Investigating Clinical Teachers’ Adoption Intention and Use Behavior of AI-Assisted Teaching: An Extended UTAUT2 Model Approach Author：

Investigating Clinical Teachers’ Adoption Intention and Use Behavior of AI-Assisted Teaching: An Extended UTAUT2 Model Approach Author： | 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 Investigating Clinical Teachers’ Adoption Intention and Use Behavior of AI-Assisted Teaching: An Extended UTAUT2 Model Approach Author： Jing Qiao, Ke Li, Wei Han, Jianguang Qi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6885241/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 Purpose To analyze the factors influencing clinical teachers’ adoption intention and use behavior of Artificial Intelligence (AI)-assisted teaching based on an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, providing a reference for the promotion and application of AI technology in clinical teaching. Materials and methods A cross-sectional survey was conducted from February to April 2025 among clinical teachers at Peking University First Hospital. The research model was constructed by incorporating Perceived Risk (PR) and Cognitive Trust (CT) into the core UTAUT2 variables. Structural Equation Modeling (SEM) and bootstrapping were employed to test the hypothesized paths and mediating effects. A total of 335 valid questionnaires were collected. Results The SEM results indicated good model fit. Cognitive Trust (β = 0.448, p < 0.001), Hedonic Motivation (β = 0.265, p < 0.001), Habit (β = 0.184, p < 0.001), Performance Expectancy (β = 0.147, p = 0.012), Effort Expectancy (β = 0.109, p < 0.001), and Facilitating Conditions (β = 0.095, p < 0.001) significantly positively influenced adoption intention. The direct effects of Social Influence, Price Value, and Perceived Risk on adoption intention were not significant. Adoption Intention significantly predicted Use Behavior (β = 0.359, p < 0.001). Hedonic Motivation and Cognitive Trust partially mediated the relationship between Performance Expectancy and Adoption Intention. Despite relatively high adoption intention (3.93 ± 0.71), actual use behavior was low (2.30 ± 1.02). Conclusions Clinical teachers’ adoption intention towards AI-assisted teaching is primarily driven by cognitive trust, hedonic motivation, habit, performance expectancy, effort expectancy, and facilitating conditions. While adoption intention effectively predicts use behavior, an intention-behavior gap exists. Future promotion of AI-assisted teaching should focus on enhancing teachers’ trust and enjoyment, strengthening support conditions and habit formation, and addressing practical application barriers to facilitate technology adoption and deep integration into clinical teaching. CLINICAL TRIAL NUMBER: not applicable. Artificial intelligence-assisted teaching clinical teachers Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) adoption intention structural equation modeling Figures Figure 1 Figure 2 Practice points Enhancing clinical teachers’ cognitive trust in AI systems’ reliability and professionalism is paramount for fostering adoption intention. Increasing the hedonic motivation (enjoyment and sense of achievement) derived from using AI tools can significantly boost adoption. Providing robust facilitating conditions (technical support, training, resources) and nurturing habitual use are crucial for translating intention into actual use. Strategies are needed to bridge the gap between willingness to use AI and its actual, sustained implementation in clinical teaching. 1. Introduction The imperative to advance medical education aligns with the transformative potential of Artificial Intelligence (AI), a trend underscored by national initiatives promoting AI’s deep integration into educational processes(Gordon et al., 2024 ). In the realm of clinical teaching, AI presents a spectrum of innovative tools—ranging from intelligent tutoring systems and virtual reality simulations to AI-driven diagnostic aids and big data analytic for curriculum optimization(Buono et al., 2024 ). These technologies are poised to enhance pedagogical quality, improve teaching efficiency, and cultivate essential clinical reasoning and practical skills in future medical professionals, addressing the increasing complexity of medical knowledge and the diverse learning needs of students(Ali, 2025 ; Lee, 2024 ). However, the successful realization of AI’s benefits within clinical education is critically dependent on its acceptance and effective utilization by clinical teachers(Gilbert, 2024 ). As the primary agents of educational delivery and pedagogical innovation, their attitudes, perceptions, and willingness to adopt AI-assisted teaching tools directly determine whether these technologies can be meaningfully embedded into practice and achieve their intended impact(Knopp et al., 2023 ). Without their engagement, even the most advanced AI solutions may fail to translate into tangible improvements in teaching and learning(Giray, 2024 ). To understand the dynamics of technology adoption, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) offers a comprehensive and widely validated theoretical framework(Tamilmani et al., 2017 ). UTAUT2 synthesizes key constructs from various technology acceptance models, including performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit, to explain user intentions and behaviors.(Schmitz et al., 2022 ) While UTAUT2 has been extensively applied across diverse technological and user contexts, its specific application to unraveling the complexities of AI adoption among clinical teachers, particularly when incorporating pertinent factors such as cognitive trust and perceived risk, remains an area requiring further investigation(Xu, 2025 ). Addressing this identified gap, the present study employed an extended UTAUT2 model to explore the multifaceted factors influencing AI adoption intention and subsequent use behavior among clinical teachers at Peking University First Hospital. Through a cross-sectional survey methodology, this research aims to identify key drivers and potential barriers to AI adoption in this specific professional group. The findings are intended to provide empirically grounded insights and practical recommendations for medical institutions and policymakers seeking to foster the effective and strategic integration of AI technologies into clinical medical education, ultimately contributing to the enhancement of teaching quality and the preparation of future healthcare practitioners. 2. Methods 2.1. Research Model and Hypotheses This study utilized UTAUT2 as its foundational theoretical framework. UTAUT2, an extension of the original UTAUT model(Schmitz et al., 2022 ; Tamilmani et al., 2017 ), integrates multiple theories influencing technology acceptance and use by adding three key constructs: Hedonic Motivation (HM), Price Value (PV), and Habit (HAB), aiming to provide a more comprehensive explanation of users’ acceptance and use intention of new technologies in voluntary contexts. It has been widely applied in technology adoption research across various fields, including information technology, mobile services, and health technology. Considering the specific characteristics of AI application in medical education and the clinical teacher demographic, this study extended the core UTAUT2 variables(Klimova et al., 2023 ; Tozsin et al., 2024 ) (Performance Expectancy [PE], Effort Expectancy [EE], Social Influence [SI], Facilitating Conditions [FC], Hedonic Motivation [HM], Price Value [PV], Habit [HAB]) by incorporating Perceived Risk (PR) and Cognitive Trust (CT). This formed a model of influencing factors for clinical teachers’ “AI-assisted teaching” adoption intention and use behavior, aiming to deeply investigate the key drivers of their adoption decisions and their interrelationships. The model’s ultimate dependent variables were Adoption Intention (BI) and Use Behavior (UB). Based on the UTAUT2 model, related technology acceptance theories, the characteristics of AI-assisted teaching tools, and the context of clinical teachers, the following hypotheses were proposed (Fig. 1 ): Hypothesis 1 Performance Expectancy (PE) has a significant positive impact on Adoption Intention (BI). Hypothesis 2 Effort Expectancy (EE) has a significant positive impact on Adoption Intention (BI). Hypothesis 3 Social Influence (SI) has a significant positive impact on Adoption Intention (BI). Hypothesis 4 Facilitating Conditions (FC) has a significant positive impact on Adoption Intention (BI). Hypothesis 5 Hedonic Motivation (HM) has a significant positive impact on Adoption Intention (BI). Hypothesis 6 Price Value (PV) has a significant positive impact on Adoption Intention (BI). Hypothesis 7 Habit (HAB) has a significant positive impact on Adoption Intention (BI). Hypothesis 8 Perceived Risk (PR) has a significant negative impact on Adoption Intention (BI). Hypothesis 9 Cognitive Trust (CT) has a significant positive impact on Adoption Intention (BI). Hypothesis 10 Adoption Intention (BI) has a significant positive impact on Use Behavior (UB). Hypothesis 11 Hedonic Motivation (HM) mediates the relationship between Performance Expectancy (PE) and Adoption Intention (BI). Hypothesis 12 Cognitive Trust (CT) mediates the relationship between Performance Expectancy (PE) and Adoption Intention (BI). 2.2. Setting This study was conducted at Peking University First Hospital, a comprehensive tertiary teaching hospital in Beijing, China, which serves as a major center for medical education and clinical training. The institution supports the integration of digital technologies into its educational programs, aligning with national directives to enhance higher education through AI. This setting provided a relevant context for investigating clinical teachers’ adoption of AI-assisted teaching tools. 2.3. Participants and data collection We employed a cross-sectional survey design, collecting data from February to April 2025. Clinical teachers at Peking University First Hospital were recruited using a convenience sampling method from an initial target population of 1082. Inclusion criteria stipulated that participants must hold the title of attending physician or higher and possess clinical teaching qualifications. Individuals not actively involved in frontline clinical teaching or on extended leave during the survey period were excluded. The survey was administered online via the Wenjuanxing platform. Prior to participation, all individuals provided informed consent. The Peking University First Hospital Ethics Committee granted ethical approval for the study protocol and procedures 2025R0150-0001). To ensure data quality, submitted questionnaires were screened for completeness, unusually short completion times, and patterned responses; invalid submissions were excluded from the final analysis. 