Development and Evaluation of An Artificial Intelligence-Based Methodology for Improving Physics Laboratory Instruction in Internal Affairs Academic Lyceums

Abstract

Artificial Intelligence (AI) is increasingly being integrated into educational processes, creating new opportunities for personalized learning, adaptive support, experimental data processing, formative feedback, and competency-based assessment. This study aimed to develop and evaluate an integrated AI-based methodology for improving physics laboratory instruction in Internal Affairs Academic Lyceums. The study employed a comparative pedagogical design involving 148 students from three Internal Affairs Academic Lyceums located in Namangan, Andijan, and Fergana, including 73 students in the experimental group and 75 students in the control group. The proposed methodology integrated AI-supported diagnostic assessment, pre-laboratory preparation, adaptive experimental guidance, experimental data processing, formative feedback, and competency-based assessment while maintaining students’ direct engagement with physical experimentation. Students’ laboratory learning outcomes were assessed before and after the intervention using three achievement levels: high, medium, and low. The results demonstrated statistically significant changes in achievement distributions within both the experimental group (χ²(2) = 6.53, p = .038) and the control group (χ²(2) = 7.13, p = .028). In the experimental group, the proportion of students at the low achievement level decreased from 39.7% to 20.5%, while the proportion achieving high and medium levels increased from 60.3% to 79.5%. However, the post-intervention comparison between the experimental and control groups did not reveal a statistically significant difference (χ²(2) = 0.142, p = .932). Therefore, the findings do not provide sufficient statistical evidence to establish the superiority of the AI-based methodology over conventional laboratory instruction. Nevertheless, the observed positive changes indicate the pedagogical potential of systematic AI integration in physics laboratory activities. The study suggests that AI is most effective when used as a methodological support mechanism that complements physical experimentation, teacher guidance, students’ independent reasoning, and critical evaluation of experimental evidence.