Journal of Continuing Education in the Health Professions最新文献

{"title":"Between Incremental Change and Transformation: The Emerging Role of Generative AI in Continuing Professional Development Within Health Care Systems.","authors":"Sofia Valanci-Aroesty, Vjekosav Hlede, Heather MacNeill, Álvaro Margolis, Ken Masters","doi":"10.1097/CEH.0000000000000648","DOIUrl":"10.1097/CEH.0000000000000648","url":null,"abstract":"<p><strong>Introduction: </strong>Generative Artificial Intelligence (GenAI) tools are reshaping professional education by enabling tailored content generation, automating tasks, and enhancing creative output. While widely studied in undergraduate and postgraduate education, little is known about their role in Continuing Professional Development (CPD), where professionals function as system-level educational leaders rather than learners.</p><p><strong>Methods: </strong>A cross-sectional international survey (March-June 2025) explored GenAI adoption among CPD professionals. Using the validated Generative Artificial Intelligence Acceptance Scale and the Artificial Intelligence Attitude Scale, the study examined performance expectancy, effort expectancy, facilitating conditions, social influence, and general attitudes toward Artificial Intelligence. Quantitative data were analyzed descriptively and inferentially, and qualitative open-ended responses were analyzed using reflexive thematic analysis.</p><p><strong>Results: </strong>A total of 110 respondents from 14 countries participated. Most reported moderate to high familiarity and comfort with GenAI, with frequent users demonstrating significantly greater confidence and perceived proficiency. Performance expectancy was high, particularly regarding efficiency and productivity, but lower for complex problem-solving. A proportion of respondents expressed undecided positions, indicating transitional stages of adoption. Qualitative analysis revealed three overarching themes: (1) adoption and utilization, (2) personal perspectives, and (3) perceived challenges and benefits.</p><p><strong>Discussion: </strong>CPD professionals view GenAI as an augmentative tool that enhances efficiency and creativity rather than replacing human expertise. While individual readiness is high, sustainable adoption depends on institutional support, structured training, and ethical integration. This study provides novel insights into GenAI adoption at the CPD system level and highlights the importance of engaging the \"undecided\" to move from incremental use to meaningful transformation.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"206-214"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Traditional and Generative Artificial Intelligence for Programmatic Decision Making in Faculty Development.","authors":"Gayle D Haischer-Rollo, Jessica T Servey, Bizualem Zelelew, Thomas McFate, Irene Chi, Joshua Duncan","doi":"10.1097/CEH.0000000000000654","DOIUrl":"10.1097/CEH.0000000000000654","url":null,"abstract":"<p><strong>Introduction: </strong>Traditional program evaluation in medical education often faces challenges with large data volumes and manual analysis, which can delay data-driven decisions. This article introduces the artificial intelligence (AI)-enhanced program evaluation (AIEPE) framework to address this gap, proposing a novel, integrated approach using both traditional and generative AI to improve efficiency and insight.</p><p><strong>Method: </strong>The AIEPE framework is a five-phase, iterative system that augments human evaluators. It begins with Multi-Modal Data Aggregation (Phase 1). Next, a Dual-Stream AI-Powered Analysis (Phase 2) uses traditional AI for sentiment and thematic analysis while generative AI synthesizes narrative summaries. These insights inform AI-Powered Strategic Formulation (Phase 3), which assists in prioritizing issues and drafting action plans. The final phases, Human-in-the-Loop Decision-Making (Phase 4) and Programmatic Implementation and Iteration (Phase 5), ensure continuous improvement with human oversight.</p><p><strong>Results: </strong>Applying the AIEPE framework to a faculty development program evaluation, we analyzed 3361 qualitative comments. Sentiment analysis revealed 87.6% positive comments and thematic analysis identified key topics. The dual-stream approach provided a deeper understanding, linking a high no show rate in an AI course to faculty feeling unprepared. This led to a top-priority recommendation to redesign the curriculum.</p><p><strong>Discussion: </strong>The AIEPE framework transforms program evaluation from a labor-intensive process into a dynamic engine for continuous improvement. It empowers academic leaders to shift from data collection to architects of data-informed strategy. Although the framework significantly increases efficiency and depth of insight, it highlights the critical need for human oversight to mitigate challenges such as bias and over reliance on technology.