{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:19:47Z","timestamp":1787663987635,"version":"build-2736575974"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>Artificial intelligence (AI) is increasingly being explored in infection prevention and control (IPC) education as a means of addressing limitations of conventional training, including limited scalability, delayed feedback, and subjective competency assessment. This mini review summarizes emerging applications of AI-enabled technologies in IPC education and examines their potential educational benefits, limitations, and implementation considerations. Current evidence includes applications of computer vision for behavioral assessment, AI-integrated virtual and extended reality for immersive and adaptive learning, and large language models for knowledge support and interactive learning. Available studies suggest that AI-assisted interventions may improve procedural performance, learner engagement, feedback efficiency, and selected measures of knowledge or self-efficacy. However, the evidence remains heterogeneous, with substantial variation in AI modalities, learner populations, study designs, outcome measures, and follow-up periods. Many studies are based on small samples or single-center settings and primarily evaluate short-term educational outcomes, limiting conclusions regarding sustained competency acquisition or translation into clinical practice. Evidence linking AI-assisted IPC education to reductions in healthcare-associated infections remains particularly limited. Important challenges also include workforce readiness, infrastructure and interoperability, privacy and governance, and potential cognitive burden associated with immersive technologies. Overall, AI shows promise as an enabling component of adaptive, data-supported, and feedback-oriented IPC education, but its educational and clinical value requires further validation through larger, multicenter, longitudinal, and implementation-focused studies.<\/jats:p>","DOI":"10.3389\/fpubh.2026.1927532","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T12:31:27Z","timestamp":1787661087000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in infection prevention and control education: toward intelligent, real-time clinical learning systems"],"prefix":"10.3389","volume":"14","author":[{"given":"Yu-long","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Hospital-Acquired Infection Control, Peking University People's Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Quality Management, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University","place":["Weihai, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Hospital Infection Management, The Second Affiliated Hospital of Inner Mongolia Medical University","place":["Hohhot, Inner Mongolia, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiao","family":"Shan","sequence":"additional","affiliation":[{"name":"Department of Hospital-Acquired Infection Control, Beijing Jishuitan Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-yuan","family":"Bao","sequence":"additional","affiliation":[{"name":"Medical Informatics Center, Institute of Advanced Clinical Medicine, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Jin","sequence":"additional","affiliation":[{"name":"Medical Informatics Center, Institute of Advanced Clinical Medicine, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Huai","sequence":"additional","affiliation":[{"name":"Department of Emergency, Peking University Third Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-cheng","family":"Jin","sequence":"additional","affiliation":[{"name":"School of General Studies, Columbia University","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-xi","family":"Jin","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Science, Northeastern University","place":["Seattle, WA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Graduate School of Medicine, Kyoto University","place":["Kyoto, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-qiu","family":"Kuang","sequence":"additional","affiliation":[{"name":"Department of Hospital-Acquired Infection Control, Peking University People's Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.ajic.2024.08.015","article-title":"Infection preventionists' current and preferred training strategies and tools","volume":"53","author":"Rebmann","year":"2025","journal-title":"Am J Infect Control."},{"key":"B2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s40506-020-00216-7","article-title":"Using artificial intelligence in infection prevention","volume":"12","author":"Fitzpatrick","year":"2020","journal-title":"Curr Treat Options Infect Dis."},{"key":"B3","doi-asserted-by":"publisher","first-page":"e317","DOI":"10.1017\/ash.2025.10191","article-title":"Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist","volume":"5","author":"Gastaldi","year":"2025","journal-title":"Antimicrob Steward Healthc Epidemiol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1109\/VRW62533.2024.00043","article-title":"Virtual reality-based donning and doffing simulator","author":"Kakdas","year":"2024","journal-title":"2024 IEEE Conf. 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