Abstract: In fundamental chemistry courses at the university level (e.g., Inorganic Chemistry, Physical Chemistry), the “conclusion-first” teaching model is characterized by presenting core conclusions directly while omitting complex derivation processes. This approach often arises as a practical compromise due to constraints such as limited class hours and the hierarchical nature of disciplinary knowledge. However, it tends to result in superficial learning, where students grasp “what” but not “why”, thereby impeding deep comprehension and the development of scientific thinking. Against the backdrop of deepening integration of artificial intelligence (AI) and digital education, this study focuses on how digital empowerment can help students understand the logical foundations of pre-presented conclusions and reinforce interdisciplinary knowledge systems. It systematically examines the root causes and cognitive challenges associated with the “conclusion-first” model in university fundamental chemistry courses and proposes a four-pronged optimization framework: Conclusion Tracing, Logic Visualization, Inquiry Verification, and System Anchoring. By leveraging digital strategies—such as virtual simulations, dynamic logic deduction, and cross-course interactive inquiry—this approach restores the omitted deductive logic and theoretical context inherent in the “conclusion-first” model. Consequently, it facilitates a shift from passive memorization of conclusions to active knowledge construction. Using the teaching of “Atomic Structure” and “Chemical Thermodynamics” (Physical Chemistry) as case studies, this research demonstrates the adaptability and efficacy of the proposed framework across multiple courses, offering a transferable model for reforming foundational chemistry education in the AI era.

Key words: Digital empowerment, University chemistry, Conclusion-first, Teaching optimization, Visual teaching, Critical thinking