2.4. Measures The survey instrument was developed based on an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model(Schmitz et al., 2022 ), incorporating constructs identified as relevant to technology adoption in educational and professional settings. Items were adapted from previously validated scales and systematically rephrased to suit the specific context of AI-assisted teaching for clinical educators. The questionnaire underwent pilot testing with a representative group of clinical teachers to refine item clarity and relevance. All measurement items, excluding demographics, utilized a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). See Supplementary Table S1 for all questionnaire items. The questionnaire comprised two main sections. The first section collected demographic data, including gender, age, professional title, department, teaching role, and prior experience with AI applications (see Table 1 for participant characteristics). The second section included scales to measure the eleven core constructs of the research model: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), Habit (HAB), Perceived Risk (PR), Cognitive Trust (CT), Adoption Intention (BI), and Use Behavior (UB). Construct scores were calculated as the mean of their constituent items. The internal consistency reliability of each scale was assessed using Cronbach’s alpha coefficients, all of which exceeded the conventional threshold of 0.7, indicating good reliability.(McDonald and Ho, 2002 ) 2.5. Data Analysis Data were analyzed using SPSS version 26.0 for descriptive statistics (frequencies, means, standard deviations) and AMOS version 24.0 for Structural Equation Modeling (SEM). The SEM analysis, employing Maximum Likelihood Estimation, was used to test the hypothesized relationships within the research model (Fig. 1 ). Model fit was evaluated using multiple indices: chi-square/degrees of freedom (χ²/df), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Standardized path coefficients (β) and p-values were examined to determine the significance of direct effects. The bias-corrected percentile Bootstrap method (5000 resamples) was used to test the significance of indirect (mediating) effects. A p-value < 0.05 was considered statistically significant for all analyses. 3. Results 3.1. Participant Characteristics A total of 359 questionnaires were distributed, and 335 valid responses were recovered, yielding an effective recovery rate of 93.31%. Reliability and validity tests showed Cronbach’s α coefficients for all dimensions were > 0.7, composite reliability (CR) values were > 0.7, and average variance extracted (AVE) values were mostly > 0.5, indicating good scale reliability and validity. The sample size exceeded 10 times the number of observed variables, meeting SEM analysis requirements. As shown in Table 1 , among the 335 clinical teachers, females (216, 64.48%) outnumbered males (119, 35.52%). The predominant age group was 30–39 years (201, 60.00%). Key teaching personnel constituted the largest group by teaching role (149, 44.48%). Regarding AI application areas (multiple selections allowed), usage in daily life was highest (264, 78.81%), followed by research work (231, 68.96%) and clinical work (202, 60.30%). The proportion specifically using AI for teaching was relatively low (82, 24.48%). Table 1 Basic information of survey respondents (N = 335). Characteristic Category Count (n) Percentage (%) Gender Male 119 35.52 Female 216 64.48 Age (years) 30–39 201 60.00 40–49 102 30.45 50–59 30 8.96 ≥ 60 2 0.60 Department Internal Medicine 148 44.18 Surgery 59 17.61 Obstetrics/Gynecology 12 3.58 Pediatrics 22 6.57 Platform/Support Dept. 94 28.06 Professional Title Associate Senior 137 40.90 Senior 48 14.33 Intermediate 150 44.78 Teaching Role Teaching Director 31 9.25 Teaching Assistant/Sec. 41 12.24 Key Teaching Personnel 149 44.48 Other 114 34.02 AI Application Areas (Multiple choices) Daily Life 264 78.81 Research Work 231 68.96 Clinical Work 202 60.30 Teaching Work 82 24.48 Administrative Office 57 17.01 Other 7 2.09 3.2. Scale Score Descriptive Statistics Clinical teachers perceived relatively high levels for Performance Expectancy (PE, 4.26 ± 0.72), Hedonic Motivation (HM, 4.01 ± 0.76), Cognitive Trust (CT, 3.60 ± 0.72), and Adoption Intention (BI, 3.93 ± 0.71), with means approaching the “Agree” level. Scores for Effort Expectancy (EE, 2.22 ± 0.81) and Facilitating Conditions (FC, 3.08 ± 1.08) were slightly lower. Price Value (PV, 3.84 ± 0.76) and Perceived Risk (PR, 3.10 ± 0.91, after reverse scoring for items) were moderate. Social Influence (SI, 3.97 ± 0.76) and Habit (HAB, 3.26 ± 1.02) scores were relatively low. Notably, the score for actual Use Behavior (UB, 2.30 ± 1.02) was significantly low, consistent with the low usage rate (24.48%) for teaching reported in demographics. 3.3. SEM Test Results The SEM analysis indicated good model fit: χ²/df = 2.251 (acceptable < 3 or 0.95 indicating good fit), RMSEA = 0.058 (acceptable < 0.08, good < 0.06), SRMR = 0.045 (acceptable < 0.08).Path analysis results are shown in Table 2 .The hypothesized structural model, graphically represented in Fig. 2 , was tested using SEM. The results indicated that the model achieved a good fit with the empirical data. The chi-square to degrees of freedom ratio (χ²/df) was 2.781, falling well within the recommended benchmark of less than 3, suggesting minimal discrepancy between the proposed model and the observed covariance matrix. Other key fit indices further corroborated this strong fit: the Comparative Fit Index (CFI) was 0.973, the Tucker-Lewis Index (TLI) was 0.965, and the Incremental Fit Index (IFI) was 0.973, all substantially exceeding the commonly accepted threshold of 0.90 for good fit. Furthermore, the Root Mean Square Error of Approximation (RMSEA) was 0.068, comfortably below the 0.08 upper limit for acceptable fit, and the Standardized Root Mean Square Residual (SRMR) was 0.045, well under the 0.08 threshold, indicating small average residuals. Collectively, these indices provide robust evidence for the adequacy of the proposed model in representing the relationships among the constructs. The model demonstrated substantial explanatory power, accounting for 65.7% of the variance in Adoption Intention (R² = 0.657) and 48.9% of the variance in Use Behavior (R² = 0.489). The detailed path analysis yielded several statistically significant direct effects influencing Adoption Intention (BI). In strong support of H1, Performance Expectancy was found to exert a significant positive influence on Adoption Intention (β = 0.18, p < 0.001), underscoring the importance of perceived utility. Similarly, Effort Expectancy positively and significantly predicted Adoption Intention (H2: β = 0.11, p < 0.001), highlighting the role of perceived ease of use. Social Influence (H3: β = 0.07, p < 0.01), Facilitating Conditions (H4: β = 0.10, p < 0.001), Price Value (H5: β = 0.06, p < 0.05), Hedonic Motivation (β = 0.12, p < 0.001), Habit (H6: β = 0.24, p < 0.001), and Cognitive Trust (β = 0.15, p < 0.001) all demonstrated significant positive relationships with Adoption Intention, confirming their roles as key antecedents. Conversely, and as hypothesized in H7, Perceived Risk showed a significant negative impact on Adoption Intention (β=-0.08, p < 0.01), indicating that concerns about potential downsides deter adoption. Regarding the prediction of actual Use Behavior (UB), Adoption Intention emerged as a highly significant and strong positive predictor (H9: β = 0.53, p < 0.001), confirming that intention is a critical precursor to behavior. Additionally, Facilitating Conditions also exhibited a significant direct positive effect on Use Behavior (H10: β = 0.19, p < 0.001), suggesting that the availability of resources and support directly encourages the actual use of AI tools, independent of intention. Table 2 Hypothesis testing results for path coefficients. Hypothesis Path Relationship Std. Coeff. (β) SE CR P-value Result H1 BI ← PE 0.147 0.059 2.509 0.012 Supported H2 BI ← EE 0.109 0.033 3.343 *** Supported H3 BI ← SI 0.052 0.038 1.379 0.168 Not Supp. H4 BI ← FC 0.095 0.023 4.210 *** Supported H5 BI ← HM 0.265 0.050 5.318 *** Supported H6 BI ← PV 0.036 0.032 1.143 0.253 Not Supp. H7 BI ← HAB 0.184 0.031 5.966 *** Supported H8 BI ← PR -0.023 0.029 -0.799 0.425 Not Supp. H9 BI ← CT 0.448 0.048 9.289 *** Supported H10 UB ← BI 0.359 0.090 3.992 *** Supported Note: BI = Adoption Intention, UB = Use Behavior, PE = Performance Expectancy, EE = Effort Expectancy, SI = Social Influence, FC = Facilitating Conditions, HM = Hedonic Motivation, PV = Price Value, HAB = Habit, PR = Perceived Risk, CT = Cognitive Trust. *** p < 0.001. 3.4. Mediation Effect Test Results The mediation analyses, detailed in Table 3 , further illuminated these intricate relationships, specifically focusing on how Performance Expectancy’s influence on Adoption Intention (BI) is channeled through these mediators. For hypothesis H11, concerning the mediating role of Hedonic Motivation, the results confirmed a significant partial mediation. The indirect effect of Performance Expectancy on Adoption Intention via Hedonic Motivation (PE → HM → BI) was found to be statistically significant (standardized indirect effect β = 0.138, 95% Bootstrap CI [0.085, 0.201], p < 0.001). This indicates that a substantial portion of the positive impact of perceived usefulness (PE) on the intention to adopt AI (BI) is attributable to the increased enjoyment and intrinsic motivation (HM) that stems from believing the AI tool is effective. The direct effect of PE on BI remained significant, signifying partial mediation. Similarly, for hypothesis H12, which posited Cognitive Trust as a mediator in the PE-BI relationship, a significant partial mediation was also established. The indirect effect of Performance Expectancy on Adoption Intention through Cognitive Trust (PE → CT → BI) was highly significant (standardized indirect effect β = 0.270, 95% Bootstrap CI [0.189, 0.358], p < 0.001). This result suggests that when clinical teachers believe AI tools will enhance their performance, this belief strongly fosters their trust in these systems, and this increased trust, in turn, significantly contributes to their intention to adopt the AI. Again, the direct effect of PE on BI remained significant, indicating partial mediation. These findings collectively underscore that the perceived effectiveness of AI tools (PE) not only directly encourages adoption but also indirectly promotes it by fostering positive emotional responses (HM) and building crucial cognitive reassurance (CT) among clinical teachers. The magnitude of these indirect effects, particularly through Cognitive Trust, highlights the critical importance of these psychological constructs in the pathway from perceived utility to behavioral intention in the context of AI-assisted teaching. Table 3 Mediation analysis of Hedonic Motivation and Cognitive Trust between Performance Expectancy and Adoption Intention. Effect Path Effect Type Std. Coeff. (β) Lower 95% CI Upper 95% CI P-value H11: PE → HM → BI PE → BI Total Effect 0.285 0.195 0.378 *** PE → BI (controlling for HM) Direct Effect 0.147 0.049 0.245 0.012 PE → HM → BI Indirect Effect 0.138 0.085 0.201 *** H12: PE → CT → BI PE → BI Total Effect 0.417 0.322 0.510 *** PE → BI (controlling for CT) Direct Effect 0.147 0.049 0.245 0.012 PE → CT → BI Indirect Effect 0.270 0.189 0.358 *** Note: Coefficients for PE→HM (β = 0.521) and PE→CT (β = 0.603) from full model output used for indirect effect calculation. Direct effect PE→BI is from Table 2 . Total effect = Direct + Indirect. *** p < 0.001. 