions, and make informed decisions in simulated electrical control environments. These findings provide practical insights for integrating AI-driven smart grid technologies into vocational education and contribute to the development of innovative learning strategies that align with Industry 4.0 and Education 5.0 requirements.