Abstract: A robust teaching platform is crucial for enhancing educational quality. In traditional chemical titration experiments, the accuracy heavily relies on operators’ experience and skill, leading to long detection cycles and significant human errors. To diversify teaching methods and empower students with various titration techniques, this study introduces an automatic titration system utilizing intelligent algorithms into university chemistry practical teaching. The developed instrument employs real-time chemical reaction image capture and neural network algorithms for instant titration endpoint determination. It offers high accuracy, reliability, ease of operation, and traceable experimental data. The integration of intelligent titration instruments not only enhances the efficiency and precision of student experiments while reducing human errors, but also optimally allocates resources for developing both practical skills and theoretical understanding. Moreover, it facilitates independent evaluation and monitoring of teaching effectiveness, enabling timely adjustments in teaching strategies to effectively elevate teaching quality.

Key words: Titration analysis, Practical teaching, Neural networks, Intelligent