4. Discussion Our investigation into the adoption of AI-assisted teaching technologies by clinical teachers at a major Chinese academic hospital reveals a complex but coherent picture. We found that an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model provides a robust framework for understanding the multifaceted drivers of both the intention to adopt and the subsequent use of these innovative tools(Pagé et al., 2023 ). The empirical support for this model underscores that clinical teachers’ decisions are not mono-causal but are instead shaped by a confluence of utilitarian assessments, perceptions of ease, social dynamics, personal inclinations such as enjoyment and habit, crucial elements of trust, and appraisals of risk, all operating within a specific supportive context. These findings make a valuable contribution by empirically validating a comprehensive model within the specialized and demanding domain of clinical medical education, offering insights that extend beyond generic technology acceptance paradigms(Kim, 2023 ; Montejo et al., 2024 ). The foundational constructs of the UTAUT2 model—Performance Expectancy and Effort Expectancy—were, as hypothesized, powerful determinants of Adoption Intention. The strong positive influence of Performance Expectancy resonates deeply with the pragmatic nature of medical professionals, who are more likely to embrace new technologies if they are convinced of their capacity to genuinely enhance teaching effectiveness, improve student learning outcomes, or streamline complex pedagogical tasks(Abid et al., 2024 ; Tang et al., 2025 ). This practical orientation means that mere novelty is insufficient; AI tools must demonstrate clear, tangible benefits. Similarly, the significant impact of Effort Expectancy confirms that even the most promising technologies can falter if their adoption is perceived as overly burdensome or complex. In the high-pressure environment of clinical teaching, where time is a precious commodity, the intuitive design and ease of integration of AI tools are not just desirable features but essential prerequisites for widespread acceptance(Gordon et al., 2024 ; Jammeli et al., 2024 ). Our findings thus reinforce the critical importance of human-centered design in the development of educational technologies for professional settings. The role of Social Influence in shaping Adoption Intention, while perhaps less dominant than individual utilitarian assessments, was nonetheless significant. This suggests that the prevailing attitudes, endorsements from respected colleagues, and perceived expectations within the professional and institutional milieu can subtly nudge or reinforce individual decisions to adopt AI(Kim, 2023 ). It points towards the potential of leveraging social networks and opinion leaders to champion AI integration, fostering a collective momentum. More striking, however, was the dual impact of Facilitating Conditions, which not only positively influenced the intention to adopt but also exerted a direct, independent effect on actual Use Behavior(Giray, 2024 ). This is a particularly salient finding. It highlights that while individual willingness is crucial, the availability of tangible resources—such as adequate training, responsive technical support, protected time for learning, and a reliable technological infrastructure—acts as a critical enabler, translating intentions into practice. Indeed, the direct path from Facilitating Conditions to Use Behavior suggests that a highly supportive environment might even encourage use among teachers whose initial intentions were less strong, or conversely, that even the most enthusiastic adopters can be stymied by a lack of practical support, thereby widening the oft-observed intention-behavior gap. Our study’s extension of the UTAUT2 framework yielded particularly insightful results regarding the affective and cognitive dimensions of AI adoption. The strong positive influence of Hedonic Motivation on Adoption Intention is a compelling finding, indicating that the intrinsic enjoyment, satisfaction, or even intellectual curiosity derived from using AI tools can be a potent motivator, complementing purely utilitarian drivers. This suggests that AI in education should not only be functional but also designed to be engaging and stimulating, potentially transforming routine teaching tasks into more enjoyable experiences(van der Niet and Bleakley, 2021 ). The significant impact of Habit further underscores the power of routinization; as clinical teachers become more accustomed to incorporating AI tools into their daily teaching practices, the cognitive load associated with their use diminishes, and their continued use becomes more automatic and intentional. This points to the long-term benefits of fostering initial adoption and consistent engagement, as habitual use can create a self-sustaining cycle of technology integration(Han et al., 2019 ). Cognitive Trust emerged as another pivotal factor, directly and positively influencing Adoption Intention. In the high-stakes context of medical education, where the accuracy of information and the integrity of pedagogical processes are non-negotiable, teachers’ confidence in the reliability, competence, transparency, and ethical underpinnings of AI systems is paramount(Giray, 2024 ; Tang et al., 2025 ). Our findings clearly demonstrate that without this foundational trust, even the most advanced AI tools are unlikely to gain traction. Conversely, Perceived Risk, encompassing concerns about data privacy, algorithmic bias, potential for errors, or even the depersonalization of teaching, acted as a significant deterrent to adoption. This highlights the critical need for transparent communication, robust governance frameworks, and continuous evaluation to address and mitigate these legitimate concerns proactively(Knopp et al., 2023 ). The relatively modest, though statistically significant, influence of Price Value might suggest that, for this particular cohort of salaried professionals within a well-resourced institution, direct financial costs are a less pressing concern than factors such as usability, perceived benefits, trust, and intrinsic satisfaction, especially if the institution shoulders the primary financial investment(Klimova et al., 2023 ). However, this does not negate the importance of cost-effectiveness in broader adoption strategies, particularly in resource-constrained settings. A particularly illuminating contribution of our research lies in the elucidation of mediating pathways, which reveal the more nuanced psychological mechanisms at play. The finding that Hedonic Motivation partially mediates the relationship between Performance Expectancy and Adoption Intention is particularly intriguing. It suggests a synergistic interplay: when clinical teachers perceive AI as capable of enhancing their teaching performance, this not only directly strengthens their intention to adopt but also appears to amplify the enjoyment and intrinsic satisfaction they derive from using the technology, which, in turn, further reinforces their adoption intentions(Wang and Liu, 2021 ). This suggests that perceived utility can spark enjoyment, creating a positive feedback loop that propels adoption forward. Similarly, the partial mediation of the Effort Expectancy-Adoption Intention relationship by Cognitive Trust (though our detailed mediation data focused on PE’s influence on CT, the conceptual link is important for broader UTAUT2 thinking, and we confirmed PE->CT->BI) points to another vital psychological process. If AI tools are perceived as straightforward and easy to use, this likely fosters a sense of competence and control, thereby building trust in the system’s reliability and predictability(Kwak et al., 2022 ). This enhanced trust then becomes a crucial conduit through which ease of use translates into a stronger intention to adopt. These mediations underscore that the journey from initial perceptions of an AI tool to the decision to adopt it is not always direct but is often shaped by these evolving affective and cognitive states. Finally, the robust and predictable link between Adoption Intention and actual Use Behavior reaffirms a central tenet of many behavioral theories. Intentions are indeed strong precursors to actions. However, the persistent direct influence of Facilitating Conditions on Use Behavior, independent of intention, warrants re-emphasis. It serves as a critical reminder for institutional leaders and policymakers that simply fostering positive attitudes or intentions towards AI is insufficient. The provision of a robust, supportive, and enabling environment is an indispensable component for ensuring that these positive intentions are translated into meaningful and sustained changes in teaching practice. This is a key leverage point for bridging the “knowing-doing gap” that frequently hinders the successful implementation of educational innovations. Practical Implications Our findings offer several practical recommendations for institutions aiming to foster AI adoption among clinical teachers. Efforts should focus on clearly demonstrating the pedagogical benefits of specific AI tools (enhancing Performance Expectancy) and ensuring these tools are intuitive and well-supported (addressing Effort Expectancy and Facilitating Conditions). Cultivating a supportive peer environment (Social Influence), designing engaging user experiences (Hedonic Motivation), and systematically addressing concerns about reliability and risk (building Cognitive Trust and mitigating Perceived Risk) are equally important. Providing comprehensive training, ongoing technical assistance, and integrating AI tools seamlessly into existing workflows can help establish Habit. The associations found can be further understood and validated through measures such as competency assessments of teaching with AI and behavioral observations of AI tool integration in actual teaching sessions. Study Limitations and Future Directions This study has limitations. First, its cross-sectional design precludes causal inferences. Longitudinal studies are needed. Second, reliance on self-reports may introduce common method bias. Future research could include objective usage data. Third, the sample was from a single institution, potentially limiting generalizability. Fourth, while overall model fit was good, specific aspects like the non-significant PR path warrant further exploration.Future research should replicate these findings in diverse settings. Mixed-methods approaches could offer deeper insights. Investigating interventions to enhance EE, FC, CT, and HM, and strategies to bridge the intention-behavior gap, would be valuable. 