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"215-221"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for Oversight: AI in Continuing Professional Development Accreditation.","authors":"Dr Graham T McMahon","doi":"10.1097/CEH.0000000000000630","DOIUrl":"10.1097/CEH.0000000000000630","url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence (AI) is increasingly integrated into health professions education, yet its application in continuing professional development (CPD) oversight remains underexplored. Accrediting bodies play a critical role in ensuring that continuing education is trustworthy, unbiased, and aligned with professional standards.</p><p><strong>Methods: </strong>This perspective synthesizes current literature and professional guidance to examine potential applications of AI in accreditation oversight. The analysis considers how AI could support efficiency, consistency, and insights across regulatory functions, while emphasizing the continued necessity of human judgment and governance.</p><p><strong>Results: </strong>Opportunities for accreditor use of AI include screening reaccreditation materials, identifying high-risk activities for audit, synthesizing national trends, linking participation data with certification and licensure systems, and assessing providers' responsible use of AI tools. A structured framework of opportunities and risks highlights the promise of efficiency and data integration alongside challenges related to accuracy, equity, transparency, security, and public trust.</p><p><strong>Discussion: </strong>Accrediting systems have an opportunity to model responsible AI use in ways that advance professional learning and safeguard the integrity of CPD. The responsible integration of AI, guided by principles of accountability, transparency, equity, and security, can help ensure that oversight is both efficient and trustworthy, while reinforcing the credibility of CPD systems worldwide.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"166-170"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"I Just Don't Know Enough:\" A Needs Assessment for Artificial Intelligence Adoption in Continuing Professional Development.","authors":"Adria Hoffman, Elissa Hall, Jennifer Curran, Bonnie Ganther, Carrie Bowler","doi":"10.1097/CEH.0000000000000628","DOIUrl":"10.1097/CEH.0000000000000628","url":null,"abstract":"<p><strong>Introduction: </strong>Rapid advances in, and availability of, generative artificial intelligence (AI) led to great interest in opportunities to create workplace efficiencies and augment health professions education. The purpose of this project was to identify the existing needs among continuing professional development (CPD) professionals and the organizational approaches necessary to support AI tool adoption.</p><p><strong>Methods: </strong>During nine focus groups and two interviews conducted between April and July 2024, participants responded to questions focused on their knowledge of, and experiences using, AI tools. Interpretive phenomenological analysis was used to identify participants' experiences with technology and organizational barriers to implementation.</p><p><strong>Results: </strong>Data revealed barriers to adoption at the individual, team, and organizational levels: (1) knowledge of organizationally licensed tools, (2) digital literacies, (3) organizational policy and communication, (4) role expectations and task clarification, and (5) defined organizational purposes.</p><p><strong>Discussion: </strong>Although current literature supports the need to develop skills for a future with AI, nuanced roles and contexts for its use had not yet been explored, creating a gap in how we consider AI in CPD. This study revealed the importance of understanding how varied technologies referred to as AI work, and the legal, ethical, and pedagogical evidence required to evaluate tools within a broader digital literacy framework. This study also informs our understanding of the organizational systems, processes, communication plans, incentives, and consequences that hinder or support technology adoption.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"198-205"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue: Artificial Intelligence in Continuing Professional Development.","authors":"Anita Samuel, Ronald Cervero, Sharisse Marie Arnold Rehring","doi":"10.1097/CEH.0000000000000673","DOIUrl":"10.1097/CEH.0000000000000673","url":null,"abstract":"","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"151-152"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148521439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence, Cognitive Abundance, and the Multi-Layered Competence of Health Professionals.","authors":"Martin V Pusic, Olle Ten Cate","doi":"10.1097/CEH.0000000000000656","DOIUrl":"10.1097/CEH.0000000000000656","url":null,"abstract":"<p><strong>Abstract: </strong>Artificial intelligence (AI) is transforming how health care professionals develop, maintain, and express competence across the span of their careers. Traditional continuing professional development has been shaped by a paradigm of what has been called cognitive scarcity (or informational resource scarcity) where clinicians had limited opportunity to find, read, synthesize, and interpret evidence, and learning systems evolved to deliver knowledge in periodic, curated updates. Emerging AI systems-large language models, multimodal analytic tools, predictive