AI-Empowered Teaching Practice of Galvanic Cells Review in Senior Three Chemistry: From the Perspective of Model Cognition and Human-Machine Collaboration
YAN Jian-Hong1*, WANG Ya-Nan1, XIE Hong-Yan2
1. The Affiliated High School of Taiyuan Normal University, Taiyuan 030000, China; 2. Taiyuan Teaching and Research Center, Taiyuan 030000, China
Abstract To address the problems of fragmented model transfer, difficulties in knowledge transfer to unfamiliar situations, and superficial application of artificial intelligence (AI) in the first-round review of galvanic cells for senior three chemistry, this study took “chemical energy and electric energy” as the theme and carried out inquiry-based teaching with the core task of “designing new chemical power sources”. By constructing a teaching model of “model deconstruction and reconstruction—transfer and revision—application and reflection”, the generative AI system was positioned as a “visualization assistant”, “engineering consultant” and “socratic tutor”, running through the complete learning process of “model establishment→scheme design→AI evaluation→revision and optimization→reflection and transfer”. Teaching practice shows that this model can effectively guide students to construct a systematic cognitive model of “electrode reaction—electrode material—ionic conductor—electronic conductor”, break through the mindset of aqueous solution systems, improve students’ ability to analyze, design and evaluate batteries in real and complex situations, and promote the formation of “human-machine collaboration, people-oriented” AI literacy. This study provides an operable and replicable implementation path for developing students’ model cognition and higher-order thinking, and promoting the intelligent transformation of chemistry review teaching.
YAN Jian-Hong, WANG Ya-Nan, XIE Hong-Yan. AI-Empowered Teaching Practice of Galvanic Cells Review in Senior Three Chemistry: From the Perspective of Model Cognition and Human-Machine Collaboration[J]. Chinese Journal of Chemical Education, 2026, 47(19): 51-57.