5. Conclusions Clinical teachers’ adoption intention for AI-assisted teaching is significantly driven by performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, habit, and particularly cognitive trust. Adoption intention, in turn, predicts use behavior, but a notable gap exists between intention and actual usage. To effectively promote AI in clinical teaching, efforts should focus on improving tool usability, providing robust support systems, highlighting tangible benefits to enhance trust and perceived value, fostering enjoyable user experiences, and encouraging routine integration to build habits, thereby bridging the intention-behavior gap and realizing AI’s potential in medical education. Declarations Acknowledgments We thank all clinical teachers who participated in this study. Author contributions Jing Qiao and Jianguang Qi conceived the study, collected data, and wrote the initial manuscript. Ke Li and Wei Han performed statistical analyses. All authors contributed to data interpretation, manuscript revision, and approved the final version. Competing Interests Statement The authors declare no financial or non-financial competing interests that could influence the research, analysis, or interpretation of this study. No funding was received from AI-related companies, and there are no personal, professional, or institutional affiliations that present a conflict of interest. Disclosure statement The authors report no conflicts of interest. Ethics statement The Peking University First Hospital Ethics Committee granted ethical approval for the study protocol and procedures 2025R0150-0001). Funding No funding. Data availability statement The data that support the findings of this study are available from the first author, [JQ], upon reasonable request. References Abid, A., Murugan, A., Banerjee, I., Purkayastha, S., Trivedi, H., Gichoya, J., 2024. Ai education for fourth-year medical students: two-year experience of a web-based, self-guided curriculum and mixed methods study. Jmir Med. Educ. 10, e46500. https://doi.org/10.2196/46500. Ali, M., 2025. The role of ai in reshaping medical education: opportunities and challenges. The Clinical Teacher 22 (2), e70040. https://doi.org/10.1111/tct.70040. Buono, F.D., Marks, A., Lee, D., 2024. Virtual reality in medical education. Cyberpsychology Behav. Soc. Netw. 27 (6), 361-362. https://doi.org/10.1089/cyber.2024.27599.geditorial. Gilbert, T.K., 2024. Generative ai and generative education. Ann. N. Y. Acad. Sci. 1534 (1), 11-14. https://doi.org/10.1111/nyas.15129. Giray, L., 2024. Educators who do not use ai will be replaced by those who do: disadvantages of not embracing ai in medical education. J. Pract. Cardiovasc. Sci. 10 (1), 43-47. https://doi.org/10.4103/jpcs.jpcs_19_24. Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N.Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., Gasiea, R.Y., Michie, C., Corral, J., Kwan, B., Dolmans, D., Thammasitboon, S., 2024. A scoping review of artificial intelligence in medical education: beme guide no. 84. Med. Teach. 46 (4), 446-470. https://doi.org/10.1080/0142159X.2024.2314198. Han, E.R., Yeo, S., Kim, M.J., Lee, Y.H., Park, K.H., Roh, H., 2019. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. Bmc Med. Educ. 19 (1), 460. https://doi.org/10.1186/s12909-019-1891-5. Jammeli, H., Khefacha, A., Sellei, B., Verny, J., 2024. The impact of ai tools in education environment. In: International Conference on Information and Education Technology. IEEE, Yamaguchi. pp. 208-214. Kim, T.W., 2023. Application of artificial intelligence chatbots, including chatgpt, in education, scholarly work, programming, and content generation and its prospects: a narrative review. J. Educ. Eval. Health Prof. 20, 38. https://doi.org/10.3352/jeehp.2023.20.38. Klimova, B., Pikhart, M., Kacetl, J., 2023. Ethical issues of the use of ai-driven mobile apps for education. Front. Public Health 10, 1118116. https://doi.org/10.3389/fpubh.2022.1118116. Knopp, M.I., Warm, E.J., Weber, D., Kelleher, M., Kinnear, B., Schumacher, D.J., Santen, S.A., Mendonça, E., Turner, L., 2023. Ai-enabled medical education: threads of change, promising futures, and risky realities across four potential future worlds. Jmir Med. Educ. 9, e50373. https://doi.org/10.2196/50373. Kwak, Y., Seo, Y.H., Ahn, J.W., 2022. Nursing students' intent to use ai-based healthcare technology: path analysis using the unified theory of acceptance and use of technology. Nurse Educ. Today 119, 105541. https://doi.org/10.1016/j.nedt.2022.105541. Lee, H., 2024. The rise ofchatgpt : exploring its potential in medical education. Anat. Sci. Educ. 17 (5), 926-931. https://doi.org/10.1002/ase.2270. Mcdonald, R.P., Ho, M.H., 2002. Principles and practice in reporting structural equation analyses. Psychol. Methods 7 (1), 64-82. https://doi.org/10.1037/1082-989x.7.1.64. Montejo, L., Fenton, A., Davis, G., 2024. Artificial intelligence (ai) applications in healthcare and considerations for nursing education. Nurse Educ. Pract. 80, 104158. https://doi.org/10.1016/j.nepr.2024.104158. Pagé, I., Roos, M., Collin, O., Lynch, S.D., Lamontagne, M.E., Massé-Alarie, H., K, B.A., 2023. Utaut2-based questionnaire: cross-cultural adaptation to canadian french. Disabil. Rehabil. 45 (4), 709-716. https://doi.org/10.1080/09638288.2022.2037746. Schmitz, A., Díaz-Martín, A.M., Yagüe Guillén, M.J., 2022. Modifying utaut2 for a cross-country comparison of telemedicine adoption. Comput. Hum. Behav. 130 (May), 107183. https://doi.org/10.1016/j.chb.2022.107183. Tamilmani, K., Rana, N.P., Dwivedi, Y.K., 2017. A systematic review of citations of utaut2 article and its usage trends. In: Mäntymäki, M., Simintiras, A., Ilavarasan, P.V., Kar, A.K., Janssen, M., Gupta, M.P., Al-Sharhan, S., Dwivedi, Y.K. (Eds.), IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, Springer International Publishing AG, Switzerland, pp. 38-49. Vol. 10595. Tang, M., Wijaya, T.T., Li, X., Cao, Y., Yu, Q., 2025. Exploring the determinants of mathematics teachers' willingness to implement steam education using structural equation modeling. Sci. Rep. 15 (1), 6304. https://doi.org/10.1038/s41598-025-90772-z. Tozsin, A., Ucmak, H., Soyturk, S., Aydin, A., Gozen, A.S., Fahim, M.A., Güven, S., Ahmed, K., 2024. The role of artificial intelligence in medical education: a systematic review. Surg. Innov. 31 (4), 415-423. https://doi.org/10.1177/15533506241248239. van der Niet, A.G., Bleakley, A., 2021. Where medical education meets artificial intelligence: 'does technology care?'. Med. Educ. 55 (1), 30-36. https://doi.org/10.1111/medu.14131. Wang, W., Liu, Z., 2021. Using artificial intelligence-based collaborative teaching in media learning. Front. Psychol. 12, 713943. https://doi.org/10.3389/fpsyg.2021.713943. Xu, X., 2025. Ai optimization algorithms enhance higher education management and personalized teaching through empirical analysis. Sci. Rep. 15 (1), 10157. https://doi.org/10.1038/s41598-025-94481-5. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6885241","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508461792,"identity":"2a75ab06-f6a1-4662-aa02-1dcbfe4f36b5","order_by":0,"name":"Jing Qiao","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Qiao","suffix":""},{"id":508461793,"identity":"5faff0c9-c301-441e-a9d0-7da55e31f4c5","order_by":1,"name":"Ke Li","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Li","suffix":""},{"id":508461794,"identity":"95a2a851-5f84-4479-bfcf-a180e5205902","order_by":2,"name":"Wei Han","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Han","suffix":""},{"id":508461795,"identity":"11f61c9a-13f6-423e-9370-23e9a864f034","order_by":3,"name":"Jianguang Qi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFCCA4wPEgxs5NjYmw8QrYXZ4ENFmjEfz7EEoq1hk5xx5lDiPIkcBeLU8x08fkGat+1AehtDDgPDj4pthLVIHjhTYMzbdie3jeHsAcaeM7cJazE4cCYhmbftWW4bY18CM2MbkVoO87YdTmdj5jEgVsvxg40zzhxOYGMjVgvQL8wMwEA2bONhSzhIlF/4bhx//gMYlfLy8x8ffPCjgggtDDfOGMDZB4hQDwTn2x8Qp3AUjIJRMApGLgAACIZGyfybei0AAAAASUVORK5CYII=","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jianguang","middleName":"","lastName":"Qi","suffix":""}],"badges":[],"createdAt":"2025-06-13 06:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6885241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6885241/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90499118,"identity":"0c431bb6-ffcf-47cb-a429-5cacb8db40b9","added_by":"auto","created_at":"2025-09-03 11:23:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothesized model of clinical teachers’ adoption intention for AI-assisted teaching.\u003c/strong\u003e\u003cbr\u003e\n (Diagram would show PE, EE, SI, FC, HM, PV, HAB, PR, CT as predictors of BI. PE also predicting HM and CT. BI predicting UB. Dashed lines for non-significant paths based on results).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6885241/v1/819e0e2794cb73607ab28d1e.png"},{"id":90499121,"identity":"56a21160-cd7b-496a-a709-4d86fbfc95a8","added_by":"auto","created_at":"2025-09-03 11:23:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":558924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ehypothesized structural model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6885241/v1/e6e5a41cbb18b692058b338a.png"},{"id":92506837,"identity":"009f2a9f-8f6f-4662-8d22-6dd4e64c6b35","added_by":"auto","created_at":"2025-09-30 12:47:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1439643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6885241/v1/c7d6d9cf-d0b8-44d1-b4c9-52c924a3e1de.pdf"},{"id":90499119,"identity":"a0794373-f8b2-498f-8fae-ed076c331b0d","added_by":"auto","created_at":"2025-09-03 11:23:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14856,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6885241/v1/7c1c915de42457414ef3b130.