This study developed a GeoGebra platform-based interactive pedagogical tool focusing on plate theory to address challenges associated with abstract theory transmission, unidirectional knowledge delivery, and low student engagement in chromatography teaching in instrumental analysis courses. This study introduced an innovative methodology that encompasses theoretical model reconstruction, tool development, and teaching-chain integration that addresses the limitations of existing teaching tools, including the complex operation of professional software, restricted accessibility to web-based tools, and insufficient parameter-adjustment flexibility. An improved mathematical plate-theory model was established by incorporating mobile-phase flow rate, dead time, and phase ratio parameters. A three-tier progressive learning system (single-component simulation, multi-component simulation, and retention-time-equation derivation modules) was developed on a cloud-based computing platform. An integrated teaching chain that combined athematical modeling (AI-assisted "Doubao" derivation), interactive-parameter adjustment (multiple adjustable chromatographic parameters), and visual verification (chromatographic elution-curve simulation) was implemented. Teaching practice demonstrated that: (1) The developed tool transcends the dimensional limitations of traditional instruction, elevating the classroom task completion rate to 94% and improving the student accuracy rate for solving advanced problems to 76%. (2) The dynamic-parameter-adjustment feature significantly enhances learning engagement by enabling 85% of the students to independently use the tool in subsequent studies and experiments. (3) The AI-powered derivation and regression-analysis modules enable the interdisciplinary integration of theoretical chemistry and computational tools. The process of deriving chromatographic retention-time equations through this methodological approach proved more convincing than the current textbook practice of directly presenting conclusions. The developed innovative "theoretical-model visualizable-model-parameter adjustable-interactive-knowledge generating" model provides a new avenue for addressing teaching challenges associated with chromatography theory, and its open-source framework and modular design philosophy can offer valuable references for the digital teaching reform in analytical chemistry.
Zhang et al. (Mon,) studied this question.