{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:03:38Z","timestamp":1785413018325,"version":"3.56.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is increasingly integrated into health professions education through generative AI, chatbots, virtual patients, automated feedback, and AI-enhanced simulation. While most discussions focus on cognitive and technical learning, the role of AI in affective-domain development remains less clearly understood. Affective-domain learning is essential in healthcare education because it shapes communication, empathy, professionalism, reflection, self-awareness, and patient-centered practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This systematic review examined how AI-based educational tools and interventions have been used to support affective-domain development among healthcare professional students and trainees.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This review followed PRISMA 2020 guidance. Searches were conducted in PubMed, Scopus, Web of Science, SpringerLink, ScienceDirect, Google Scholar, Wiley Online Library, and IEEE Xplore for English-language empirical studies published between January 2010 and May 2026. Eligible studies involved healthcare professional learners, used AI-based educational tools for teaching, simulation, feedback, communication training, reflection, or assessment, and reported at least one affective-domain-related outcome. Methodological quality was appraised using Joanna Briggs Institute critical appraisal tools as the primary appraisal framework, with CASP used only as a supplementary interpretive aid for studies with qualitative or mixed-methods components. Findings were synthesized narratively because of heterogeneity in study designs, AI interventions, and affective-domain outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Seventeen studies were included involving undergraduate and post-graduate healthcare learners. AI tools were primarily used for communication training, virtual patient interaction, reflective feedback, breaking-bad-news simulation, medical interview rehearsal, and OSCE-style assessment. Most studies reported improvements in learner confidence, self-efficacy, reflective engagement, perceived communication skills, or simulation-based assessment performance. AI was valued for providing repeated low-stakes practice, immediate structured feedback, accessibility, and scalability. However, evidence supporting deeper affective outcomes such as empathy, emotional responsiveness, relational authenticity, and non-verbal communication remained limited. Several studies also highlighted concerns regarding emotional realism, over-reliance on AI, and reduced interpersonal authenticity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI may support selected aspects of affective-domain learning, particularly communication rehearsal, reflective learning, and formative feedback. However, AI should complement rather than replace human-facilitated teaching, as educators remain essential for fostering empathy, ethical judgement, professional identity formation, and relational care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD420261401425<\/jats:ext-link>\n                      , Identifier: CRD420261401425.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1894820","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:10:29Z","timestamp":1785409829000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for affective-domain development in healthcare professions education: a systematic review"],"prefix":"10.3389","volume":"13","author":[{"given":"Noor Akmal Shareela","family":"Ismail","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Universiti Kebangsaan Malaysia","place":["Kuala Lumpur, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azizah","family":"Zafira","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Universiti Kebangsaan Malaysia","place":["Kuala Lumpur, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"E146","DOI":"10.1001\/amajethics.2019.146","article-title":"Reimagining medical education in the age of AI","volume":"21","author":"Wartman","year":"2019","journal-title":"AMA J Ethics."},{"key":"B2","doi-asserted-by":"publisher","first-page":"e16048","DOI":"10.2196\/16048","article-title":"Introducing artificial intelligence training in 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