Intensity of Artificial Intelligence (AI) Use for Physics Learning in High Schools

Abstract

This study investigates the intensity of Artificial Intelligence utilization in Physics education at a public high school in Manado City, Indonesia. The research aimed to understand the patterns and extent of AI integration, as well as the influencing factors, through a qualitative case study approach. The methodology involved in-depth interviews, classroom observations, and document analysis with six Physics teachers, students, and school management selected via purposive sampling. Data were analyzed using Miles and Huberman's model and thematic analysis. Key parameters examined included the forms of AI usage, intensity levels (low, medium, high), frequency, depth of utilization, and both supporting (teacher digital competence, school policy support, student enthusiasm) and hindering factors (lack of formal pedagogical training, infrastructure limitations, cultural resistance). Important findings indicate that AI adoption is predominantly at a medium level, primarily utilizing interactive simulations like PhET Interactive Simulation for abstract concept visualization, which significantly enhances student engagement and comprehension. However, comprehensive AI integration for adaptive, personalized learning remains rare. While school facilities are generally supportive, teachers' digital readiness varies, and AI usage frequency averages one to two times per week, the depth of its application often remains instrumental rather than transformational. In conclusion, AI adoption in Physics learning is still nascent, not yet fully optimizing AI's potential as an adaptive intelligent learning system, largely due to variations in teacher competency, school policies, and infrastructure readiness.