Development and Implementation of a Cloud-Based HPLC Teaching Platform Enhanced by Artificial Intelligence

Abstract

High-performance liquid chromatography (HPLC) is a fundamental component of undergraduate chemistry curricula. However, the high cost of instrumentation and limited laboratory hours often restrict students’ hands-on access. This lack of practical experience hinders their ability to deeply understand HPLC principles, master instrument operation, and develop problem-solving skills. To address these challenges, we developed an artificial intelligence (AI)-powered cloud HPLC experiment platform, termed C-HPLC. Specifically, a deep learning model was constructed to predict compound retention time (RT); then, a refined 3D model was established to illustrate the HPLC instrument structure, and a web-based architecture was implemented to enable cross-platform interoperability. C-HPLC allows students to freely explore mixture separation under varied experimental parameters through accurate RT prediction. The platform is accessible on multiple devices, including smartphones, tablets, and computers, and provides online visualization of HPLC instrument structures and operating principles. Through online exploratory learning, students can deepen their understanding of HPLC principles and instrument configurations. Subsequent hands-on laboratory experiments reinforced and validated this knowledge, leading to an improvement in the effectiveness of HPLC experimental teaching. Usage data and questionnaire results demonstrated the reliability and usability of the platform. Among 105 trial users, 94 reported an enhanced understanding of HPLC, and 100 expressed overall satisfaction. C-HPLC offers a promising solution to current challenges in HPLC experimental teaching and supports the achievement of instructional objectives.