{"paper_id":"47ce3103-58b3-419d-bd75-48a42c7bf99b","body_text":"Examining the teacher readiness gap at the interface of artificial intelligence and medical education: A qualitative study of clinical educators | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Examining the teacher readiness gap at the interface of artificial intelligence and medical education: A qualitative study of clinical educators Tim Murphy, Ginger Vaughn, Rob E. Carpenter, Benjamin McKinney, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5362276/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of Artificial Intelligence (AI) into healthcare is transforming medical education, reshaping how diagnostic skills, treatment approaches, and patient care methods are taught. This study investigates the interface of AI and medical education, focusing on the preparedness and views of clinical educators. Using the Unified Theory of Acceptance and Use of Technology as a framework, this research assesses the factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Through an inductive-to-deductive methodology, we conducted semi-structured interviews with 15 clinical educators from the south-central region of the United States who oversee third-year medical students. Key findings of teacher readiness at the interface of AI and medical education centered around 1) the technological learning curve, 2) the need for hands-on, action-based learning, 3) the critical role of institutional support, 4) mentorship as a crucial support system, 5) balancing human elements with AI integration, and 6) divergent comfort levels between generational cohorts. While AI holds promise to reform medical education by fostering adaptive, personalized learning environments, it also raises challenges in preserving essential human elements of patient care. Addressing these challenges demands a strategic, institutionally supported shift in medical pedagogy to ensure that AI integration is both effective and sustainable. The study’s insight into clinical educators' perspectives lay the groundwork for developing AI-ready educational models that balance technical expertise with core humanistic values, supporting a comprehensive approach to medical training in the AI-driven future. medical education unified theory of acceptance and use of technology (UTAUT) medical pedagogy nomological network medical school healthcare Figures Figure 1 Figure 2 Introduction Two rapidly evolving fields—healthcare and artificial intelligence (AI)—are converging, reshaping the delivery of medicine. The integration of AI into healthcare is reforming clinical practice, fundamentally changing how physicians approach diagnostics, treatment planning, and patient care [ 1 ]. The integration is not a mere enhancement; it signifies a profound shift in clinical workflows, where AI systems provide advanced decision-making support, real-time analytics, and streamlined processes. And these changes inherently prompt a reassessment of medical education—particularly on how it must adapt to equip both current and future physicians with the skills to leverage AI effectively, without losing sight of the essential human-centric aspects of patient care [ 2 – 4 ]. The challenge for medical education goes beyond adopting new technologies; it requires a pedagogical transformation [ 5 ] to keep the physician's role fundamentally human-centered, despite the growing non-human influence of AI tools. Historically, medical education has focused on cultivating clinical reasoning and diagnostic skills through a structured combination of theoretical instruction and experiential learning, fostering competencies like problem-solving, critical thinking, and reflective practice [ 6 , 7 ]. However, recent evidence suggests the need for recalibration. Current research challenges the emphasis on generalized thinking skills, instead highlighting the centrality of domain-specific knowledge in cultivating true medical expertise [ 8 ]. Mastery of both formal and experiential knowledge is fundamental to effective medical practice, raising a crucial question: How can medical education adapt to the rapid evolution of knowledge, especially given AI's growing role in clinical settings? This question compels a rethinking of educational approaches—ensuring that medical students are not only taught traditional clinical competencies but are also adept at leveraging AI technologies to enhance both the depth and precision of their subject expertise. We believe the current literature largely overlooks key nuances in the adoption of AI in medical education, specifically the readiness of clinical educators to incorporate AI competencies into their teaching practices and the pedagogical shift required for effective instruction. While many studies examine AI integration in healthcare, few address the perspectives and preparedness of clinical educators in effectively teaching these emerging skills. This gap likely exists because integrating AI requires not only technological advancements but also a notable pedagogical transformation, challenging long-standing educational models that have remained largely unchanged for the past century [ 9 ]. Problem Statement A pedagogical challenge emerges at the intersection of AI and medical education, as AI fundamentally differs from previous technological advancements in the field. Unlike earlier innovations—such as X-rays [ 10 ], simulation tools [ 11 ], problem-based learning [ 12 ], multimedia [ 13 ], or more recent technologies like virtual and augmented reality [ 14 , 15 ], which enhanced experiential, observational, and interactive learning—AI redefines the epistemic framework of knowledge acquisition in medical education. AI not only changes the tools used for learning but also transforms the nature of knowledge and decision-making processes [ 16 ]. For the most part, new medical technologies have historically been incremental innovations, requiring only enhanced pedagogical structures to teach. In contrast, AI is not limited to predefined learning pathways; it stimulates a nomological network of individualized learning experiences, transforming medical education from incremental pedagogical adjustments into a personalized, evolving learning journey [ 17 ]. And this presents teaching challenges, especially for clinical educators who perceive that their roles are being fundamentally redefined, especially because clinical educators have been the primary architects of knowledge dissemination and the arbiters of student competence [ 18 – 19 ]. As AI increasingly assumes roles like providing real-time feedback, identifying learning gaps, and delivering personalized interventions, it risks undermining the traditional authority and autonomy of clinical educators [ 20 ]. This shift could create friction as clinical educators adapt to the evolving landscape of instructional authority [ 21 ]. The rapid advancement of AI also demands new competencies, including understanding AI algorithms and managing AI-driven insights—requiring a pedagogical reorientation that may not be universally accepted [ 22 ]. Another critical concern lies in the potential erosion of the human elements at the core of medical education. Medical training extends beyond clinical knowledge transmission; it also encompasses mentorship, ethical deliberation, empathy, and the nurturing of professional identity—all aspects that thrive on human interaction. While AI can undoubtedly optimize content delivery and personalize learning, these essential human components must not be compromised. The challenge for clinical educators, therefore, lies in striking a balance between the technological precision offered by AI and the empathetic, human-centered nature of medical education to maintain the integrity of professional training [ 23 ]. To better understand this challenge, we apply the unified theory of acceptance and use of technology (UTAUT) [ 24 ] as a nomological framework to examine clinical educators' readiness, perspectives, and adaptability toward AI integration into their teaching practices. Theoretical Framework: Nomological Network Perspective on UTAUT To gain a deeper understanding of the challenges clinical educators face in integrating AI into medical education, a nomological approach provides a valuable framework for evaluating their acceptance and readiness [ 25 , 26 ]. Given the complexity of AI integration, this approach enables a thorough assessment of the factors shaping educators' readiness and willingness to adopt new technologies, encompassing mindset shifts, skill development, teaching adjustments, and institutional support [ 27 ]. UTAUT is particularly relevant for this context because it provides a well-rounded understanding of both the individual and organizational factors influencing the adoption of AI in medical education. It helps bridge the gap between technological capabilities and pedagogical transformation, ensuring that both acceptance and readiness are adequately assessed for viable teaching practices. Each UTAUT construct—performance expectancy, effort expectancy, social influence, and facilitating conditions—plays a vital role in generating a nomological network, establishing a comprehensive perspective to assess clinical educators’ readiness, perspectives, and adaptability toward AI integration into their teaching practices. These constructs are not isolated predictors but, instead, act in concert to build a cumulative, networked understanding of the challenges and enablers of technology adoption in medical settings [ 28 ]. Performance expectancy functions as a central node in this network by capturing clinical educators' beliefs regarding AI's potential to enhance the quality of medical education. By connecting performance expectancy to educational outcomes, it becomes part of the nomological network that highlights the impact of perceived effectiveness on clinical educators' willingness to integrate AI technologies in their teaching practices. High performance expectancy, thus, strengthens the network's predictive validity by incorporating empirical insights from clinical educators’ anticipated outcomes, demonstrating the link between technology potential and its practical educational benefits. Effort expectancy complements this by representing the perceived ease of use. The simplicity or complexity of integrating AI into curricula feeds directly into the broader network, providing insight into practical barriers or facilitators. By incorporating effort expectancy, we identify how ease of use interplays with the perceived benefits, thereby providing a dual perspective on both the advantages and complexities of AI technology integration. The interplay of effort expectancy with performance expectancy constructs a richer nomological network, supporting a more nuanced understanding of teacher readiness that goes beyond isolated predictors [29. Social influence and facilitating conditions extend the network into the social and infrastructural contexts in which AI adoption occurs. Social influence reflects how the attitudes of peers, institutional culture, and perceived normative pressures shape clinical educators' behaviors. Its inclusion within the nomological network underlines the relational dynamics, emphasizing how peer acceptance and institutional encouragement contribute to shaping clinical educators’ attitudes toward AI. By analyzing social influence alongside facilitating conditions—which addresses the infrastructural and administrative resources available for technology adoption—the framework captures the practical context in which clinical educators operate, thereby grounding the theoretical constructs in real-world logistical considerations. By conceptualizing the UTAUT constructs as elements within a nomological network, we establish a cohesive theoretical framework that systematically integrates individual, social, and infrastructural factors. This interconnected approach accounts for the predictive capabilities of each construct and illustrates how these elements interact dynamically (Fig. 1 ), thereby generating a more robust explanatory model to assess for readiness. Methods This study employed a qualitative research design utilizing an inductive-to-deductive methodological flow to explore clinical educators' readiness for AI integration into medical education. Borrowing from Fereday and Muir-Cochrane [ 30 ], this approach allowed for a dual