Novel Thermodynamics Teaching Approach: Temperature Sensor-Based on a Real-Time Verification

The classical techniques of verifying the laws of thermodynamics have traditionally involved the use of measuring equipment like barometers, pressure gauges and thermometers. However, manual data acquisition especially, when using tables or graphs on millimeter paper can make the analysis of experimental data time-consuming. Our goal through this paper is to improve these techniques for confirming thermodynamic laws by creating affordable instructional materials and automating experiments. We employed the technique of computer-aided experimentation (CAE), which has a number of benefits, one of them being the ability to display observed events graphically in real time. In order to do this, we created a temperature sensor, whose assembly was verified using experimental methods in order to use computer-assisted experimentation to check different thermodynamic principles. Parallel to this, adding artificial intelligence (AI) to this procedure has improved its efficacy and efficiency even further. We can enhance the analysis and the interpretation of experimental data by fusing CAE with AI methods, such as machine learning algorithms. For instance, compared to manual approaches, AI algorithms are more effective at seeing patterns and trends in data, which produces quicker insights and more precise outcomes. Additionally, researchers can anticipate results and enhance experimental conditions by using AI to aid predictive modeling based on historical data. This improves the verification process and creates opportunities for more in-depth and precise investigation of intricate thermodynamic processes.