Abstract: Coordination titration serves as a fundamental teaching module in analytical chemistry laboratories, extensively employed for quantitative metal ion analysis. EDTA, the most widely used titrant, exhibits pKa distribution characteristics that facilitate selective titration through pH modulation—a principle thoroughly demonstrated in classical teaching experiments. Commercial self-heating food packets contain precisely formulated calcium-aluminum binary systems (CaO and metallic Al as primary active components). This study designs a stepwise titration protocol enabling simultaneous training in coordination titration techniques under two characteristic pH conditions (alkaline and acidic media) within a single experiment, combining strong pedagogical representativeness with inherent student engagement. Addressing limitations in conventional teaching methods, we developed a digital experimental learning system through this self-heating pack component analysis case study. Guided by student-centered pedagogy, the system seamlessly integrates digital technologies with traditional titration workflows, featuring: real-time feasibility feedback for experimental design, automated titration operation and monitoring, intelligent real-time data acquisition and analysis, and comprehensive student performance evaluation. This integrated approach significantly strengthens students’ experimental competencies while enhancing logical reasoning, analytical judgment, and problem-solving skills, thereby markedly improving experimental teaching efficacy.

Key words: Digital experimental learning system, Machine vision, Machine learning, Coordination titration, Heating pack components