exploration of emergent themes through inductive analysis, followed by their systematic interpretation within a predetermined framework—in this case, UTAUT [ 24 ]. This research approached aimed to capture the nuanced perceptions of clinical educators, providing a rich, in-depth understanding of their subjective experiences and attitudes within their unique clinical and educational environments [ 31 ]. This methodology is particularly well-suited for studying complex social phenomena, as it enables researchers to gather data that deeply reflects the breadth and diversity of participants' experiences [ 32 ]. This study was approval by the University of Texas at Tyler Institutional Review Board. Participants Participants were a convenience sample of clinical educators actively supervising third-year medical students. The educators were selected for their dual roles as both clinical practitioners and mentors, which gave them unique perspectives on the integration of AI into medical education. As frontline facilitators, clinical educators provide invaluable insights into both the opportunities and challenges of AI adoption [ 33 – 36 ]. Their perspectives are essential for balancing AI's operational efficiency with the humanistic elements vital to medical education. Inclusion criteria required participants to be actively involved in supervising medical students and have familiarity with or interest in AI technologies in healthcare education. Table 1 Demographics of interview participants Interview Participant Number Gender Age Teaching Experience (Years) Medical Degree; Certifications Location 01 F 32 3 DO; AOBFM Dallas, TX 02 F 34 1 DO; AOBFM; ABFM McKinney, TX 03 M 36 5 MD; ABFM Dallas, TX 04 M 37 5 MD; ABFM Dallas, TX 05 M 42 8 MD; ABFM North Garland, TX 06 M 52 14 MD; ABFM Midlothian, TX 07 F 44 13 MD; ABFM McKinney, TX 08 M 46 12 MD; ABFM North Garland, TX 09 F 50 8 MD; ABFM Uptown, TX 10 F 53 16 MD; ABFM North Garland, TX 11 M 53 20 MD; ABPM Dallas, TX 12 M 57 25 MD; ABFM; ABR Midway, TX 13 M 61 30 MD; ABFM; ABIM Dallas, TX 14 F 52 7 + 1 MD; ABFM Frisco, TX 15 F 65 27 MD;ABFM; ABIM Dallas, TX Note : M = Male, F = Female; MD = Doctor of Medicine Degree, DO = Doctor of Osteopathic Medicine; ABFM = American Board of Family Medicine; ABIM = American Board of Internal Medicine; AOBFM = American Osteopathic Board of Family Medicine Data Collection Individual, recorded, semi-structured interviews were conducted with participants using both face-to-face and virtual formats, depending on availability. The interviews consisted of peer-reviewed open-ended and follow-up questions, allowing participants to share their experiences with AI in medical education. Questions addressed the participants' background, clinical education experiences, and perceptions of AI's impact on teaching and learning. Follow-up questions were used to extract details about AI-related challenges, enablers, and ethical concerns. Interviews were conducted until data saturation was reached, ensuring that no new themes emerged [ 37 ]. All interviews were transcribed verbatim, and deidentified codes were assigned to each participant to maintain confidentiality. Data Analysis The integration of inductive and deductive approaches was crucial for this study. The inductive phase provided a grounded understanding of participants' experiences, free from preconceived biases, while the deductive phase offered a structured way to interpret these themes using the UTAUT framework. This dual approach is particularly valuable in complex educational settings where emerging technologies are reshaping practices. By first allowing emergent insights and then aligning them with established technology adoption theories, the study effectively captured educators' perspectives and contextualized them within broader theoretical constructs [ 38 , 39 ]. Inductive Phase The initial phase of analysis was inductive, aiming to allow themes to emerge directly from the raw data without imposing preconceived categories or hypotheses. The participant interviews were designed to be flexible, allowing participants to freely express their experiences, which was crucial for capturing the diverse perspectives and attitudes of those involved in integrating AI into medical education [ 40 ]. After each participant reviewed and confirmed their transcript, we begin an initial round of thematic analysis [ 41 ]. During this inductive coding phase, emergent (common) themes were identified and characterized into generalized categories [ 42 ]. Thematic analysis was conducted independently by multiple researchers, each systematically identifying key patterns and themes from the data. Once the individual analyses were completed, we engaged in detailed discussions to compare findings, resolve discrepancies, and collaboratively refine the themes through deliberation. This process of researcher triangulation helped ensure the reliability and credibility of the identified themes by minimizing individual bias and promoting a more robust and comprehensive interpretation of the data [ 43 ]. This inductive phase was critical for understanding the underlying views and concerns of the participants, providing a foundation for subsequent deductive analysis. Deductive Phase A deductive approach was applied in the second phase of analysis to classify the inductive themes within the UTAUT framework. This phase involved systematically mapping the identified themes from the inductive coding process to the four core constructs of UTAUT—performance expectancy, effort expectancy, social influence, and facilitating conditions (Table 2 ). To ensure rigor, the mapping process was conducted by multiple researchers, who independently reviewed the themes in relation to the UTAUT constructs. Any discrepancies in categorization were addressed through group discussion and consensus-building [ 44 ]. This iterative process not only enhanced the reliability of the classifications but also allowed for refinement of the themes to fit more precisely within the theoretical constructs. By using this structured deductive approach, the analysis was able to situate the empirical findings within a well-established theoretical context, thereby increasing both the validity and grounding of the study's conclusions. Table 2 Categorization of inductive themes within the UTAUT framework UTAUT Construct Categorization of Inductive Themes for Deductive Analysis Performance Expectancy Categorized themes focused on AI's potential benefits for improving personalized learning, diagnostic simulations, and overall educational quality in medical training. Effort Expectancy Categorized themes focused on the challenges of AI adoption, focusing on the effort required by educators to understand and integrate AI tools into their teaching. Social Influence Categorized themes focused on how institutional culture, peer attitudes, and administrative support influenced educators' decisions to adopt AI. Facilitating Conditions Categorized themes focused on the availability of resources, infrastructure, and support for AI adoption, including educators' insights on training, technical support, and policy guidance. Results The inductive analysis yielded a richly contextualized contribution of clinical educators' experiences (Table 3 ), offering nuanced insights into their engagement with AI technologies in teaching. The emergent themes provided the foundation for the subsequent deductive phase (Table 4 ), where they were systematically mapped into the UTAUT framework constructs. This methodological sequence ensured that the qualitative data remained grounded in participants' experiences while aligning the findings with established theoretical constructs for a more structured interpretation and comprehensive analysis. Table 3 Inductive insights: Emergent themes, supporting participant quotes, and interpretative analysis of educators' perspectives on AI integration Emergent Themes Supporting Quotes Qualitative Analysis Technological Learning Curve \"AI isn’t something you can master overnight.\" (Participant 2) This reflects the perception of technological complexity, and the steep learning curve associated with mastering AI, highlighting perceived barriers to readiness. Need for Hands-On, Action-Based Learning \"You can’t just read about it in a textbook or sit through a workshop; you need to be in the trenches using it.\" (Participant 5) The need for experiential, hands-on engagement with AI tools is emphasized here, demonstrating belief in learning through active practice rather than passive instruction. Institutional Support as a Barrier to Effective AI Integration \"Institutions need to provide ongoing training that’s tailored to different skill levels.\" (Participant 2) This points to a gap in institutional support and emphasizes the necessity of ongoing, differentiated training as a critical factor in facilitating AI integration. Mentorship as a Crucial Support System \"My mentors have been there to answer questions, offer advice, and even show me practical ways to incorporate AI tools into my daily work.\" (Participant 2) The importance of mentorship is highlighted as a critical enabler of AI integration, suggesting that social learning plays a key role in reducing the uncertainty associated with new technologies. Balancing Human Elements with AI Integration \"AI can provide incredible insights, but there is something to be said for being able to truly listen to a patient. You can't teach bedside manner through an algorithm.\" (Participant 6) There is an expressed concern that AI might compromise essential human elements in medical education, such as empathy and patient interaction, suggesting potential resistance to AI if it undermines human-centric training. Divergent Comfort Levels Between Generational Cohorts \"Students today are pretty tech-savvy and comfortable with using AI.\" (Participant 8) This reveals a generational divide, with younger educators and students showing greater comfort with AI, implying that familiarity with technology influences readiness for adoption. Table 4 Deductive mapping of emergent themes to UTAUT constructs: A structured analysis of factors influencing AI integration in medical education UTAUT Constructs Mapped Themes Mapping Insights Performance Expectancy Need for Hands-On, Action-Based Learning This theme maps to performance expectancy as it captures educators’ belief that AI has the potential to significantly enhance the quality of educational outcomes, specifically when integrated in a practical and immersive manner. Performance Expectancy Balancing Human Elements with AI Integration This theme maps to performance expectancy in that educators perceive AI as a tool that should enhance educational quality without detracting from the essential human aspects of medical education, such as empathy and interpersonal skills. Effort Expectancy Technological Learning Curve This theme maps to effort expectancy, emphasizing the perceived difficulty and learning demands associated with AI integration. Educators' readiness to adopt AI is significantly influenced by their perceptions of how complex or time-consuming it might be to learn and utilize these technologies. Effort Expectancy Divergent Comfort Levels Between Generational Cohorts This theme maps to effort expectancy because it highlights the generational differences in perceived ease of use, where younger educators and students are more comfortable and confident with AI tools compared to their older counterparts. Social Influence Mentorship as a Crucial Support System This theme maps to social influence as it captures how mentorship and peer attitudes shape individual educators’ willingness to adopt AI. Social Influence Institutional Culture and Peer Attitudes This theme maps to social influence, emphasizing how an institution’s culture and peer behaviors impact an educator’s readiness to embrace AI, either encouraging adoption through collective support or hindering it through lack of enthusiasm or endorsement. Facilitating Conditions Institutional Support as a Barrier to Effective AI Integration This theme maps to facilitating conditions, as it speaks to the availability—or lack—of necessary resources, infrastructure, and support mechanisms that facilitate effective AI adoption in medical education. Facilitating Conditions Availability of Resources and Infrastructure This theme maps to facilitating conditions because