Organic reaction mechanisms involve electron transfer, orbital hybridization, and changes in molecular spatial configuration—microscopic processes that constitute a major teaching challenge in the vocational undergraduate Organic Chemistry course. Traditional instruction relies on blackboard work and static structural formulas, which leads students to memorize reaction equations mechanically and leaves them with insufficient spatial imagination and mechanistic reasoning. This study introduced AI-powered dynamic visualization into the classroom and developed a blended teaching model combining “theoretical lecture + AI-based dynamic mechanism demonstration + in-class virtual inquiry,” covering the full process of pre-class preview, in-class instruction, and post-class consolidation. Two parallel classes in Pharmaceutical Engineering were selected for a controlled teaching study, and teaching effectiveness was evaluated using course scores, questionnaires, and semi-structured interviews. The results show that AI visualization can concretize microscopic processes, reduce cognitive load, and significantly improve students’ higher-order abilities in mechanism derivation and stereochemical analysis. At the same time, problems were identified, including professional errors in AI-generated materials, student dependence on tools, and insufficient digital teaching competence among teachers. Accordingly, optimization strategies are proposed: building a standardized repository of mechanism visualization resources, balancing AI demonstrations with independent reasoning practice, and strengthening teacher training in AI-supported teaching. These findings provide practical reference for intelligent teaching reform in similar courses.

Taimin Wang, Bin Cheng, Wei Jiang. Cracking the Abstract Barrier: Application and Practice of AI-Powered Dynamic Visualization in Organic Chemistry Teaching. Frontiers in Educational Research (2026), Vol. 9, Issue 10: 55-59. https://doi.org/10.25236/FER.2026.091008.

[1] Xu D, Dai L, Yao WZ, et al. Exploration and application of smart teaching in organic chemistry [J]. University Chemistry, 2024, 39(12): 2412072. 

[2] Zhan P, Liu XY. Application of visualization teaching methods in chemistry courses for pharmacy-related majors [J]. University Chemistry, 2021, 36(01): 2008064. 

[3] Qin YC, Liu GP. Generative artificial intelligence empowering higher education: opportunities, risks, and governance pathways [J]. China Educational Technology, 2024(04): 45-52. 

[4] Yik B J, Dood A J. ChatGPT Convincingly Explains Organic Chemistry Reaction Mechanisms Slightly Inaccurately with High Levels of Explanation Sophistication [J]. Journal of Chemical Education, 2024,101 (5):1836-1846. 

[5] Holme T A. The Dawn of Generative Artificial Intelligence in Chemistry Education[J]. Journal of Chemical Education,2024,101(8):2957-2959. 

[6] Eriksen K, Nielsen B E, Pittelkow M. Visualizing 3D Molecular Structures Using an Augmented Reality App[J]. Journal of Chemical Education,2020,97(5):1487-1490. 

[7] Aw J K, Boellaard K C, Tan T K, et al. Interacting with Three Dimensional Molecular Structures Using an Augmented Reality Mobile App[J]. Journal of Chemical Education,2020,97(10):3877-3881. 

[8] Xiao L, Hu C, Su Y, et al. StereoLearn: An Online Platform for Learning and Automatic Grading of Organic Structure Representations[J]. Journal of Chemical Education,2026,103(9):5158-5165. 

[9] Hsieh H C, Chang H Y, Lin S Y, et al. Using AI Based Chatbots in Chemistry Learning: A Review of Applications and Challenges[J]. Journal of Chemical Education,2022,99(6):2276-2284. 

[10] Yuriev E, Orgill M, Holme T A. Generative AI in Chemistry Education: Current Progress, Pedagogical Values, and the Challenge of Rapid Evolution[J]. Journal of Chemical Education, 2025,102(9):3773-3776.