Abstract: Inorganic Chemistry is one of the four core chemistry courses required for students majoring in chemical engineering, chemistry, materials science, and related fields in higher education. However, it has been observed in teaching practice that students often struggle to establish effective connections between prior and subsequent knowledge, leading to fragmented learning and an inability to apply previous ly acquired knowledge to guide later studies. Taking the teaching of the Nernst equation as an example, this study constructed a three-tier knowledge network—“basic knowledge, core derivation, and comprehensive application”—using a knowledge graph, and developed an AI-powered intelligent question-answering system. Additionally, intelligent learning profiling was integrated into the teaching process. Practice demonstrated that this approach not only improved students’ knowledge coherence and application skills but also shifted teaching interventions from experience-based intuition to a data-driven paradigm.

Key words: Knowledge graph, AI diagnosis, AI empowerment, Inorganic chemistry, Nernst equation