Frontiers in medicine · 2026
Artificial intelligence for affective-domain development in healthcare professions education: a systematic review.
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Ismail Noor Akmal Shareela, Zafira Azizah
Аннотация
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261401425, Identifier: CRD420261401425.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261401425, Identifier: CRD420261401425.