it emphasizes the need for a comprehensive support infrastructure to enable educators to integrate AI effectively. The dual-phased approach not only reinforced the theoretical understanding of the data but also bridged empirical observations with established technology adoption theories, thereby increasing rigor and relevance [ 30 ]. By integrating the findings into the nomological network of the UTAUT for AI adoption (Fig. 2 ), the results establish key relationships among the constructs [ 43 ]. This process helped to enrich the scholarly discourse by contributing both practical insights and theoretical advancements to the field of medical education research [ 45 , 46 ]. Discussion AI integration in medical education presents a pivotal opportunity to transform clinical training. While AI's potential to elevate diagnostic simulations and personalized learning is widely recognized, educators' readiness to adopt these tools remain largely unexplored [ 47 ]. As one participant noted, \" AI isn’t something you can master overnight \" (Participant 2), emphasizing that successful AI integration hinges on educators' ability to effectively apply it. This shift demands more than superficial engagement; traditional lecture-based models must evolve into interactive, AI-enhanced teaching methods. To prepare future healthcare professionals for an AI-driven landscape, educators need both technical proficiency and adapted teaching strategies, supported by institutional training programs [ 48 ]. Our study used an inductive-to-deductive methodological approach, drawing on the UTAUT to frame our findings. This approach allowed us to explore emergent themes and then map these insights to UTAUT constructs, offering a structured understanding of the factors influencing the integration of AI technology into the teaching practices of clinical educators. The following discussion illustrates how each UTAUT construct—performance expectancy, effort expectancy, social influence, and facilitating conditions—fundamentally supports the need for a for pedagogical transformation at the interface of AI and medical education. Performance Expectancy What emerged clearly in our analysis was the central theme of hands-on, action-based learning. AI, with its sophisticated simulations and real-time feedback, provides a unique platform for experiential learning [ 49 ]. This is not mere abstraction but a tangible evolution in how medical students engage with clinical scenarios. The traditional pedagogical frameworks, steeped in passive knowledge dissemination, must now give way to more interactive and practical learning environments. As the evidence suggests, AI allows students to be immersed in dynamic, risk-free environments that mirror the real-life complexities of patient care [ 50 ]. In this context, performance expectancy is no longer about passive, or even active learning, but about a new way of learning—a radical shift that underscores the need for a new pedagogical model. A student learning with AI differs fundamentally from traditional passive and active learning because AI offers personalized, adaptive experiences that dynamically respond to a student's individual learning needs [ 51 ]. In traditional passive learning, students absorb information, often without tailored feedback. Even in active learning, while students engage with content through interaction, the approach is usually standardized for a group rather than individualized. With AI, however, the learning process is transformed into a highly adaptive system where the technology continuously assesses the student's performance and provides immediate, customized feedback. AI-driven learning tools can adjust the difficulty level, suggest targeted learning materials, and offer real-time simulations that align with a student's pace, knowledge gaps, and preferences [ 52 ]. This kind of personalized learning fosters deeper engagement and accelerates mastery, as opposed to the one-size-fits-all methods of traditional teaching. Moreover, AI tools can create immersive environments where students apply skills in practical scenarios, enhancing both cognitive and experiential learning [ 48 ]. Thus, the AI-driven approach empowers students to take control of their learning journey, bridging gaps in real time and promoting an individualized, hands-on learning experience that surpasses both passive and conventional active learning methods. Yet, in our rush to embrace AI’s potential, we must pause to consider the equally important theme of balancing human elements with AI integration. AI, while powerful, lacks the ability to teach the nuances of empathy, bedside manner, or the subtleties of human interaction in patient care [ 53 ]. As one participant wisely noted, “ You can’t teach bedside manner through an algorithm ” (Participant 6). Here, we find a crucial tension: while AI can enhance educational outcomes, it must not come at the expense of core humanistic values that are integral to healthcare [ 54 ]. AI’s power lies in its ability to augment human capabilities, not replace them. This balance between technological advancement and human interaction is essential, lest we risk creating healthcare professionals who are technically proficient but emotionally disconnected. Thus, the performance expectancy of AI in medical education is a nuanced construct. On the one hand, it promises a revolution in how students engage with clinical content—moving beyond the lecture hall or even the hospital room. On the other hand, it requires a careful balancing act, ensuring that as we enhance technical skills, we do not erode the human elements that define effective patient care. This dual requirement of hands-on learning and humanistic balance points to a larger pedagogical transformation that must be embraced by clinical educators if AI is to fulfill its potential in reshaping medical education. Effort Expectancy AI promises to revolutionize medical education, but such transformation is neither seamless nor instantaneous. Effort expectancy, the perceived ease of integrating AI into teaching, has emerged as a pivotal factor, illustrated by the complex technological readiness of clinical educators. One cannot expect AI mastery to materialize without experiential immersion; as one participant put it, “ You can’t just read about it in a textbook... you need to be in the trenches using it ” (Participant 5). Herein lies the crux of the issue—AI demands hands-on engagement. You do not gain fluency in its operations by passive osmosis, but rather by actively navigating its intricacies. This brings us to the technological readiness and learning curve—an unavoidable companion of any transformative tool. The integration of AI into clinical education requires clinical educators to ascend a steep learning curve, one that demands cognitive flexibility and the reconfiguration of long-held pedagogical practices [ 55 ]. It is not just about inserting new technology into old frameworks; it’s about redefining those frameworks altogether. Clinical educators must be equipped with technical training and with conceptual understanding of how AI can augment learning. It is a shift akin to the fourth industrial revolution [ 56 ]—adapt or be left behind. The data does not merely show a gap in technical proficiency, it highlights a fundamental tension: a system built on tradition now tasked with embracing novel innovation. Equally significant is the divergent comfort levels between generational cohorts. AI integration unveils a generational chasm within academia. Younger educators, digital natives, find comfort and agility in adopting AI technologies, while their older counterparts often exhibit hesitation and discomfort. This generational divide is not trivial; it is a symptom of a broader evolutionary mismatch between the rapid advance of technology and the slower pace of human adaptability [ 57 ]. As one participant candidly remarked, “ There’s definitely a divide... younger educators pick this up quickly, while those of us with more traditional training need more time ” (Participant 3). This is no mere anecdotal observation—it speaks to a structural imbalance in how technological fluency is distributed across age groups. If left unaddressed, this divergence threatens to create a divided educational environment where the younger, AI-proficient educators surge ahead, leaving their older colleagues grappling in the dust of digital inertia. The implications are clear. To navigate this learning curve, there must be institutional frameworks that account for these divergent comfort levels, offering personalized, ongoing support. The key is not just in training clinical educators on AI tools but in reshaping the mindset that has traditionally governed medical education itself. As AI continues to intertwine with the fabric of medical education, institutions must invest in differentiated training programs that recognize these generational disparities, otherwise, we risk creating not just a technological gap, but a pedagogical one [ 58 ]. Social Influence When we dissect the social mechanisms that either catalyze or hinder AI adoption in medical education, we cannot underestimate the gravity of social influence. Human beings are inherently social creatures, and their behaviors are deeply influenced by the norms, attitudes, and feedback of their peers and institutional surroundings [ 59 ]. In the context of clinical educators, mentorship and peer attitudes become the guiding forces that either accelerate or stymie the integration of AI into their teaching practices [ 60 ]. The theme of mentorship as a crucial support system plays an indispensable role. Just as evolutionary biology teaches us that species learn complex behaviors from more experienced members of the group [ 61 ], educators, too, rely on mentorship to navigate the complexities of AI integration. One participant succinctly captured this, stating, “ My mentors have been there to answer questions and offer practical ways to incorporate AI tools into my daily work ” (Participant 2). Mentorship, then, is not a mere courtesy—it's the scaffolding that allows clinical educators to transcend their apprehension toward AI technology. It provides real-time, hands-on guidance that textbooks and online tutorials simply cannot replicate. This observation is bolstered by research on learning systems, which highlights that the presence of a mentor not only enhances skill acquisition but also reduces the cognitive load involved in grappling with new technologies [ 62 ]. But mentorship is just the first layer of social influence. Let’s consider the broader institutional culture and peer attitudes. A supportive institutional culture—where peers and leaders endorse the use of AI technology—creates a powerful feedback loop that reinforces adoption. One participant stated it plainly: “ The institution’s stance on AI makes all the difference. If your colleagues and leadership aren’t on board, it’s hard to make AI part of the daily routine ” (Participant 4). Here we see the phenomenon of social proof in action. When educators perceive that their peers and institutional leaders have embraced AI, they are more likely to engage with it themselves. But this is not just about conformity. It is about the evolutionary advantage of collaboration. Historically, educators excel when they share scholastic insights with their peers [ 62 ]. When peer attitudes are positive, educators feel empowered to experiment with AI tools, knowing they have the backing of their institution [ 22 ]. The absence of this support, on the other hand, turns AI into an isolated endeavor—a solitary uphill battle that many educators are reluctant to fight. The social influence dynamic also highlights the necessity of an adaptive institutional culture. It is not enough to introduce AI tools and expect clinical educators to naturally integrate them. The institution must create a fertile ground for AI adoption by fostering a culture that values innovation and collaboration. If the institutional leadership champions AI, while providing the resources and professional development required to navigate its intricacies, clinical educators will be more inclined to engage with these tools. Without such institutional alignment, AI is at risk of being a fringe tool, underutilized and often misunderstood [ 63 ]. Facilitating Conditions When we think about the successful integration of AI