<![CDATA[This study aims to explore the integration of Artificial Intelligence (AI) in data analysis for modern physics experiments, focusing on how AI-based analytical tools can improve the accuracy, efficiency, and interpretability of experimental results. The research was conducted through an experimental approach combining traditional physics data collection methods with AI-driven algorithms, including regression models, clustering techniques, and neural networks. The experiment utilized datasets from motion and optics laboratories, where sensor-based measurements were analyzed using supervised and unsupervised learning models. Data preprocessing, feature extraction, and model validation were implemented through Python-based frameworks such as TensorFlow and Scikit-learn. The results demonstrated that AI-assisted data analysis significantly enhanced the precision of measurement interpretation, reduced error margins by 15–20% compared to conventional methods and identified hidden patterns within complex datasets that were previously difficult to detect through manual analysis. Moreover, neural network models proved highly effective in predicting outcomes of nonlinear systems, particularly in optics and electromagnetism experiments. The study also revealed that the integration of AI not only accelerates data processing but also serves as an educational tool to promote computational thinking among physics students. It is recommended that modern physics laboratories adopt AI-based analytical frameworks as a standard complement to traditional methods, supported by training modules that familiarize students with data-driven experimentation. This integration is expected to strengthen the alignment between physics education and emerging technologies, ultimately fostering innovation and interdisciplinary competence among future physicists.]]>
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