This article introduces the Medical Education Development, Innovation, and Competency in AI (MEDIC-AI) program, a novel peer-led instructional method specifically designed to address and bridge the 'faculty readiness gap' prevalent among experienced clinician-educators regarding the integration and utilization of Artificial Intelligence (AI) tools in medical education. The study evaluates the pilot program's effectiveness by measuring participants' perceived usefulness, perceived ease of use, and behavioral intention to adopt new AI technologies, ultimately suggesting that a practical, peer-modeled, and do-it-yourself (DIY) instructional format can significantly enhance AI competency, boost confidence, and foster the successful integration of AI applications into daily teaching responsibilities and clinical practice, thereby improving both medical instruction and patient care outcomes.
Introduction
Artificial intelligence (AI) has profoundly impacted medical education, creating a significant 'faculty readiness gap' among seasoned clinician-educators who often lack confidence and competence in understanding, evaluating, or integrating these rapidly evolving digital tools. Unlike younger faculty, many experienced clinicians find traditional AI training models overwhelming and ineffective, leading to a reluctance to adopt new technologies. This study aims to present and evaluate medical education development, innovation, and competency in AI (MEDIC-AI), a unique peer-led instructional design, to understand its potential in increasing faculty readiness and influencing clinician-educators' willingness to integrate AI tools into their teaching and clinical practices, using the Technology Acceptance Model (TAM) framework.
Materials and methods
The MEDIC-AI program was meticulously designed by a mid-career clinician-educator (NW) to provide highly tailored and time-efficient faculty development, directly addressing the busy schedules of physician educators. Its core domains were carefully mapped to the Accreditation Council for Graduate Medical Education (ACGME) systems-based practice milestones and the AAMC Foundational Competencies, emphasizing practice efficiency and technological adaptation. The pilot episode, a 17-minute human-AI hybrid podcast produced with simple DIY tools, demonstrated practical AI applications for administrative tasks common in medical education, such as drafting letters of recommendation. An anonymous cross-sectional survey based on the Technology Acceptance Model (TAM) was distributed internally to 96 clinician-educators at the Medical College of Georgia (MCG), with 33 participants completing the survey. The survey measured perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) to implement at least one AI tool within 30 days, while qualitative responses were analyzed using thematic coding to capture psychological barriers and motivations.
Results
The quantitative evaluation of 33 anonymous faculty responses demonstrated robust acceptance across all primary Technology Acceptance Model (TAM) variables. Specifically, 90.9% of respondents reported high perceived usefulness (PU) of MEDIC-AI in exposing them to novel AI applications for administrative tasks and clinical teaching. Furthermore, 81.8% perceived high ease of use (PEOU), finding the podcast format simpler to digest than traditional workshops, and a significant 87.9% expressed a clear behavioral intention (BI) to utilize at least one discussed AI tool within the subsequent 30 days. Qualitative thematic analysis, following Braun and Clarke's six-phase approach, revealed three major themes: a strong and urgent demand for AI tools to alleviate administrative burdens (e.g., medical student performance evaluations, CV summaries, patient education), the crucial role of peer hosting in fostering trust and engagement with AI, and persistent pedagogical and ethical concerns among faculty regarding potential learner intellectual shortcuts, erosion of critical thinking skills, depersonalization of patient care, and the environmental impact of AI usage.
Discussion
This study strongly suggests that transitioning faculty development from impersonal, broad AI training to a peer-led instructional model, such as MEDIC-AI, can significantly improve AI competency and integration among experienced clinician-educators. Unlike traditional commercial training that often leads to reluctance and fatigue, the pilot demonstrated that even imperfect tools and operators can be highly effective when a trusted colleague acts as the host. The peer host's ability to contextualize content and empathize with the institutional audience proved crucial in increasing trust, usability, and integration of otherwise daunting technologies. Encouraged by informal feedback from colleagues seeking visual guidance, MEDIC-AI has since evolved into a DIY video series, and there are plans to expand content to address the needs of non-educator, community-based physicians for tasks like custom educational podcasts, slide generation, and billing support. However, the study acknowledges several limitations, including its single-institution design, a modest sample size (n=33) potentially introducing selection bias towards early adopters, reliance on a single qualitative coder, the absence of a control group or pre-intervention data, and a cross-sectional assessment of short-term behavioral intentions rather than long-term mastery or educational outcomes.
Conclusions
In conclusion, as clinician-educators mature and technology, particularly AI, advances at an unprecedented pace, there is a genuine risk of disrupting the vital transfer of clinical wisdom and foundational knowledge to successive generations of physicians. This study provides compelling preliminary evidence that traditional faculty development approaches can be substantially enhanced through peer-led models to effectively bridge the existing 'faculty readiness gap' for AI utilization in medical education. While these initial results should be interpreted cautiously due to the study's single-institution design and small sample size, the reproducible DIY format of MEDIC-AI offers a highly adaptable and scalable framework for other educational institutions striving to improve AI literacy across both graduate and undergraduate medical training programs. By altering the learner experience through a trusted, peer-led environment, modern clinician-educators may be more inclined to confidently embrace and integrate emerging AI technology. Future piloting endeavors should expand to include multiple educational institutions, implement longitudinal tracking of AI tool adoption, and incorporate objective competency metrics to further validate the long-term benefits and widespread applicability of peer-led instructional models in fostering AI readiness.