into educational systems, one might be tempted to focus solely on the technology itself, but this is a trap. The reality is far more nuanced. As we have learned from countless historical frameworks, human ingenuity is often stifled not by the limitations of technology but by the institutions tasked with adopting it [ 64 , 65 ]. In the context of AI, the importance of facilitating conditions becomes undeniable. It is not enough to introduce AI tools into medical education—without the scaffolding of institutional support and access to resources, these tools remain an untapped potential. Let us address institutional support as a barrier to effective AI integration. Participants in this study repeatedly highlighted the lack of structured, ongoing training as a core obstacle to successfully adopting AI. One participant observed, “ Institutions need to provide ongoing training that’s tailored to different skill levels ” (Participant 2), emphasizing that AI integration cannot be a one-size-fits-all endeavor. Here, we find a central paradox: institutions that are eager to adopt the veneer of innovation often fail to invest in the human capital necessary to sustain it. The result? A disconnect between technology and its practical application. The availability of resources and infrastructure further complicates this dynamic. While institutions may invest in the initial acquisition of AI tools, the long-term success of these tools is contingent upon their application to teaching practices. As one participant noted, “ You need more than just access to AI tools; you need the institutional backing to learn, adapt, and troubleshoot ” (Participant 4). This points to a deeper issue within the education ecosystem: technological readiness is not achieved merely by providing the tool but by embedding that tool within a robust support structure. Consider this: if AI represents the future of medical education, it follows that educators must evolve alongside these advancements. But as we know, this evolution is rarely linear. Institutional barriers act as evolutionary bottlenecks, hindering the adaptation process and slowing the integration of new technologies [ 66 ]. When the resources needed for proper training and infrastructure are inadequate, educators are left to navigate this technological landscape alone—often leading to frustration, burnout, and ultimately, the underutilization of AI [ 48 ]. This is not just a logistical failure; it is a reasoning mismatch between the promise of AI and the actual capacity of educators to wield it effectively. The facilitating conditions for AI integration are multifaceted. The UTAUT framework underscores that for any technology to be adopted, the right conditions must be in place—not just in terms of hardware but in fostering an institutional culture that prioritizes continuous learning, adaptability, and resource allocation. Research consistently shows that when institutions fail to provide these conditions, AI remains a fringe tool, utilized only by the most technologically adept educators while the majority struggle to incorporate it meaningfully into their pedagogy [ 63 ]. The success of AI integration is as much about institutional scaffolding as it is about the technology itself, and until this is recognized, the gap between potential and practice will persist. Limitations Despite its contributions to understanding the integration of AI into medical education, this study has several limitations that should be acknowledged. First, the study employed a qualitative case study approach with a convenience sample, limiting the generalizability of the findings. Although this method provided rich, in-depth insights into clinical educators' perspectives, the findings may not fully represent the broader population of medical educators across different regions or institutions. Second, the study’s reliance on semi-structured interviews means that responses were subject to participants' willingness and ability to articulate their experiences, which may introduce some response bias [ 67 ]. Third, as the study focused primarily on clinical educators’ perceptions, it did not directly examine students' experiences or outcomes related to AI integration, thereby presenting an incomplete picture of the pedagogical impacts of AI [ 68 ]. Last, the rapidly evolving nature of AI technologies means that findings could quickly become outdated, as new advancements continue to transform the educational landscape. Future research should aim to incorporate longitudinal data to track changes over time and broaden the scope by including diverse stakeholders in medical education, such as students and administrative staff, to capture a more holistic view of AI integration. Conclusion The findings of this study contribute to the need for a pedagogical transformation in medical education, one that equips clinical educators with the skills and support necessary to integrate AI into their teaching practices. By mapping clinical educators' perceptions to the UTAUT framework, this research highlights the multifaceted challenges and opportunities associated with AI adoption in medicine. The implications are clear: AI integration is not merely a technological endeavor but a pedagogical one that requires notable adjustments in how medical education is structured and delivered. Institutions must rise to the challenge by providing the necessary resources, training, and support to ensure that clinical educators are not only technically prepared but also pedagogically equipped to navigate this evolving technological frontier in medicine. Declarations Author contributions The authors confirm contribution to the paper as follows: T.M, G.V. and B.M. did study conception and design. T.M. and G.V. did data collection. R.M. performed data analysis. R.E.C. did interpretation of results, draft manuscript preparation and final edit. All authors reviewed the results and approved the final version of the manuscript. Financial Disclosure The authors have no financial disclosure or conflict of interest to report. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study has been approved by the ethics committee of The University of Texas at Tyler Institutional Review Board and Conscious consent was obtained from all participants. Clinical trial number: not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5362276\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":374375632,\"identity\":\"7b352cdd-5c84-4f75-91b2-0f4cf05b835a\",\"order_by\":0,\"name\":\"Tim Murphy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The University of Texas at Tyler\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tim\",\"middleName\":\"\",\"lastName\":\"Murphy\",\"suffix\":\"\"},{\"id\":374375633,\"identity\":\"4a912cb6-3495-4309-9caa-a33ac4364c86\",\"order_by\":1,\"name\":\"Ginger Vaughn\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The University of Texas at Tyler\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ginger\",\"middleName\":\"\",\"lastName\":\"Vaughn\",\"suffix\":\"\"},{\"id\":374375634,\"identity\":\"928618a9-d164-4977-a68f-076b0087a871\",\"order_by\":2,\"name\":\"Rob E. 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The integration of AI into healthcare is reforming clinical practice, fundamentally changing how physicians approach diagnostics, treatment planning, and patient care [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The integration is not a mere enhancement; it signifies a profound shift in clinical workflows, where AI systems provide advanced decision-making support, real-time analytics, and streamlined processes. And these changes inherently prompt a reassessment of medical education\\u0026mdash;particularly on how it must adapt to equip both current and future physicians with the skills to leverage AI effectively, without losing sight of the essential human-centric aspects of patient care [\\u003cspan additionalcitationids=\\\"CR3\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The challenge for medical education goes beyond adopting new technologies; it requires a pedagogical transformation [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e] to keep the physician's role fundamentally human-centered, despite the growing non-human influence of AI tools.\\u003c/p\\u003e \\u003cp\\u003eHistorically, medical education has focused on cultivating clinical reasoning and diagnostic skills through a structured combination of theoretical instruction and experiential learning, fostering competencies like problem-solving, critical thinking, and reflective practice [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. However, recent evidence suggests the need for recalibration. Current research challenges the emphasis on generalized thinking skills, instead highlighting the centrality of domain-specific knowledge in cultivating true medical expertise [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Mastery of both formal and experiential knowledge is fundamental to effective medical practice, raising a crucial question: How can medical education adapt to the rapid evolution of knowledge, especially given AI's growing role in clinical settings? This question compels a rethinking of educational approaches\\u0026mdash;ensuring that medical students are not only taught traditional clinical competencies but are also adept at leveraging AI technologies to enhance both the depth and precision of their subject expertise.\\u003c/p\\u003e \\u003cp\\u003eWe believe the current literature largely overlooks key nuances in the adoption of AI in medical education, specifically the readiness of clinical educators to incorporate AI competencies into their teaching practices and the pedagogical shift required for effective instruction. While many studies examine AI integration in healthcare, few address the perspectives and preparedness of clinical educators in effectively teaching these emerging skills. This gap likely exists because integrating AI requires not only technological advancements but also a notable pedagogical transformation, challenging long-standing educational models that have remained largely unchanged for the past century [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003ch3\\u003eProblem Statement\\u003c/h3\\u003e\\n\\u003cp\\u003eA pedagogical challenge emerges at the intersection of AI and medical education, as AI fundamentally differs from previous technological advancements in the field. Unlike earlier innovations\\u0026mdash;such as X-rays [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], simulation tools [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], problem-based learning [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], multimedia [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], or more recent technologies like virtual and augmented reality [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], which enhanced experiential, observational, and interactive learning\\u0026mdash;AI redefines the epistemic framework of knowledge acquisition in medical education. AI not only changes the tools used for learning but also transforms the nature of knowledge and decision-making processes [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFor the most part, new medical technologies have historically been incremental innovations, requiring only enhanced pedagogical structures to teach. In contrast, AI is not limited to predefined learning pathways; it stimulates a nomological network of individualized learning experiences, transforming medical education from incremental pedagogical adjustments into a personalized, evolving learning journey [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. And this presents teaching challenges, especially for clinical educators who perceive that their roles are being fundamentally redefined, especially because clinical educators have been the primary architects of knowledge dissemination and the arbiters of student competence [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. As AI increasingly assumes roles like providing real-time feedback, identifying learning gaps, and delivering personalized interventions, it risks undermining the traditional authority and autonomy of clinical educators [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. This shift could create friction as clinical educators adapt to the evolving landscape of instructional authority [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. The rapid advancement of AI also demands new competencies, including understanding AI algorithms and managing AI-driven insights\\u0026mdash;requiring a pedagogical reorientation that may not be universally accepted [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Another critical concern lies in the potential erosion of the human elements at the core of medical education. Medical training extends beyond clinical knowledge transmission; it also encompasses mentorship, ethical deliberation, empathy, and the nurturing of professional identity\\u0026mdash;all aspects that thrive on human interaction. While AI can undoubtedly optimize content delivery and personalize learning, these essential human components must not be compromised.