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Research landscape and future pathways of “artificial intelligence + university chemistry education”: a CiteSpace-based bibliometric analysis

Jiawei Xue, Yanqing Fan, Xiaojuan Li, Jiajia Li, Jing Zeng   

  1. College of Chemistry and Chemical Engineering, Xinjiang Normal University, Urumqi 830054, Xinjiang Uygur Autonomous Region, China
  • Received:2026-02-27 Accepted:2026-04-14
  • Contact: Jing Zeng E-mail:zengjing800111@163.com

Abstract: A CiteSpace-based bibliometric analysis reveals that domestic research on “artificial intelligence + university chemistry education” has established a teaching-reform-centered framework encomp-assing the entire instructional process, with significant advancements in discipline-specific applications.In contrast, international research prioritizes cognitive construction and inquiry design, incorporating flipped laboratories and data science integration. Future research directions should enhance interdisciplinary collaboration, leverage international insights to develop discipline-specific intelligent teaching models, conduct longitudinal empirical studies, and foster deeper educational integration from the student perspective.

Key words: Digital transformation, Higher education, Artificial intelligence, Chemistry education, Teaching reform