The Utilization of Artificial Intelligence (AI) in Physics Learning for Physics Education Students

This study aims to examine the effectiveness of utilizing Artificial Intelligence (AI) in physics learning for students in the Physics Education program. The research employed a quasi-experimental design with a one-group pretest-posttest approach. The sample consisted of 24 students selected through purposive sampling. Data were collected using physics understanding tests administered before (pretest) and after (posttest) the implementation of AI-based learning. Data analysis involved paired sample t-tests to determine significant differences between pretest and posttest scores, as well as N-Gain calculations to measure the improvement in student learning outcomes. The results revealed a significant improvement in students' physics comprehension after applying AI-based learning, with average pretest and posttest scores of 62.08 and 81.25, respectively. The paired t-test yielded a t-value of 17.82 with a p-value < 0.001, indicating a highly significant difference. The average N-Gain score of 0.53 reflects a moderate improvement in learning outcomes. These findings suggest that integrating AI in physics education can enhance both the learning process and student achievement. This study recommends that educational institutions and instructors adopt AI technology in teaching methods to optimize students competency attainment.