\\u003c/p\\u003e \\u003cp\\u003eThe challenge for clinical educators, therefore, lies in striking a balance between the technological precision offered by AI and the empathetic, human-centered nature of medical education to maintain the integrity of professional training [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. To better understand this challenge, we apply the unified theory of acceptance and use of technology (UTAUT) [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e] as a nomological framework to examine clinical educators' readiness, perspectives, and adaptability toward AI integration into their teaching practices.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTheoretical Framework: Nomological Network Perspective on UTAUT\\u003c/h2\\u003e \\u003cp\\u003eTo gain a deeper understanding of the challenges clinical educators face in integrating AI into medical education, a nomological approach provides a valuable framework for evaluating their acceptance and readiness [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Given the complexity of AI integration, this approach enables a thorough assessment of the factors shaping educators' readiness and willingness to adopt new technologies, encompassing mindset shifts, skill development, teaching adjustments, and institutional support [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. UTAUT is particularly relevant for this context because it provides a well-rounded understanding of both the individual and organizational factors influencing the adoption of AI in medical education. It helps bridge the gap between technological capabilities and pedagogical transformation, ensuring that both acceptance and readiness are adequately assessed for viable teaching practices.\\u003c/p\\u003e \\u003cp\\u003eEach UTAUT construct\\u0026mdash;performance expectancy, effort expectancy, social influence, and facilitating conditions\\u0026mdash;plays a vital role in generating a nomological network, establishing a comprehensive perspective to assess clinical educators\\u0026rsquo; readiness, perspectives, and adaptability toward AI integration into their teaching practices. These constructs are not isolated predictors but, instead, act in concert to build a cumulative, networked understanding of the challenges and enablers of technology adoption in medical settings [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003ePerformance expectancy functions as a central node in this network by capturing clinical educators' beliefs regarding AI's potential to enhance the quality of medical education. By connecting performance expectancy to educational outcomes, it becomes part of the nomological network that highlights the impact of perceived effectiveness on clinical educators' willingness to integrate AI technologies in their teaching practices. High performance expectancy, thus, strengthens the network's predictive validity by incorporating empirical insights from clinical educators\\u0026rsquo; anticipated outcomes, demonstrating the link between technology potential and its practical educational benefits.\\u003c/p\\u003e \\u003cp\\u003eEffort expectancy complements this by representing the perceived ease of use. The simplicity or complexity of integrating AI into curricula feeds directly into the broader network, providing insight into practical barriers or facilitators. By incorporating effort expectancy, we identify how ease of use interplays with the perceived benefits, thereby providing a dual perspective on both the advantages and complexities of AI technology integration. The interplay of effort expectancy with performance expectancy constructs a richer nomological network, supporting a more nuanced understanding of teacher readiness that goes beyond isolated predictors [29.\\u003c/p\\u003e \\u003cp\\u003eSocial influence and facilitating conditions extend the network into the social and infrastructural contexts in which AI adoption occurs. Social influence reflects how the attitudes of peers, institutional culture, and perceived normative pressures shape clinical educators' behaviors. Its inclusion within the nomological network underlines the relational dynamics, emphasizing how peer acceptance and institutional encouragement contribute to shaping clinical educators\\u0026rsquo; attitudes toward AI. By analyzing social influence alongside facilitating conditions\\u0026mdash;which addresses the infrastructural and administrative resources available for technology adoption\\u0026mdash;the framework captures the practical context in which clinical educators operate, thereby grounding the theoretical constructs in real-world logistical considerations.\\u003c/p\\u003e \\u003cp\\u003eBy conceptualizing the UTAUT constructs as elements within a nomological network, we establish a cohesive theoretical framework that systematically integrates individual, social, and infrastructural factors. This interconnected approach accounts for the predictive capabilities of each construct and illustrates how these elements interact dynamically (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), thereby generating a more robust explanatory model to assess for readiness.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eThis study employed a qualitative research design utilizing an inductive-to-deductive methodological flow to explore clinical educators' readiness for AI integration into medical education. Borrowing from Fereday and Muir-Cochrane [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e], this approach allowed for a dual exploration of emergent themes through inductive analysis, followed by their systematic interpretation within a predetermined framework\\u0026mdash;in this case, UTAUT [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. This research approached aimed to capture the nuanced perceptions of clinical educators, providing a rich, in-depth understanding of their subjective experiences and attitudes within their unique clinical and educational environments [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. This methodology is particularly well-suited for studying complex social phenomena, as it enables researchers to gather data that deeply reflects the breadth and diversity of participants' experiences [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. This study was approval by the University of Texas at Tyler Institutional Review Board.\\u003c/p\\u003e\\n\\u003ch3\\u003eParticipants\\u003c/h3\\u003e\\n\\u003cp\\u003eParticipants were a convenience sample of clinical educators actively supervising third-year medical students. The educators were selected for their dual roles as both clinical practitioners and mentors, which gave them unique perspectives on the integration of AI into medical education. As frontline facilitators, clinical educators provide invaluable insights into both the opportunities and challenges of AI adoption [\\u003cspan additionalcitationids=\\\"CR34 CR35\\\" citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. Their perspectives are essential for balancing AI's operational efficiency with the humanistic elements vital to medical education. Inclusion criteria required participants to be actively involved in supervising medical students and have familiarity with or interest in AI technologies in healthcare education.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDemographics of interview participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInterview Participant Number\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGender\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTeaching Experience (Years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedical Degree; Certifications\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eLocation\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eDO; AOBFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDallas, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eDO; AOBFM; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMcKinney, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDallas, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDallas, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNorth Garland, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMidlothian, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMcKinney, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNorth Garland, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eUptown, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNorth Garland, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABPM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDallas, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM; ABR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMidway, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM; ABIM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDallas, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7\\u0026thinsp;+\\u0026thinsp;1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD; ABFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eFrisco, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMD;ABFM; ABIM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDallas, TX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNote\\u003c/em\\u003e: M\\u0026thinsp;=\\u0026thinsp;Male, F\\u0026thinsp;=\\u0026thinsp;Female; MD\\u0026thinsp;=\\u0026thinsp;Doctor of Medicine Degree, DO\\u0026thinsp;=\\u0026thinsp;Doctor of Osteopathic Medicine; ABFM\\u0026thinsp;=\\u0026thinsp;American Board of Family Medicine; ABIM\\u0026thinsp;=\\u0026thinsp;American Board of Internal Medicine; AOBFM\\u0026thinsp;=\\u0026thinsp;American Osteopathic Board of Family Medicine\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eData Collection\\u003c/h3\\u003e\\n\\u003cp\\u003e Individual, recorded, semi-structured interviews were conducted with participants using both face-to-face and virtual formats, depending on availability. The interviews consisted of peer-reviewed open-ended and follow-up questions, allowing participants to share their experiences with AI in medical education. Questions addressed the participants' background, clinical education experiences, and perceptions of AI's impact on teaching and learning. Follow-up questions were used to extract details about AI-related challenges, enablers, and ethical concerns. Interviews were conducted until data saturation was reached, ensuring that no new themes emerged [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. All interviews were transcribed verbatim, and deidentified codes were assigned to each participant to maintain confidentiality.