Jul 2026· International Medical Education· 0 citations· 67 references
TL;DR
Insight is provided into developing AI-ready medical education models that balance technical competence with humanistic values and factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions.
Abstract
Artificial intelligence (AI), treated in this study as an umbrella term for AI-enabled clinical and educational technologies rather than as a single platform, is reshaping medical education, including how diagnostic skills, treatment planning, and patient care are taught. This study examines AI integration in medical education through the perceptions and readiness of clinical educators. Guided by the Unified Theory of Acceptance and Use of Technology, the study explores factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In this exploratory study, semi-structured interviews were conducted with 15 clinical educators in the south-central United States who supervise third-year medical students. Findings suggested six recurring themes: the technological learning curve, the need for hands-on learning, institutional support, mentorship, preservation of human elements, and generational differences in comfort with AI. While some AI-enabled applications may support adaptive and personalized learning, educators expressed concerns about maintaining empathy, patient interaction, and human-centered care. The findings suggest that effective AI integration may require strategic institutional support, ongoing training, and pedagogical change. This study provides insight into developing AI-ready medical education models that balance technical competence with humanistic values.
Artificial intelligence (AI) is being increasingly utilized in medical education, with growing interest in its potential to enhance learning, assessment, and faculty support across undergraduate, postgraduate, and continuing medical education. While much of the literature focuses on specialty training or technology-driven innovation in isolation, family medicine represents a distinct educational domain characterized by its breadth, clinical uncertainty, multimorbidity, longitudinal care, prevention, and shared decision-making. These features present specific educational challenges and opportunities for integrating AI-supported tools. This narrative review synthesizes peer-reviewed literature on the application of AI in medical education, specifically within the context of family medicine. This review utilized PubMed and a consensus search engine to identify relevant reviews, empirical studies, and articles that address AI-supported learning, simulation training, competency-based education, workplace assessment, faculty support, and educational governance. The evidence was integrated through a narrative synthesis, guided by the Scale for Assessment of Narrative Review Articles. The literature indicates that AI can facilitate personalized learning pathways, augment simulation-based training, and aid in the integration of longitudinal assessment data, particularly in educational contexts characterized by distributed supervision and diverse learner requirements. There is a paucity of evidence concerning the application of AI in high-stakes assessments, autonomous decision-making, and its long-term impact on professional identity formation. In all areas, the successful incorporation of AI necessitates robust educational governance, faculty oversight and alignment with fundamental educational principles. AI should be considered an augmentative educational technology that can enhance but not supplant human-centered teaching, supervision, and professional judgment. Future research should prioritize the validation of educational outcomes, the clarification of ethical safeguards, and the establishment of best practices for the responsible integration of AI into family medicine education.
C. Wiedermann, Anne Wiedermann, Hendrik Reismann· Journal of Medical Education...· 0 citations
This study explores how artificial intelligence (AI) is transforming teaching practices in Education. It analyzes the impact of integration of AI-Driven support use on Physical Education student teachers (PE-ST) well-being, including workload, autonomy, and role conception during their internship period. It reports ethical challenges associated with the integration of AI in education, such as data privacy, bias, and responsibility believed to be important for a successful preparatory traineeship and training device in the professional life. However, it remains uncertain to how well Higher Education Institutions effectively equip PE-ST with pedagogical, technological, and ethical competencies required to teach and make informed decisions in AI-enhanced educational contexts. Adopting a descriptive/exploratory methods research design, the study used quantitative data collected through a structured questionnaire administered to 100 student teachers (age 21±2) before professional training. The value of Cronbach alpha index is 0.826. The quantitative findings indicate statistically significant relationships between AI-Driven support tools and conceived competence, sense of autonomy, and emotional readiness. results reveal that AI is conceived as a support pedagogical resource, particularly effective in streamlining lesson planning, instructional organization, and classroom management while fostering pedagogical innovation. The findings suggest that AI integration can bolster professional confidence and adaptability, it introduces a new set of pedagogical and ethical complexities that must be addressed to ensure successful adoption. the absence of these multifaceted competencies, AI is liable to be marginalized as a peripheral technological supplement rather than embraced as a pedagogical collaborator. Such a disconnect not only hinders innovation but also risks exacerbating professional stress and eroding the PE-ST sense of agency. Addressing the evolving requirements of modern pedagogy, this study will encourage teacher’s and special educator’s to reflect on professional training and the ethical imperatives of the profession within an increasingly automated educational landscape. It also examines the specific skills and emotional responses that emerge within AI-enhanced digital ecosystems.
Wadii Zayed, A. Fridhi, Zied Hassen et al.· Journal of Intelligent Decis...· 1 citation
Although baseline knowledge of AI among medical students and faculty members was limited, both groups demonstrated strong positive attitudes and a clear demand for further training, highlighting the importance of integrating structured AI education into medical curricula to support the responsible and effective use of emerging technologies.
Ay Sıla Çaloğlu, Halid Durna, Zeynep Naz Ergen et al.· Journal of Medical Education...· 0 citations
This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.
Hiba Hamdar· Journal of Medical Education...· 0 citations
Artificial Intelligence (AI) is presently reshaping higher education in a rapid way, due to the high engagement of students with AI. The study's aim was to explore strategies driving transformation in higher education at large and particularly dental education. This article is a narrative review adopting a structured approach for literature search, study selection, and data extraction. A comprehensive search of electronic databases, including PubMed, Scopus, Google Scholar, and ScienceDirect for studies published between 2015 and 2025 was conducted. The review is discussed under the themes of transformative education, institutional readiness, including resistance to change, ethical considerations and global perspectives in health professions, and dental education. Further, transformative education explores faculty development, including micro-credentials and lifelong learning, a safe learning environment, infrastructure, and assessment. AI is well acknowledged in both health profession education and clinical practice. While GenAI has the potential to improve creativity, feedback, and collaborative learning, the issues of plagiarism, assessment validity, and authorship transparency are also discussed. AI encounters limitations in gauging psychomotor dexterity and clinical reasoning. Ethical concerns about algorithmic prejudice, data privacy, monitoring, and the deterioration of human-centered learning are also included. To conclude, transformative strategies for institutional readiness to be considered under the domains of governance and policy, curriculum integration and faculty training, academic integrity and assessment reforms, and leadership and change management to ensure proper integration of AI and to prepare competent, adaptable, and socially responsible health professionals. Maintenance of human oversight at the center of higher education is essential, and universities should treat AI as a catalyst for modernization rather than a threat.
Prathibha Prasad, M. Amin, Elizabeth Fitriana Sari et al.· Frontiers in Dental Medicine· 0 citations
It is argued that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event, and implications are offered for technology integration, faculty development, and organizational learning in clinical education.
T. Murphy, Rob E. Carpenter· International Journal on Int...· 1 citation
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