Error Station in Physics Experiments Using Artificial Intelligence

Abstract Accuracy and precision are fundamental aspects of physics experiments. Errors in measurement and observation can significantly affect experimental results and conclusions. Traditionally, errors in physics laboratories are detected manually by comparing experimental data with theoretical values. However, manual detection is often time-consuming and prone to human mistakes. Artificial Intelligence (AI) offers a powerful solution for identifying, analyzing, and minimizing such errors. The concept of an AI-based Error Station is designed to monitor experimental data, detect anomalies, and provide corrective feedback in real time. This research paper explores the role of Artificial Intelligence in improving the reliability and efficiency of physics experiments. It discusses the types of experimental errors, the architecture of an AI-based error detection system, its working mechanism, advantages, limitations, and future prospects in scientific laboratories and educational institutions.