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe integration of inductive and deductive approaches was crucial for this study. The inductive phase provided a grounded understanding of participants' experiences, free from preconceived biases, while the deductive phase offered a structured way to interpret these themes using the UTAUT framework. This dual approach is particularly valuable in complex educational settings where emerging technologies are reshaping practices. By first allowing emergent insights and then aligning them with established technology adoption theories, the study effectively captured educators' perspectives and contextualized them within broader theoretical constructs [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eInductive Phase\\u003c/h2\\u003e \\u003cp\\u003eThe initial phase of analysis was inductive, aiming to allow themes to emerge directly from the raw data without imposing preconceived categories or hypotheses. The participant interviews were designed to be flexible, allowing participants to freely express their experiences, which was crucial for capturing the diverse perspectives and attitudes of those involved in integrating AI into medical education [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAfter each participant reviewed and confirmed their transcript, we begin an initial round of thematic analysis [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. During this inductive coding phase, emergent (common) themes were identified and characterized into generalized categories [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. Thematic analysis was conducted independently by multiple researchers, each systematically identifying key patterns and themes from the data. Once the individual analyses were completed, we engaged in detailed discussions to compare findings, resolve discrepancies, and collaboratively refine the themes through deliberation. This process of researcher triangulation helped ensure the reliability and credibility of the identified themes by minimizing individual bias and promoting a more robust and comprehensive interpretation of the data [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. This inductive phase was critical for understanding the underlying views and concerns of the participants, providing a foundation for subsequent deductive analysis.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eDeductive Phase\\u003c/h3\\u003e\\n\\u003cp\\u003eA deductive approach was applied in the second phase of analysis to classify the inductive themes within the UTAUT framework. This phase involved systematically mapping the identified themes from the inductive coding process to the four core constructs of UTAUT\\u0026mdash;performance expectancy, effort expectancy, social influence, and facilitating conditions (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). To ensure rigor, the mapping process was conducted by multiple researchers, who independently reviewed the themes in relation to the UTAUT constructs. Any discrepancies in categorization were addressed through group discussion and consensus-building [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. This iterative process not only enhanced the reliability of the classifications but also allowed for refinement of the themes to fit more precisely within the theoretical constructs. By using this structured deductive approach, the analysis was able to situate the empirical findings within a well-established theoretical context, thereby increasing both the validity and grounding of the study's conclusions.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCategorization of inductive themes within the UTAUT framework\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUTAUT Construct\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategorization of Inductive Themes for Deductive Analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePerformance Expectancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategorized themes focused on AI's potential benefits for improving personalized learning, diagnostic simulations, and overall educational quality in medical training.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEffort\\u003c/p\\u003e \\u003cp\\u003eExpectancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategorized themes focused on the challenges of AI adoption, focusing on the effort required by educators to understand and integrate AI tools into their teaching.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSocial\\u003c/p\\u003e \\u003cp\\u003eInfluence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategorized themes focused on how institutional culture, peer attitudes, and administrative support influenced educators' decisions to adopt AI.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFacilitating\\u003c/p\\u003e \\u003cp\\u003eConditions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategorized themes focused on the availability of resources, infrastructure, and support for AI adoption, including educators' insights on training, technical support, and policy guidance.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe inductive analysis yielded a richly contextualized contribution of clinical educators' experiences (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), offering nuanced insights into their engagement with AI technologies in teaching. The emergent themes provided the foundation for the subsequent deductive phase (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), where they were systematically mapped into the UTAUT framework constructs. This methodological sequence ensured that the qualitative data remained grounded in participants' experiences while aligning the findings with established theoretical constructs for a more structured interpretation and comprehensive analysis.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eInductive insights: Emergent themes, supporting participant quotes, and interpretative analysis of educators' perspectives on AI integration\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEmergent Themes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSupporting Quotes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQualitative Analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTechnological Learning Curve\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\\"AI isn\\u0026rsquo;t something you can master overnight.\\\" (Participant 2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis reflects the perception of technological complexity, and the steep learning curve associated with mastering AI, highlighting perceived barriers to readiness.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeed for Hands-On, Action-Based Learning\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\\"You can\\u0026rsquo;t just read about it in a textbook or sit through a workshop; you need to be in the trenches using it.\\\" (Participant 5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe need for experiential, hands-on engagement with AI tools is emphasized here, demonstrating belief in learning through active practice rather than passive instruction.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInstitutional Support as a Barrier to Effective AI Integration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\\"Institutions need to provide ongoing training that\\u0026rsquo;s tailored to different skill levels.\\\" (Participant 2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis points to a gap in institutional support and emphasizes the necessity of ongoing, differentiated training as a critical factor in facilitating AI integration.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMentorship as a Crucial Support System\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\\"My mentors have been there to answer questions, offer advice, and even show me practical ways to incorporate AI tools into my daily work.\\\" (Participant 2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe importance of mentorship is highlighted as a critical enabler of AI integration, suggesting that social learning plays a key role in reducing the uncertainty associated with new technologies.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBalancing Human Elements with AI Integration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\\"AI can provide incredible insights, but there is something to be said for being able to truly listen to a patient. You can't teach bedside manner through an algorithm.\\\" (Participant 6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThere is an expressed concern that AI might compromise essential human elements in medical education, such as empathy and patient interaction, suggesting potential resistance to AI if it undermines human-centric training.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDivergent Comfort Levels Between Generational Cohorts\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\\"Students today are pretty tech-savvy and comfortable with using AI.\\\" (Participant 8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis reveals a generational divide, with younger educators and students showing greater comfort with AI, implying that familiarity with technology influences readiness for adoption.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDeductive mapping of emergent themes to UTAUT constructs: A structured analysis of factors influencing AI integration in medical education\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUTAUT Constructs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMapped Themes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMapping Insights\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePerformance\\u003c/p\\u003e \\u003cp\\u003eExpectancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNeed for Hands-On, Action-Based Learning\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to performance expectancy as it captures educators\\u0026rsquo; belief that AI has the potential to significantly enhance the quality of educational outcomes, specifically when integrated in a practical and immersive manner.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePerformance\\u003c/p\\u003e \\u003cp\\u003eExpectancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBalancing Human Elements with AI Integration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to performance expectancy in that educators perceive AI as a tool that should enhance educational quality without detracting from the essential human aspects of medical education, such as empathy and interpersonal skills.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEffort\\u003c/p\\u003e \\u003cp\\u003eExpectancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTechnological Learning Curve\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to effort expectancy, emphasizing the perceived difficulty and learning demands associated with AI integration. Educators' readiness to adopt AI is significantly influenced by their perceptions of how complex or time-consuming it might be to learn and utilize these technologies.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEffort\\u003c/p\\u003e \\u003cp\\u003eExpectancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDivergent Comfort Levels Between Generational Cohorts\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to effort expectancy because it highlights the generational differences in perceived ease of use, where younger educators and students are more comfortable and confident with AI tools compared to their older counterparts.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSocial\\u003c/p\\u003e \\u003cp\\u003eInfluence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMentorship as a Crucial Support System\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to social influence as it captures how mentorship and peer attitudes shape individual educators\\u0026rsquo; willingness to adopt AI.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSocial\\u003c/p\\u003e \\u003cp\\u003eInfluence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eInstitutional Culture and Peer Attitudes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to social influence, emphasizing how an institution\\u0026rsquo;s culture and peer behaviors impact an educator\\u0026rsquo;s readiness to embrace AI, either encouraging adoption through collective support or hindering it through lack of enthusiasm or endorsement.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFacilitating\\u003c/p\\u003e \\u003cp\\u003eConditions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eInstitutional Support as a Barrier to Effective AI Integration\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to facilitating conditions, as it speaks to the availability\\u0026mdash;or lack\\u0026mdash;of necessary resources, infrastructure, and support mechanisms that facilitate effective AI adoption in medical education.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFacilitating\\u003c/p\\u003e \\u003cp\\u003eConditions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAvailability of Resources and Infrastructure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis theme maps to facilitating conditions because it emphasizes the need for a comprehensive support infrastructure to enable educators to integrate AI effectively.