Artificial intelligence for affective-domain development in healthcare professions education: a systematic review

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.

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.

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.

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.

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.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420261401425, Identifier: CRD420261401425.