algorithms, and reflective agents-disrupt this scarcity by creating cognitive abundance (or informational resource abundance). These systems generate real-time evidence syntheses, contextual insights, adaptive learning trajectories, and continuous performance feedback. Using ten Cate et al.'s (2024, Medical competence as a multilayered construct. Med Educ , 58, 93) multilayered model of competence-canonical, contextual, and personalized-this paper analyzes how AI can both enhance existing educational processes and fundamentally reshape the developmental landscape. AI shifts clinicians from being knowledge stewards to orchestrators of distributed cognition, from experiential learners to data-informed practitioners, and from using opportunistic continuous personal development to continuous reshaping of professional identity. Continuing professional development must evolve to cultivate AI literacy, hybrid reasoning, interprofessional coherence, and ethical stewardship in work environments where cognition is abundant. The contents of long-term memory of clinicians will shift from a predominance of biomedical facts needed to steer daily clinical work, to new procedural inquiry skills needed to find, select, and evaluate the validity of information for clinical decision making.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"153-159"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148333475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Avatar Advantage: AI-Powered Faculty Development to Enhance Feedback Delivery Skills.","authors":"Kara Michalsen, Tai Lockspeiser, Angela S Czaja, Clint Carlson, Catherine Bilyeu, Michelle Kiger, Aimee K Gardner","doi":"10.1097/CEH.0000000000000662","DOIUrl":"10.1097/CEH.0000000000000662","url":null,"abstract":"<p><strong>Background: </strong>Feedback is fundamental to health professions education and essential for workplace-based assessment. Unfortunately, most educators lack formal training and access to evidence-based programs that incorporate experiential learning and real-time interactions.</p><p><strong>Objective: </strong>This study investigates the extent to which feedback delivery ratings provided by artificial intelligence (AI)-powered avatars are considered acceptable and reliable in comparison with feedback delivery ratings created by faculty participants and peer observers.</p><p><strong>Methods: </strong>After an interactive lecture on high-quality feedback, participants worked in small, facilitated four-person groups to deliver feedback in structured scenarios using AI-powered avatars. After each scenario, participants, peers, and the avatar independently rated feedback delivery using the DOCS-FBS scale (1-3; 1 = not done, 3 = successfully done). Scenario complexity was assessed with a nine-item scale (1-4; 4 = most complex). Multisource feedback ratings, postsession surveys, and group debrief data were analyzed with descriptive statistics, paired-sample t tests, and analysis of variance.</p><p><strong>Results: </strong>Participants rated session quality as very good (38%) or excellent (62%). Most found avatar-based feedback practice very or extremely effective (82%) and expressed strong interest in future avatar training (80%) and postworkshop access (74%). Participants reported high engagement and valued detailed rationales from the avatar. Peer observer ratings (2.82 ± 0.08) were significantly higher than both self-ratings (2.53 ± 0.39, P < .001) and avatar ratings (2.62 ± 0.32, P < .001). No differences emerged between self and avatar feedback delivery ratings.</p><p><strong>Conclusions: </strong>Using AI-powered avatars in feedback training sessions provide reliable feedback delivery ratings, are well received by participants, and can serve as a pragmatic solution for those leading professional development efforts.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"184-190"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148340806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reimagining Continuous Professional Development for Physicians in the Age of Artificial Intelligence.","authors":"James S Barry, Ryan M McAdams","doi":"10.1097/CEH.0000000000000644","DOIUrl":"10.1097/CEH.0000000000000644","url":null,"abstract":"<p><strong>Abstract: </strong>Artificial intelligence (AI) is transforming medicine by changing how clinicians learn, reason, communicate, and care for patients. Clinician expertise and abilities extend beyond cognition and technical skill to include human capacities like empathy, ethical discernment, moral responsibility, and trust-building with patients. As AI becomes integral to health care, continuing professional development (CPD) is increasingly positioned to support physicians in working effectively with intelligent technology while preserving the human capacities central to medical practice. CPD has long served clinicians by strengthening foundational knowledge and reinforcing standards of care by delivering high-quality, outcome-based education. Various educational models, designed for a pre-AI era, emphasize knowledge acquisition over deeper clinical reasoning. Such reasoning is now increasingly being augmented by AI-clinician collaboration