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe dual-phased approach not only reinforced the theoretical understanding of the data but also bridged empirical observations with established technology adoption theories, thereby increasing rigor and relevance [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. By integrating the findings into the nomological network of the UTAUT for AI adoption (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), the results establish key relationships among the constructs [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. This process helped to enrich the scholarly discourse by contributing both practical insights and theoretical advancements to the field of medical education research [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eAI integration in medical education presents a pivotal opportunity to transform clinical training. While AI's potential to elevate diagnostic simulations and personalized learning is widely recognized, educators' readiness to adopt these tools remain largely unexplored [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. As one participant noted, \\\"\\u003cem\\u003eAI isn\\u0026rsquo;t something you can master overnight\\u003c/em\\u003e\\\" (Participant 2), emphasizing that successful AI integration hinges on educators' ability to effectively apply it. This shift demands more than superficial engagement; traditional lecture-based models must evolve into interactive, AI-enhanced teaching methods. To prepare future healthcare professionals for an AI-driven landscape, educators need both technical proficiency and adapted teaching strategies, supported by institutional training programs [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOur study used an inductive-to-deductive methodological approach, drawing on the UTAUT to frame our findings. This approach allowed us to explore emergent themes and then map these insights to UTAUT constructs, offering a structured understanding of the factors influencing the integration of AI technology into the teaching practices of clinical educators. The following discussion illustrates how each UTAUT construct\\u0026mdash;performance expectancy, effort expectancy, social influence, and facilitating conditions\\u0026mdash;fundamentally supports the need for a for pedagogical transformation at the interface of AI and medical education.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePerformance Expectancy\\u003c/h2\\u003e \\u003cp\\u003eWhat emerged clearly in our analysis was the central theme of hands-on, action-based learning. AI, with its sophisticated simulations and real-time feedback, provides a unique platform for experiential learning [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. This is not mere abstraction but a tangible evolution in how medical students engage with clinical scenarios. The traditional pedagogical frameworks, steeped in passive knowledge dissemination, must now give way to more interactive and practical learning environments. As the evidence suggests, AI allows students to be immersed in dynamic, risk-free environments that mirror the real-life complexities of patient care [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. In this context, performance expectancy is no longer about passive, or even active learning, but about a new way of learning\\u0026mdash;a radical shift that underscores the need for a new pedagogical model. A student learning with AI differs fundamentally from traditional passive and active learning because AI offers personalized, adaptive experiences that dynamically respond to a student's individual learning needs [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. In traditional passive learning, students absorb information, often without tailored feedback. Even in active learning, while students engage with content through interaction, the approach is usually standardized for a group rather than individualized. With AI, however, the learning process is transformed into a highly adaptive system where the technology continuously assesses the student's performance and provides immediate, customized feedback. AI-driven learning tools can adjust the difficulty level, suggest targeted learning materials, and offer real-time simulations that align with a student's pace, knowledge gaps, and preferences [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. This kind of personalized learning fosters deeper engagement and accelerates mastery, as opposed to the one-size-fits-all methods of traditional teaching. Moreover, AI tools can create immersive environments where students apply skills in practical scenarios, enhancing both cognitive and experiential learning [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. Thus, the AI-driven approach empowers students to take control of their learning journey, bridging gaps in real time and promoting an individualized, hands-on learning experience that surpasses both passive and conventional active learning methods.\\u003c/p\\u003e \\u003cp\\u003eYet, in our rush to embrace AI\\u0026rsquo;s potential, we must pause to consider the equally important theme of balancing human elements with AI integration. AI, while powerful, lacks the ability to teach the nuances of empathy, bedside manner, or the subtleties of human interaction in patient care [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. As one participant wisely noted, \\u0026ldquo;\\u003cem\\u003eYou can\\u0026rsquo;t teach bedside manner through an algorithm\\u003c/em\\u003e\\u0026rdquo; (Participant 6). Here, we find a crucial tension: while AI can enhance educational outcomes, it must not come at the expense of core humanistic values that are integral to healthcare [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]. AI\\u0026rsquo;s power lies in its ability to augment human capabilities, not replace them. This balance between technological advancement and human interaction is essential, lest we risk creating healthcare professionals who are technically proficient but emotionally disconnected.\\u003c/p\\u003e \\u003cp\\u003eThus, the performance expectancy of AI in medical education is a nuanced construct. On the one hand, it promises a revolution in how students engage with clinical content\\u0026mdash;moving beyond the lecture hall or even the hospital room. On the other hand, it requires a careful balancing act, ensuring that as we enhance technical skills, we do not erode the human elements that define effective patient care. This dual requirement of hands-on learning and humanistic balance points to a larger pedagogical transformation that must be embraced by clinical educators if AI is to fulfill its potential in reshaping medical education.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEffort Expectancy\\u003c/h2\\u003e \\u003cp\\u003eAI promises to revolutionize medical education, but such transformation is neither seamless nor instantaneous. Effort expectancy, the perceived ease of integrating AI into teaching, has emerged as a pivotal factor, illustrated by the complex technological readiness of clinical educators. One cannot expect AI mastery to materialize without experiential immersion; as one participant put it, \\u0026ldquo;\\u003cem\\u003eYou can\\u0026rsquo;t just read about it in a textbook... you need to be in the trenches using it\\u003c/em\\u003e\\u0026rdquo; (Participant 5). Herein lies the crux of the issue\\u0026mdash;AI demands hands-on engagement. You do not gain fluency in its operations by passive osmosis, but rather by actively navigating its intricacies.\\u003c/p\\u003e \\u003cp\\u003eThis brings us to the technological readiness and learning curve\\u0026mdash;an unavoidable companion of any transformative tool. The integration of AI into clinical education requires clinical educators to ascend a steep learning curve, one that demands cognitive flexibility and the reconfiguration of long-held pedagogical practices [\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]. It is not just about inserting new technology into old frameworks; it\\u0026rsquo;s about redefining those frameworks altogether. Clinical educators must be equipped with technical training and with conceptual understanding of how AI can augment learning. It is a shift akin to the fourth industrial revolution [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]\\u0026mdash;adapt or be left behind. The data does not merely show a gap in technical proficiency, it highlights a fundamental tension: a system built on tradition now tasked with embracing novel innovation.\\u003c/p\\u003e \\u003cp\\u003eEqually significant is the divergent comfort levels between generational cohorts. AI integration unveils a generational chasm within academia. Younger educators, digital natives, find comfort and agility in adopting AI technologies, while their older counterparts often exhibit hesitation and discomfort. This generational divide is not trivial; it is a symptom of a broader evolutionary mismatch between the rapid advance of technology and the slower pace of human adaptability [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. As one participant candidly remarked, \\u0026ldquo;\\u003cem\\u003eThere\\u0026rsquo;s definitely a divide... younger educators pick this up quickly, while those of us with more traditional training need more time\\u003c/em\\u003e\\u0026rdquo; (Participant 3). This is no mere anecdotal observation\\u0026mdash;it speaks to a structural imbalance in how technological fluency is distributed across age groups. If left unaddressed, this divergence threatens to create a divided educational environment where the younger, AI-proficient educators surge ahead, leaving their older colleagues grappling in the dust of digital inertia.\\u003c/p\\u003e \\u003cp\\u003eThe implications are clear. To navigate this learning curve, there must be institutional frameworks that account for these divergent comfort levels, offering personalized, ongoing support. The key is not just in training clinical educators on AI tools but in reshaping the mindset that has traditionally governed medical education itself. As AI continues to intertwine with the fabric of medical education, institutions must invest in differentiated training programs that recognize these generational disparities, otherwise, we risk creating not just a technological gap, but a pedagogical one [\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSocial Influence\\u003c/h2\\u003e \\u003cp\\u003eWhen we dissect the social mechanisms that either catalyze or hinder AI adoption in medical education, we cannot underestimate the gravity of social influence. Human beings are inherently social creatures, and their behaviors are deeply influenced by the norms, attitudes, and feedback of their peers and institutional surroundings [\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]. In the context of clinical educators, mentorship and peer attitudes become the guiding forces that either accelerate or stymie the integration of AI into their teaching practices [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe theme of mentorship as a crucial support system plays an indispensable role. Just as evolutionary biology teaches us that species learn complex behaviors from more experienced members of the group [\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e], educators, too, rely on mentorship to navigate the complexities of AI integration. One participant succinctly captured this, stating, \\u0026ldquo;\\u003cem\\u003eMy mentors have been there to answer questions and offer practical ways to incorporate AI tools into my daily work\\u003c/em\\u003e\\u0026rdquo; (Participant 2). Mentorship, then, is not a mere courtesy\\u0026mdash;it's the scaffolding that allows clinical educators to transcend their apprehension toward AI technology. It provides real-time, hands-on guidance that textbooks and online tutorials simply cannot replicate. This observation is bolstered by research on learning systems, which highlights that the presence of a mentor not only enhances skill acquisition but also reduces the cognitive load involved in grappling with new technologies [\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBut mentorship is just the first layer of social influence. Let\\u0026rsquo;s consider the broader institutional culture and peer attitudes. A supportive institutional culture\\u0026mdash;where peers and leaders endorse the use of AI technology\\u0026mdash;creates a powerful feedback loop that reinforces adoption. One participant stated it plainly: \\u0026ldquo;\\u003cem\\u003eThe institution\\u0026rsquo;s stance on AI makes all the difference. If your colleagues and leadership aren\\u0026rsquo;t on board, it\\u0026rsquo;s hard to make AI part of the daily routine\\u003c/em\\u003e\\u0026rdquo; (Participant 4). Here we see the phenomenon of social proof in action. When educators perceive that their peers and institutional leaders have embraced AI, they are more likely to engage with it themselves. But this is not just about conformity. It is about the evolutionary advantage of collaboration. Historically, educators excel when they share scholastic insights with their peers [\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e]. When peer attitudes are positive, educators feel empowered to experiment with AI tools, knowing they have the backing of their institution [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. The absence of this support, on the other hand, turns AI into an isolated endeavor\\u0026mdash;a solitary uphill battle that many educators are reluctant to fight.