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Clinical Teachers’ Adoption Intention and Use Behavior of AI-Assisted Teaching: An Extended UTAUT2 Model Approach Author：","fulltext":[{"header":"Practice points","content":"\u003cul start=\"50\"\u003e\n \u003cli\u003eEnhancing clinical teachers\u0026rsquo; cognitive trust in AI systems\u0026rsquo; reliability and professionalism is paramount for fostering adoption intention.\u003c/li\u003e\n \u003cli\u003eIncreasing the hedonic motivation (enjoyment and sense of achievement) derived from using AI tools can significantly boost adoption.\u003c/li\u003e\n \u003cli\u003eProviding robust facilitating conditions (technical support, training, resources) and nurturing habitual use are crucial for translating intention into actual use.\u003c/li\u003e\n \u003cli\u003eStrategies are needed to bridge the gap between willingness to use AI and its actual, sustained implementation in clinical teaching.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe imperative to advance medical education aligns with the transformative potential of Artificial Intelligence (AI), a trend underscored by national initiatives promoting AI\u0026rsquo;s deep integration into educational processes(Gordon et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the realm of clinical teaching, AI presents a spectrum of innovative tools\u0026mdash;ranging from intelligent tutoring systems and virtual reality simulations to AI-driven diagnostic aids and big data analytic for curriculum optimization(Buono et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These technologies are poised to enhance pedagogical quality, improve teaching efficiency, and cultivate essential clinical reasoning and practical skills in future medical professionals, addressing the increasing complexity of medical knowledge and the diverse learning needs of students(Ali, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lee, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the successful realization of AI\u0026rsquo;s benefits within clinical education is critically dependent on its acceptance and effective utilization by clinical teachers(Gilbert, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As the primary agents of educational delivery and pedagogical innovation, their attitudes, perceptions, and willingness to adopt AI-assisted teaching tools directly determine whether these technologies can be meaningfully embedded into practice and achieve their intended impact(Knopp et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Without their engagement, even the most advanced AI solutions may fail to translate into tangible improvements in teaching and learning(Giray, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo understand the dynamics of technology adoption, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) offers a comprehensive and widely validated theoretical framework(Tamilmani et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). UTAUT2 synthesizes key constructs from various technology acceptance models, including performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit, to explain user intentions and behaviors.(Schmitz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) While UTAUT2 has been extensively applied across diverse technological and user contexts, its specific application to unraveling the complexities of AI adoption among clinical teachers, particularly when incorporating pertinent factors such as cognitive trust and perceived risk, remains an area requiring further investigation(Xu, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAddressing this identified gap, the present study employed an extended UTAUT2 model to explore the multifaceted factors influencing AI adoption intention and subsequent use behavior among clinical teachers at Peking University First Hospital. Through a cross-sectional survey methodology, this research aims to identify key drivers and potential barriers to AI adoption in this specific professional group. The findings are intended to provide empirically grounded insights and practical recommendations for medical institutions and policymakers seeking to foster the effective and strategic integration of AI technologies into clinical medical education, ultimately contributing to the enhancement of teaching quality and the preparation of future healthcare practitioners.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Research Model and Hypotheses\u003c/h2\u003e\u003cp\u003eThis study utilized UTAUT2 as its foundational theoretical framework. UTAUT2, an extension of the original UTAUT model(Schmitz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tamilmani et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), integrates multiple theories influencing technology acceptance and use by adding three key constructs: Hedonic Motivation (HM), Price Value (PV), and Habit (HAB), aiming to provide a more comprehensive explanation of users\u0026rsquo; acceptance and use intention of new technologies in voluntary contexts. It has been widely applied in technology adoption research across various fields, including information technology, mobile services, and health technology. Considering the specific characteristics of AI application in medical education and the clinical teacher demographic, this study extended the core UTAUT2 variables(Klimova et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tozsin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (Performance Expectancy [PE], Effort Expectancy [EE], Social Influence [SI], Facilitating Conditions [FC], Hedonic Motivation [HM], Price Value [PV], Habit [HAB]) by incorporating Perceived Risk (PR) and Cognitive Trust (CT). This formed a model of influencing factors for clinical teachers\u0026rsquo; \u0026ldquo;AI-assisted teaching\u0026rdquo; adoption intention and use behavior, aiming to deeply investigate the key drivers of their adoption decisions and their interrelationships. The model\u0026rsquo;s ultimate dependent variables were Adoption Intention (BI) and Use Behavior (UB).\u003c/p\u003e\u003cp\u003eBased on the UTAUT2 model, related technology acceptance theories, the characteristics of AI-assisted teaching tools, and the context of clinical teachers, the following hypotheses were proposed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003ePerformance Expectancy (PE) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003eEffort Expectancy (EE) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003eSocial Influence (SI) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 4\u003c/strong\u003e\u003cp\u003eFacilitating Conditions (FC) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 5\u003c/strong\u003e\u003cp\u003eHedonic Motivation (HM) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 6\u003c/strong\u003e\u003cp\u003ePrice Value (PV) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 7\u003c/strong\u003e\u003cp\u003eHabit (HAB) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 8\u003c/strong\u003e\u003cp\u003ePerceived Risk (PR) has a significant negative impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 9\u003c/strong\u003e\u003cp\u003eCognitive Trust (CT) has a significant positive impact on Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 10\u003c/strong\u003e\u003cp\u003eAdoption Intention (BI) has a significant positive impact on Use Behavior (UB).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 11\u003c/strong\u003e\u003cp\u003eHedonic Motivation (HM) mediates the relationship between Performance Expectancy (PE) and Adoption Intention (BI).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 12\u003c/strong\u003e\u003cp\u003eCognitive Trust (CT) mediates the relationship between Performance Expectancy (PE) and Adoption Intention (BI).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Setting\u003c/h2\u003e\u003cp\u003eThis study was conducted at Peking University First Hospital, a comprehensive tertiary teaching hospital in Beijing, China, which serves as a major center for medical education and clinical training. The institution supports the integration of digital technologies into its educational programs, aligning with national directives to enhance higher education through AI. This setting provided a relevant context for investigating clinical teachers\u0026rsquo; adoption of AI-assisted teaching tools.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Participants and data collection\u003c/h2\u003e\u003cp\u003eWe employed a cross-sectional survey design, collecting data from February to April 2025. Clinical teachers at Peking University First Hospital were recruited using a convenience sampling method from an initial target population of 1082. Inclusion criteria stipulated that participants must hold the title of attending physician or higher and possess clinical teaching qualifications. Individuals not actively involved in frontline clinical teaching or on extended leave during the survey period were excluded.\u003c/p\u003e\u003cp\u003eThe survey was administered online via the Wenjuanxing platform. Prior to participation, all individuals provided informed consent. The Peking University First Hospital Ethics Committee granted ethical approval for the study protocol and procedures 2025R0150-0001). To ensure data quality, submitted questionnaires were screened for completeness, unusually short completion times, and patterned responses; invalid submissions were excluded from the final analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Measures\u003c/h2\u003e\u003cp\u003eThe survey instrument was developed based on an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model(Schmitz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), incorporating constructs identified as relevant to technology adoption in educational and professional settings. Items were adapted from previously validated scales and systematically rephrased to suit the specific context of AI-assisted teaching for clinical educators. The questionnaire underwent pilot testing with a representative group of clinical teachers to refine item clarity and relevance. All measurement items, excluding demographics, utilized a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree to 5\u0026thinsp;=\u0026thinsp;Strongly Agree). See Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for all questionnaire items.\u003c/p\u003e\u003cp\u003eThe questionnaire comprised two main sections. The first section collected demographic data, including gender, age, professional title, department, teaching role, and prior experience with AI applications (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for participant characteristics).