in contemporary practice. CPD can engage AI not only as a topic to study but as an opportunity to reshape how physicians learn, reason, and approach clinical care. This paper introduces the Technical Understanding, Critical Appraisal, and Practical Application × Dual-Axis model, a framework supporting the coevolution of individual and organizational learning for AI-enabled health care. This conceptual framework integrates three domains of AI competency: Technical Understanding, Critical Appraisal, and Practical Application with organizational adoption and implementation strategies that focus on governance, infrastructure, and culture. Linking personal AI literacy and fluency with institutional capabilities creates a foundation for responsible AI in medicine. Reimagining CPD with AI supports clinicians in developing adaptive expertise while remaining clinically competent, ethically grounded, and compassionate while working alongside AI models in a rapidly changing health care environment.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"177-183"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuing Professional Development in the Artificial Intelligence Era.","authors":"Anita Samuel, Ronald M Cervero","doi":"10.1097/CEH.0000000000000668","DOIUrl":"10.1097/CEH.0000000000000668","url":null,"abstract":"<p><strong>Abstract: </strong>Artificial Intelligence (AI) is reshaping clinical work in ways that unsettle the foundational assumptions of continuing professional development (CPD). Existing CPD frameworks-Kirkpatrick, Miller, Moore, and the Five Domains-presume that clinicians are the primary generators of knowledge, the center of agency, and the drivers of performance and outcomes. AI disrupts this logic. Systems that draft clinical reasoning, modulate workflows, and influence decision pathways no longer function as passive tools but as active contributors to clinical judgment. As agency becomes distributed across human and machine actors, traditional frames that link CPD participation to changes in competence, performance, and outcomes begin to collapse. This paper introduces a Post-AI CPD model comprising three configurations-Augmentation, Integration, and Co-Agency-that describe how clinicians learn and act within AI-mediated environments. In Augmentation, AI enhances efficiency without altering reasoning. In Integration, clinicians negotiate uncertainty and shared judgment with AI partners. In Co-Agency, AI becomes embedded in workflows such that clinical action is jointly produced by humans, algorithms, and institutional structures. CPD's future relevance depends on shifting from training individuals to use AI tools toward preparing clinicians to interpret, supervise, and ultimately steward AI-embedded practice environments. Without this reconfiguration, CPD risks misalignment with the realities of AI-saturated clinical care.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"160-165"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148425950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PEPCAL: A Heuristic Framework for Teaching Generative AI to Continuing Professional Education Providers and Faculty Developers.","authors":"Heather MacNeill, Ken Masters, Jennifer Benjamin, Neil Mehta, Anoop Agrawal, Sofia Valanci-Aroesty","doi":"10.1097/CEH.0000000000000671","DOIUrl":"10.1097/CEH.0000000000000671","url":null,"abstract":"<p><strong>Abstract: </strong>Generative AI is rapidly evolving with the potential to change the way we think about and provide continuing professional development (CPD) and faculty development (FD), yet educators may lack practical and theoretically informed guidance for its integration. This Forum article introduces the Pedagogical-Ethical-Preparation-Creative-Administrative-Large Data (PEPCAL) framework to support CPD/FD leaders in teaching about and using GenAI. Developed from the authors' international teaching experience in GenAI, evolving literature, and inductive feedback, PEPCAL provides a heuristic matrix with two intersecting axes: how we begin to teach GenAI (Pedagogical Design, Ethical Considerations, and Preparation/Practical Application) and how health professions educators may use GenAI in their teaching (Creative Applications, Administrative Educational Tasks, and Large Data Insights). The framework highlights opportunities for GenAI to enhance multimodal learning, curriculum design, feedback, and data-informed decision-making, while building a foundation in ethics, environmental impact, pedagogical design, and avoiding deskilling and burnout. PEPCAL is intended to complement existing CPD frameworks and learning theories, prompting educators to move from tool-driven experimentation toward education-first adoption, adapting to different educational contexts in CPD and FD. The authors have found it helpful in training CPD/FD providers to responsibly implement, evaluate and iteratively refine GenAI-enabled CPD/FD, as well as help foster conversations around complex and evolving sociotechnical issues.</p>","PeriodicalId":50218,"journal":{"name":"Journal of Continuing Education in the Health Professions","volume":" ","pages":"191-197"},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}