Abstract Structural chemistry,a core sub-discipline of chemistry,relies heavily on the Hückel Molecular Orbital(HMO) method—a classical quantum chemical approximation pivotal for elucidating the electronic structures of conjugated molecules.However,the inherent computational complexity of the HMO method presents pedagogical challenges.The rapid advancement of large language models(LLMs),such as DeepSeek and Doubao,provides significant new potential for programming assistance and problem-solving in educational settings.Based on HMO theory teaching practice,this paper explores the integration of LLMs to support structural chemistry instruction.Our research demonstrates that LLMs are effective in facilitating teaching,fostering students’ higher-order thinking skills,and promoting the transformation of structural chemistry education from a standardized to a personalized approach.
MU Jing-Lin, YIN Xiu-Ru, CHEN Zi-Yan, ZHUO Shu-Ping, ZHOU Jin. Cultivating Higher-Order Thinking in Structural Chemistry with Large Language Model Assistance:Teaching Exploration Based on HMO Theory[J]. Chinese Journal of Chemical Education, 2026, 47(14): 114-120.