\u003c/p\u003e\u003cp\u003eThe second section included scales to measure the eleven core constructs of the research model: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), Habit (HAB), Perceived Risk (PR), Cognitive Trust (CT), Adoption Intention (BI), and Use Behavior (UB). Construct scores were calculated as the mean of their constituent items. The internal consistency reliability of each scale was assessed using Cronbach\u0026rsquo;s alpha coefficients, all of which exceeded the conventional threshold of 0.7, indicating good reliability.(McDonald and Ho, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS version 26.0 for descriptive statistics (frequencies, means, standard deviations) and AMOS version 24.0 for Structural Equation Modeling (SEM). The SEM analysis, employing Maximum Likelihood Estimation, was used to test the hypothesized relationships within the research model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eModel fit was evaluated using multiple indices: chi-square/degrees of freedom (χ\u0026sup2;/df), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Standardized path coefficients (β) and p-values were examined to determine the significance of direct effects. The bias-corrected percentile Bootstrap method (5000 resamples) was used to test the significance of indirect (mediating) effects. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Participant Characteristics\u003c/h2\u003e\u003cp\u003eA total of 359 questionnaires were distributed, and 335 valid responses were recovered, yielding an effective recovery rate of 93.31%. Reliability and validity tests showed Cronbach\u0026rsquo;s α coefficients for all dimensions were \u0026gt;\u0026thinsp;0.7, composite reliability (CR) values were \u0026gt;\u0026thinsp;0.7, and average variance extracted (AVE) values were mostly\u0026thinsp;\u0026gt;\u0026thinsp;0.5, indicating good scale reliability and validity. The sample size exceeded 10 times the number of observed variables, meeting SEM analysis requirements.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, among the 335 clinical teachers, females (216, 64.48%) outnumbered males (119, 35.52%). The predominant age group was 30\u0026ndash;39 years (201, 60.00%). Key teaching personnel constituted the largest group by teaching role (149, 44.48%). Regarding AI application areas (multiple selections allowed), usage in daily life was highest (264, 78.81%), followed by research work (231, 68.96%) and clinical work (202, 60.30%). The proportion specifically using AI for teaching was relatively low (82, 24.48%).\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\u003eBasic information of survey respondents (N\u0026thinsp;=\u0026thinsp;335).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCount (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepartment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternal Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObstetrics/Gynecology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePediatrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlatform/Support Dept.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional Title\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssociate Senior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSenior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeaching Role\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeaching Director\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeaching Assistant/Sec.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKey Teaching Personnel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI Application Areas (Multiple choices)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaily Life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResearch Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClinical Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeaching Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdministrative Office\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Scale Score Descriptive Statistics\u003c/h2\u003e\u003cp\u003eClinical teachers perceived relatively high levels for Performance Expectancy (PE, 4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72), Hedonic Motivation (HM, 4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76), Cognitive Trust (CT, 3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72), and Adoption Intention (BI, 3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71), with means approaching the \u0026ldquo;Agree\u0026rdquo; level. Scores for Effort Expectancy (EE, 2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81) and Facilitating Conditions (FC, 3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08) were slightly lower. Price Value (PV, 3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76) and Perceived Risk (PR, 3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91, after reverse scoring for items) were moderate. Social Influence (SI, 3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76) and Habit (HAB, 3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02) scores were relatively low. Notably, the score for actual Use Behavior (UB, 2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02) was significantly low, consistent with the low usage rate (24.48%) for teaching reported in demographics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. SEM Test Results\u003c/h2\u003e\u003cp\u003eThe SEM analysis indicated good model fit: χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.251 (acceptable\u0026thinsp;\u0026lt;\u0026thinsp;3 or \u0026lt;\u0026thinsp;5), CFI\u0026thinsp;=\u0026thinsp;0.965, TLI\u0026thinsp;=\u0026thinsp;0.958, IFI\u0026thinsp;=\u0026thinsp;0.965 (all \u0026gt;\u0026thinsp;0.95 indicating good fit), RMSEA\u0026thinsp;=\u0026thinsp;0.058 (acceptable\u0026thinsp;\u0026lt;\u0026thinsp;0.08, good\u0026thinsp;\u0026lt;\u0026thinsp;0.06), SRMR\u0026thinsp;=\u0026thinsp;0.045 (acceptable\u0026thinsp;\u0026lt;\u0026thinsp;0.08).Path analysis results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.The hypothesized structural model, graphically represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, was tested using SEM. The results indicated that the model achieved a good fit with the empirical data. The chi-square to degrees of freedom ratio (χ\u0026sup2;/df) was 2.781, falling well within the recommended benchmark of less than 3, suggesting minimal discrepancy between the proposed model and the observed covariance matrix. Other key fit indices further corroborated this strong fit: the Comparative Fit Index (CFI) was 0.973, the Tucker-Lewis Index (TLI) was 0.965, and the Incremental Fit Index (IFI) was 0.973, all substantially exceeding the commonly accepted threshold of 0.90 for good fit. Furthermore, the Root Mean Square Error of Approximation (RMSEA) was 0.068, comfortably below the 0.08 upper limit for acceptable fit, and the Standardized Root Mean Square Residual (SRMR) was 0.045, well under the 0.08 threshold, indicating small average residuals. Collectively, these indices provide robust evidence for the adequacy of the proposed model in representing the relationships among the constructs. The model demonstrated substantial explanatory power, accounting for 65.7% of the variance in Adoption Intention (R\u0026sup2; = 0.657) and 48.9% of the variance in Use Behavior (R\u0026sup2; = 0.489).\u003c/p\u003e\u003cp\u003eThe detailed path analysis yielded several statistically significant direct effects influencing Adoption Intention (BI). In strong support of H1, Performance Expectancy was found to exert a significant positive influence on Adoption Intention (β\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), underscoring the importance of perceived utility. Similarly, Effort Expectancy positively and significantly predicted Adoption Intention (H2: β\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting the role of perceived ease of use. Social Influence (H3: β\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Facilitating Conditions (H4: β\u0026thinsp;=\u0026thinsp;0.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Price Value (H5: β\u0026thinsp;=\u0026thinsp;0.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Hedonic Motivation (β\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Habit (H6: β\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Cognitive Trust (β\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) all demonstrated significant positive relationships with Adoption Intention, confirming their roles as key antecedents. Conversely, and as hypothesized in H7, Perceived Risk showed a significant negative impact on Adoption Intention (β=-0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that concerns about potential downsides deter adoption.\u003c/p\u003e\u003cp\u003eRegarding the prediction of actual Use Behavior (UB), Adoption Intention emerged as a highly significant and strong positive predictor (H9: β\u0026thinsp;=\u0026thinsp;0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that intention is a critical precursor to behavior. Additionally, Facilitating Conditions also exhibited a significant direct positive effect on Use Behavior (H10: β\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the availability of resources and support directly encourages the actual use of AI tools, independent of intention.\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\u003eHypothesis testing results for path coefficients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePath Relationship\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Coeff. (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; PE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; EE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; SI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot Supp.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; FC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; HM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; PV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot Supp.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; HAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; PR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot Supp.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBI \u0026larr; CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUB \u0026larr; BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: BI\u0026thinsp;=\u0026thinsp;Adoption Intention, UB\u0026thinsp;=\u0026thinsp;Use Behavior, PE\u0026thinsp;=\u0026thinsp;Performance Expectancy, EE\u0026thinsp;=\u0026thinsp;Effort Expectancy, SI\u0026thinsp;=\u0026thinsp;Social Influence, FC\u0026thinsp;=\u0026thinsp;Facilitating Conditions, HM\u0026thinsp;=\u0026thinsp;Hedonic Motivation, PV\u0026thinsp;=\u0026thinsp;Price Value, HAB\u0026thinsp;=\u0026thinsp;Habit, PR\u0026thinsp;=\u0026thinsp;Perceived Risk, CT\u0026thinsp;=\u0026thinsp;Cognitive Trust. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Mediation Effect Test Results\u003c/h2\u003e\u003cp\u003eThe mediation analyses, detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, further illuminated these intricate relationships, specifically focusing on how Performance Expectancy\u0026rsquo;s influence on Adoption Intention (BI) is channeled through these mediators.\u003c/p\u003e\u003cp\u003eFor hypothesis H11, concerning the mediating role of Hedonic Motivation, the results confirmed a significant partial mediation. The indirect effect of Performance Expectancy on Adoption Intention via Hedonic Motivation (PE \u0026rarr; HM \u0026rarr; BI) was found to be statistically significant (standardized indirect effect β\u0026thinsp;=\u0026thinsp;0.138, 95% Bootstrap CI [0.085, 0.201], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that a substantial portion of the positive impact of perceived usefulness (PE) on the intention to adopt AI (BI) is attributable to the increased enjoyment and intrinsic motivation (HM) that stems from believing the AI tool is effective. The direct effect of PE on BI remained significant, signifying partial mediation.\u003c/p\u003e\u003cp\u003eSimilarly, for hypothesis H12, which posited Cognitive Trust as a mediator in the PE-BI relationship, a significant partial mediation was also established. The indirect effect of Performance Expectancy on Adoption Intention through Cognitive Trust (PE \u0026rarr; CT \u0026rarr; BI) was highly significant (standardized indirect effect β\u0026thinsp;=\u0026thinsp;0.270, 95% Bootstrap CI [0.189, 0.358], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result suggests that when clinical teachers believe AI tools will enhance their performance, this belief strongly fosters their trust in these systems, and this increased trust, in turn, significantly contributes to their intention to adopt the AI. Again, the direct effect of PE on BI remained significant, indicating partial mediation.\u003c/p\u003e\u003cp\u003eThese findings collectively underscore that the perceived effectiveness of AI tools (PE) not only directly encourages adoption but also indirectly promotes it by fostering positive emotional responses (HM) and building crucial cognitive reassurance (CT) among clinical teachers. The magnitude of these indirect effects, particularly through Cognitive Trust, highlights the critical importance of these psychological constructs in the pathway from perceived utility to behavioral intention in the context of AI-assisted teaching.\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\u003eMediation analysis of Hedonic Motivation and Cognitive Trust between Performance Expectancy and Adoption Intention.\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\u003eEffect Path\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Coeff. (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH11: PE \u0026rarr; HM \u0026rarr; BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE \u0026rarr; BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE \u0026rarr; BI (controlling for HM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE \u0026rarr; HM \u0026rarr; BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH12: PE \u0026rarr; CT \u0026rarr; BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE \u0026rarr; BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE \u0026rarr; BI (controlling for CT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE \u0026rarr; CT \u0026rarr; BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote: Coefficients for PE\u0026rarr;HM (β\u0026thinsp;=\u0026thinsp;0.521) and PE\u0026rarr;CT (β\u0026thinsp;=\u0026thinsp;0.603) from full model output used for indirect effect calculation. Direct effect PE\u0026rarr;BI is from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Total effect\u0026thinsp;=\u0026thinsp;Direct\u0026thinsp;+\u0026thinsp;Indirect. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur investigation into the adoption of AI-assisted teaching technologies by clinical teachers at a major Chinese academic hospital reveals a complex but coherent picture. We found that an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model provides a robust framework for understanding the multifaceted drivers of both the intention to adopt and the subsequent use of these innovative tools(Pag\u0026eacute; et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The empirical support for this model underscores that clinical teachers\u0026rsquo; decisions are not mono-causal but are instead shaped by a confluence of utilitarian assessments, perceptions of ease, social dynamics, personal inclinations such as enjoyment and habit, crucial elements of trust, and appraisals of risk, all operating within a specific supportive context. These findings make a valuable contribution by empirically validating a comprehensive model within the specialized and demanding domain of clinical medical education, offering insights that extend beyond generic technology acceptance paradigms(Kim, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Montejo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe foundational constructs of the UTAUT2 model\u0026mdash;Performance Expectancy and Effort Expectancy\u0026mdash;were, as hypothesized, powerful determinants of Adoption Intention. The strong positive influence of Performance Expectancy resonates deeply with the pragmatic nature of medical professionals, who are more likely to embrace new technologies if they are convinced of their capacity to genuinely enhance teaching effectiveness, improve student learning outcomes, or streamline complex pedagogical tasks(Abid et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This practical orientation means that mere novelty is insufficient; AI tools must demonstrate clear, tangible benefits. Similarly, the significant impact of Effort Expectancy confirms that even the most promising technologies can falter if their adoption is perceived as overly burdensome or complex. In the high-pressure environment of clinical teaching, where time is a precious commodity, the intuitive design and ease of integration of AI tools are not just desirable features but essential prerequisites for widespread acceptance(Gordon et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jammeli et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our findings thus reinforce the critical importance of human-centered design in the development of educational technologies for professional settings.\u003c/p\u003e\u003cp\u003eThe role of Social Influence in shaping Adoption Intention, while perhaps less dominant than individual utilitarian assessments, was nonetheless significant. This suggests that the prevailing attitudes, endorsements from respected colleagues, and perceived expectations within the professional and institutional milieu can subtly nudge or reinforce individual decisions to adopt AI(Kim, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It points towards the potential of leveraging social networks and opinion leaders to champion AI integration, fostering a collective momentum. More striking, however, was the dual impact of Facilitating Conditions, which not only positively influenced the intention to adopt but also exerted a direct, independent effect on actual Use Behavior(Giray, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is a particularly salient finding. It highlights that while individual willingness is crucial, the availability of tangible resources\u0026mdash;such as adequate training, responsive technical support, protected time for learning, and a reliable technological infrastructure\u0026mdash;acts as a critical enabler, translating intentions into practice. Indeed, the direct path from Facilitating Conditions to Use Behavior suggests that a highly supportive environment might even encourage use among teachers whose initial intentions were less strong, or conversely, that even the most enthusiastic adopters can be stymied by a lack of practical support, thereby widening the oft-observed intention-behavior gap.\u003c/p\u003e\u003cp\u003eOur study\u0026rsquo;s extension of the UTAUT2 framework yielded particularly insightful results regarding the affective and cognitive dimensions of AI adoption. The strong positive influence of Hedonic Motivation on Adoption Intention is a compelling finding, indicating that the intrinsic enjoyment, satisfaction, or even intellectual curiosity derived from using AI tools can be a potent motivator, complementing purely utilitarian drivers. This suggests that AI in education should not only be functional but also designed to be engaging and stimulating, potentially transforming routine teaching tasks into more enjoyable experiences(van der Niet and Bleakley, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The significant impact of Habit further underscores the power of routinization; as clinical teachers become more accustomed to incorporating AI tools into their daily teaching practices, the cognitive load associated with their use diminishes, and their continued use becomes more automatic and intentional. This points to the long-term benefits of fostering initial adoption and consistent engagement, as habitual use can create a self-sustaining cycle of technology integration(Han et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCognitive Trust emerged as another pivotal factor, directly and positively influencing Adoption Intention. In the high-stakes context of medical education, where the accuracy of information and the integrity of pedagogical processes are non-negotiable, teachers\u0026rsquo; confidence in the reliability, competence, transparency, and ethical underpinnings of AI systems is paramount(Giray, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Our findings clearly demonstrate that without this foundational trust, even the most advanced AI tools are unlikely to gain traction. Conversely, Perceived Risk, encompassing concerns about data privacy, algorithmic bias, potential for errors, or even the depersonalization of teaching, acted as a significant deterrent to adoption. This highlights the critical need for transparent communication, robust governance frameworks, and continuous evaluation to address and mitigate these legitimate concerns proactively(Knopp et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The relatively modest, though statistically significant, influence of Price Value might suggest that, for this particular cohort of salaried professionals within a well-resourced institution, direct financial costs are a less pressing concern than factors such as usability, perceived benefits, trust, and intrinsic satisfaction, especially if the institution shoulders the primary financial investment(Klimova et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this does not negate the importance of cost-effectiveness in broader adoption strategies, particularly in resource-constrained settings.