\\u003c/p\\u003e \\u003cp\\u003eThe social influence dynamic also highlights the necessity of an adaptive institutional culture. It is not enough to introduce AI tools and expect clinical educators to naturally integrate them. The institution must create a fertile ground for AI adoption by fostering a culture that values innovation and collaboration. If the institutional leadership champions AI, while providing the resources and professional development required to navigate its intricacies, clinical educators will be more inclined to engage with these tools. Without such institutional alignment, AI is at risk of being a fringe tool, underutilized and often misunderstood [\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFacilitating Conditions\\u003c/h2\\u003e \\u003cp\\u003eWhen we think about the successful integration of AI into educational systems, one might be tempted to focus solely on the technology itself, but this is a trap. The reality is far more nuanced. As we have learned from countless historical frameworks, human ingenuity is often stifled not by the limitations of technology but by the institutions tasked with adopting it [\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]. In the context of AI, the importance of facilitating conditions becomes undeniable. It is not enough to introduce AI tools into medical education\\u0026mdash;without the scaffolding of institutional support and access to resources, these tools remain an untapped potential.\\u003c/p\\u003e \\u003cp\\u003eLet us address institutional support as a barrier to effective AI integration. Participants in this study repeatedly highlighted the lack of structured, ongoing training as a core obstacle to successfully adopting AI. One participant observed, \\u0026ldquo;\\u003cem\\u003eInstitutions need to provide ongoing training that\\u0026rsquo;s tailored to different skill levels\\u003c/em\\u003e\\u0026rdquo; (Participant 2), emphasizing that AI integration cannot be a one-size-fits-all endeavor. Here, we find a central paradox: institutions that are eager to adopt the veneer of innovation often fail to invest in the human capital necessary to sustain it. The result? A disconnect between technology and its practical application. The availability of resources and infrastructure further complicates this dynamic. While institutions may invest in the initial acquisition of AI tools, the long-term success of these tools is contingent upon their application to teaching practices. As one participant noted, \\u0026ldquo;\\u003cem\\u003eYou need more than just access to AI tools; you need the institutional backing to learn, adapt, and troubleshoot\\u003c/em\\u003e\\u0026rdquo; (Participant 4). This points to a deeper issue within the education ecosystem: technological readiness is not achieved merely by providing the tool but by embedding that tool within a robust support structure.\\u003c/p\\u003e \\u003cp\\u003eConsider this: if AI represents the future of medical education, it follows that educators must evolve alongside these advancements. But as we know, this evolution is rarely linear. Institutional barriers act as evolutionary bottlenecks, hindering the adaptation process and slowing the integration of new technologies [\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e]. When the resources needed for proper training and infrastructure are inadequate, educators are left to navigate this technological landscape alone\\u0026mdash;often leading to frustration, burnout, and ultimately, the underutilization of AI [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. This is not just a logistical failure; it is a reasoning mismatch between the promise of AI and the actual capacity of educators to wield it effectively.\\u003c/p\\u003e \\u003cp\\u003eThe facilitating conditions for AI integration are multifaceted. The UTAUT framework underscores that for any technology to be adopted, the right conditions must be in place\\u0026mdash;not just in terms of hardware but in fostering an institutional culture that prioritizes continuous learning, adaptability, and resource allocation. Research consistently shows that when institutions fail to provide these conditions, AI remains a fringe tool, utilized only by the most technologically adept educators while the majority struggle to incorporate it meaningfully into their pedagogy [\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e]. The success of AI integration is as much about institutional scaffolding as it is about the technology itself, and until this is recognized, the gap between potential and practice will persist.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLimitations\\u003c/h2\\u003e \\u003cp\\u003eDespite its contributions to understanding the integration of AI into medical education, this study has several limitations that should be acknowledged. First, the study employed a qualitative case study approach with a convenience sample, limiting the generalizability of the findings. Although this method provided rich, in-depth insights into clinical educators' perspectives, the findings may not fully represent the broader population of medical educators across different regions or institutions. Second, the study\\u0026rsquo;s reliance on semi-structured interviews means that responses were subject to participants' willingness and ability to articulate their experiences, which may introduce some response bias [\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e]. Third, as the study focused primarily on clinical educators\\u0026rsquo; perceptions, it did not directly examine students' experiences or outcomes related to AI integration, thereby presenting an incomplete picture of the pedagogical impacts of AI [\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e]. Last, the rapidly evolving nature of AI technologies means that findings could quickly become outdated, as new advancements continue to transform the educational landscape. Future research should aim to incorporate longitudinal data to track changes over time and broaden the scope by including diverse stakeholders in medical education, such as students and administrative staff, to capture a more holistic view of AI integration.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe findings of this study contribute to the need for a pedagogical transformation in medical education, one that equips clinical educators with the skills and support necessary to integrate AI into their teaching practices. By mapping clinical educators' perceptions to the UTAUT framework, this research highlights the multifaceted challenges and opportunities associated with AI adoption in medicine. The implications are clear: AI integration is not merely a technological endeavor but a pedagogical one that requires notable adjustments in how medical education is structured and delivered. Institutions must rise to the challenge by providing the necessary resources, training, and support to ensure that clinical educators are not only technically prepared but also pedagogically equipped to navigate this evolving technological frontier in medicine.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors confirm contribution to the paper as follows: T.M, G.V. and B.M. did study conception and design. T.M. and G.V. did data collection. R.M. performed data analysis. R.E.C. did interpretation of results, draft manuscript preparation and final edit. All authors reviewed the results and approved the final version of the manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFinancial Disclosure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have no financial disclosure or conflict of interest to report.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study has been approved by the ethics committee of The University of Texas at Tyler Institutional Review Board and Conscious consent was obtained from all participants.\\u0026nbsp;Clinical trial number: not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eLin SY, Mahoney MR, Sinsky CA. 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Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22(1):772. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12909-022-03852-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12909-022-03852-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"medical education, unified theory of acceptance and use of technology (UTAUT), medical pedagogy, nomological network, medical school, healthcare\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5362276/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5362276/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe integration of Artificial Intelligence (AI) into healthcare is transforming medical education, reshaping how diagnostic skills, treatment approaches, and patient care methods are taught. This study investigates the interface of AI and medical education, focusing on the preparedness and views of clinical educators. Using the Unified Theory of Acceptance and Use of Technology as a framework, this research assesses the factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Through an inductive-to-deductive methodology, we conducted semi-structured interviews with 15 clinical educators from the south-central region of the United States who oversee third-year medical students. Key findings of teacher readiness at the interface of AI and medical education centered around 1) the technological learning curve, 2) the need for hands-on, action-based learning, 3) the critical role of institutional support, 4) mentorship as a crucial support system, 5) balancing human elements with AI integration, and 6) divergent comfort levels between generational cohorts. While AI holds promise to reform medical education by fostering adaptive, personalized learning environments, it also raises challenges in preserving essential human elements of patient care. Addressing these challenges demands a strategic, institutionally supported shift in medical pedagogy to ensure that AI integration is both effective and sustainable. The study\\u0026rsquo;s insight into clinical educators' perspectives lay the groundwork for developing AI-ready educational models that balance technical expertise with core humanistic values, supporting a comprehensive approach to medical training in the AI-driven future.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Examining the teacher readiness gap at the interface of artificial intelligence and medical education: A qualitative study of clinical educators\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-12-02 22:49:49\",\"doi\":\"10.21203/rs.3.rs-5362276/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a2cf3fbd-8cb5-4843-990c-20eff56caf01\",\"owner\":[],\"postedDate\":\"December 2nd, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-01-20T09:54:37+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-12-02 22:49:49\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5362276\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5362276\",\"identity\":\"rs-5362276\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}