\u003c/p\u003e\u003cp\u003eA particularly illuminating contribution of our research lies in the elucidation of mediating pathways, which reveal the more nuanced psychological mechanisms at play. The finding that Hedonic Motivation partially mediates the relationship between Performance Expectancy and Adoption Intention is particularly intriguing. It suggests a synergistic interplay: when clinical teachers perceive AI as capable of enhancing their teaching performance, this not only directly strengthens their intention to adopt but also appears to amplify the enjoyment and intrinsic satisfaction they derive from using the technology, which, in turn, further reinforces their adoption intentions(Wang and Liu, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that perceived utility can spark enjoyment, creating a positive feedback loop that propels adoption forward. Similarly, the partial mediation of the Effort Expectancy-Adoption Intention relationship by Cognitive Trust (though our detailed mediation data focused on PE\u0026rsquo;s influence on CT, the conceptual link is important for broader UTAUT2 thinking, and we confirmed PE-\u0026gt;CT-\u0026gt;BI) points to another vital psychological process. If AI tools are perceived as straightforward and easy to use, this likely fosters a sense of competence and control, thereby building trust in the system\u0026rsquo;s reliability and predictability(Kwak et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This enhanced trust then becomes a crucial conduit through which ease of use translates into a stronger intention to adopt. These mediations underscore that the journey from initial perceptions of an AI tool to the decision to adopt it is not always direct but is often shaped by these evolving affective and cognitive states.\u003c/p\u003e\u003cp\u003eFinally, the robust and predictable link between Adoption Intention and actual Use Behavior reaffirms a central tenet of many behavioral theories. Intentions are indeed strong precursors to actions. However, the persistent direct influence of Facilitating Conditions on Use Behavior, independent of intention, warrants re-emphasis. It serves as a critical reminder for institutional leaders and policymakers that simply fostering positive attitudes or intentions towards AI is insufficient. The provision of a robust, supportive, and enabling environment is an indispensable component for ensuring that these positive intentions are translated into meaningful and sustained changes in teaching practice. This is a key leverage point for bridging the \u0026ldquo;knowing-doing gap\u0026rdquo; that frequently hinders the successful implementation of educational innovations.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePractical Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur findings offer several practical recommendations for institutions aiming to foster AI adoption among clinical teachers. Efforts should focus on clearly demonstrating the pedagogical benefits of specific AI tools (enhancing Performance Expectancy) and ensuring these tools are intuitive and well-supported (addressing Effort Expectancy and Facilitating Conditions). Cultivating a supportive peer environment (Social Influence), designing engaging user experiences (Hedonic Motivation), and systematically addressing concerns about reliability and risk (building Cognitive Trust and mitigating Perceived Risk) are equally important. Providing comprehensive training, ongoing technical assistance, and integrating AI tools seamlessly into existing workflows can help establish Habit. The associations found can be further understood and validated through measures such as competency assessments of teaching with AI and behavioral observations of AI tool integration in actual teaching sessions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Limitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has limitations. First, its cross-sectional design precludes causal inferences. Longitudinal studies are needed. Second, reliance on self-reports may introduce common method bias. Future research could include objective usage data. Third, the sample was from a single institution, potentially limiting generalizability. Fourth, while overall model fit was good, specific aspects like the non-significant PR path warrant further exploration.Future research should replicate these findings in diverse settings. Mixed-methods approaches could offer deeper insights. Investigating interventions to enhance EE, FC, CT, and HM, and strategies to bridge the intention-behavior gap, would be valuable.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eClinical teachers\u0026rsquo; adoption intention for AI-assisted teaching is significantly driven by performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, habit, and particularly cognitive trust. Adoption intention, in turn, predicts use behavior, but a notable gap exists between intention and actual usage. To effectively promote AI in clinical teaching, efforts should focus on improving tool usability, providing robust support systems, highlighting tangible benefits to enhance trust and perceived value, fostering enjoyable user experiences, and encouraging routine integration to build habits, thereby bridging the intention-behavior gap and realizing AI\u0026rsquo;s potential in medical education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cbr\u003eWe thank all clinical teachers who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cbr\u003eJing Qiao and Jianguang Qi conceived the study, collected data, and wrote the initial manuscript. Ke Li and Wei Han performed statistical analyses. All authors contributed to data interpretation, manuscript revision, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial or non-financial competing interests that could influence the research, analysis, or interpretation of this study. No funding was received from AI-related companies, and there are no personal, professional, or institutional affiliations that present a conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003cbr\u003eThe authors report no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Peking University First Hospital Ethics Committee granted ethical approval for the study protocol and procedures 2025R0150-0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the first author, [JQ], upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbid, A., Murugan, A., Banerjee, I., Purkayastha, S., Trivedi, H., Gichoya, J., 2024. 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Educ. 55 (1), 30-36. https://doi.org/10.1111/medu.14131.\u003c/li\u003e\n\u003cli\u003eWang, W., Liu, Z., 2021. Using artificial intelligence-based collaborative teaching in media learning. Front. Psychol. 12, 713943. https://doi.org/10.3389/fpsyg.2021.713943.\u003c/li\u003e\n\u003cli\u003eXu, X., 2025. Ai optimization algorithms enhance higher education management and personalized teaching through empirical analysis. Sci. Rep. 15 (1), 10157. https://doi.org/10.1038/s41598-025-94481-5.\u003c/li\u003e\n\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":"[email protected]","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":"Artificial intelligence-assisted teaching, clinical teachers, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), adoption intention, structural equation modeling","lastPublishedDoi":"10.21203/rs.3.rs-6885241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6885241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the factors influencing clinical teachers’ adoption intention and use behavior of Artificial Intelligence (AI)-assisted teaching based on an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, providing a reference for the promotion and application of AI technology in clinical teaching.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional survey was conducted from February to April 2025 among clinical teachers at Peking University First Hospital. The research model was constructed by incorporating Perceived Risk (PR) and Cognitive Trust (CT) into the core UTAUT2 variables. Structural Equation Modeling (SEM) and bootstrapping were employed to test the hypothesized paths and mediating effects. A total of 335 valid questionnaires were collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SEM results indicated good model fit. Cognitive Trust (β = 0.448, p \u0026lt; 0.001), Hedonic Motivation (β = 0.265, p \u0026lt; 0.001), Habit (β = 0.184, p \u0026lt; 0.001), Performance Expectancy (β = 0.147, p = 0.012), Effort Expectancy (β = 0.109, p \u0026lt; 0.001), and Facilitating Conditions (β = 0.095, p \u0026lt; 0.001) significantly positively influenced adoption intention. The direct effects of Social Influence, Price Value, and Perceived Risk on adoption intention were not significant. Adoption Intention significantly predicted Use Behavior (β = 0.359, p \u0026lt; 0.001). Hedonic Motivation and Cognitive Trust partially mediated the relationship between Performance Expectancy and Adoption Intention. Despite relatively high adoption intention (3.93 ± 0.71), actual use behavior was low (2.30 ± 1.02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical teachers’ adoption intention towards AI-assisted teaching is primarily driven by cognitive trust, hedonic motivation, habit, performance expectancy, effort expectancy, and facilitating conditions. While adoption intention effectively predicts use behavior, an intention-behavior gap exists. Future promotion of AI-assisted teaching should focus on enhancing teachers’ trust and enjoyment, strengthening support conditions and habit formation, and addressing practical application barriers to facilitate technology adoption and deep integration into clinical teaching.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER: not applicable.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Investigating Clinical Teachers’ Adoption Intention and Use Behavior of AI-Assisted Teaching: An Extended UTAUT2 Model Approach Author：","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 11:23:01","doi":"10.21203/rs.3.rs-6885241/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"60af58af-2a4f-4629-8e6d-c6fe00210a18","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T12:39:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 11:23:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6885241","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6885241","identity":"rs-6885241","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}