The Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) for Medical Students: Psychometric Properties of the Turkish Version

The Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) for Medical Students: Psychometric Properties of the Turkish Version | 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 The Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) for Medical Students: Psychometric Properties of the Turkish Version Gülay ESEN, Erol ESEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8977743/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 Background: The rapid integration of artificial intelligence (AI) into healthcare practice has resulted in new demands on medical education systems. This study aimed to adapt the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ), originally developed for health profession students, into Turkish and examine its psychometric properties among Turkish medical students. Method: Through convenience sampling, 238 medical students (56.7% female; M = 22.32, SD = 2.58) from various state and foundation universities participated. The scale adaptation process followed a standardized translation and back-translation protocol. Confirmatory factor analysis (CFA) was conducted to examine construct validity, while convergent validity was tested via correlations with digital nativeness and individual innovativeness measures. Internal consistency was evaluated using Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). Results: CFA supported the AAIQ's original unidimensional structure, which demonstrated good model fit indices: χ²(35) = 42.51, χ²/df = 1.21, SRMR = .06, RMSEA = .07, TLI = .99, and CFI = .99. Reliability analyses indicated high internal consistency, with α = .88, CR = .95, and AVE = .67. Notably, 81.1% of participants expressed support for the integration of AI into medical education, although only 21.0% had received formal training related to AI. Conclusion: The Turkish version of the AAIQ shows strong validity and reliability, making it a robust tool for evaluating medical students' attitudes toward artificial intelligence. Artificial intelligence scale adaptation medical education medical students INTRODUCTION Artificial intelligence (AI) encompasses a broad array of technologies designed to simulate human intelligence and perform tasks traditionally requiring human cognitive functions, such as reasoning, learning, and problem-solving ( 1 ). At its core, AI involves the creation of computer algorithms capable of replicating behaviors associated with intelligent human action ( 1 ). This technological domain includes subfields such as machine learning, deep learning, and natural language processing, each contributing to AI systems' increasing sophistication and versatility ( 2 ). AI technologies are rapidly integrated into diverse sectors, including law, finance, computer science, and industrial manufacturing ( 3 ). Among these, the field of medicine has emerged as one of the most dynamically evolving areas of AI application, supported by the exponential growth of healthcare data and significant investments by the technology industry ( 2 ). Today, AI tools are used across numerous medical specialties to assist clinicians in radiology, pathology, and precision oncology, contributing to improved diagnostic accuracy and personalized treatment planning ( 4 , 5 ). Health systems are increasingly adopting AI to enhance service efficiency and clinical decision-making. For example, machine learning–based models are now used to predict the risk of patient admissions in emergency departments, thereby reducing unnecessary hospitalizations ( 6 ). In gastroenterology, AI-integrated endoscopic systems aid in detecting and classifying pathological lesions ( 7 ), while in oncology, AI contributes to cancer diagnosis and grading through automated image analysis ( 8 ). Beyond clinical care, AI has also become a transformative tool in medical education. AI-driven learning platforms are being developed to expose students to clinical scenarios and support experiential learning through simulated diagnostic practice ( 9 ). This accelerating integration of AI into healthcare practice places new demands on medical education systems. Medical faculties are increasingly expected to prepare future physicians to understand and effectively collaborate with AI technologies. To this end, there is a pressing need to assess medical students’ readiness and attitudes toward AI. Understanding their perceptions is essential to informing curriculum development and ensuring that AI adoption in healthcare is supported by a workforce with appropriate knowledge and acceptance. However, empirical studies examining medical students' attitudes toward AI in Turkey remain limited. One significant barrier is the lack of validated and culturally adapted measurement tools. To address this gap, the present study aimed to adapt the Attitudes Towards AI Questionnaire for Health Professions’ Students (AAIQ)—initially developed by Al-Qerem et al. (2023) into Turkish and evaluate its psychometric properties among medical students ( 10 ). By doing so, the study seeks to contribute to the growing body of literature on AI in medical education and to facilitate future research and curriculum design in the Turkish context. MATERIAL AND METHODS Participants This study used convenience sampling to recruit participants in various medical faculties across Turkey. Convenience sampling is a non-probability technique that allows researchers to easily access individuals who are members of the target population and are both eligible and willing to participate in the study ( 11 ). A total of 238 medical students participated, comprising 56.7% female (n = 135) and 43.3% male (n = 103) students from both state (85.3%) and foundation universities (14.7%). Participants’ ages ranged from 17 to 32 years ( M = 22.32, SD = 2.58). Socioeconomic status was assessed using a self-reported 10-point Likert-type scale (1 = lowest, 10 = highest), yielding a mean score of 5.55 (SD = 2.03). Detailed demographic characteristics of the sample are presented in Table 1 . Table 1 Sociodemographic Characteristics of Participants Gender f % Female 135 56.7 Male 103 43.3 University type State University 203 85.3 Foundation university 35 14.7 Grade 1st year 57 23.9 2nd year 44 18.5 3rd year 48 20.2 4th year 30 12.6 5th year 28 11.8 6th year 31 13.0 Data Collection Instruments Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) The AAIQ, developed by Al-Qerem et al. ( 10 ), is a unidimensional instrument of 10 items designed to assess health profession students' attitudes toward AI. Items are rated on a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). Total scores range from 10 to 50, with higher scores indicating more favorable attitudes toward AI use in healthcare. In the original development study, the scale demonstrated good internal consistency (Cronbach’s α = .87) ( 10 ). In the current study, the Turkish version of the AAIQ yielded a Cronbach’s alpha coefficient of .88, indicating high internal consistency. Digital Native Assessment Scale (DNAS) Originally developed by Teo ( 12 ), the DNAS assesses university students’ perceptions of themselves as digital natives. The Turkish adaptation was conducted by Teo et al. ( 13 ). The scale comprises 21 items across four subscales: growing up with technology, comfort with multitasking, preference for visual communication, and preference for instant gratification and rewards. A total score is computed by summing the subscale scores. Each item is rated on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree), with total scores ranging from 21 to 147. In the Turkish validation study, construct validity indicators were acceptable (χ² = 673.539; χ²/df = 3.893; TLI = .90; CFI = .91; RMSEA = .07; SRMR = .068) ( 13 ). Composite reliability (CR) and average variance extracted (AVE) values were reported as .83 and .50, respectively ( 13 ). The DNAS demonstrated good reliability in the present study, with Cronbach’s α = .84. Individual Innovativeness Scale (IIS) The IIS, developed by Hurt et al. ( 14 ), measures an individual’s general tendency toward innovation. The scale consists of 20 items, of which 12 are positively worded and eight are negatively worded. Items are rated on a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). The Turkish adaptation was performed by Kılıçer and Odabaşı ( 15 ), and demonstrated acceptable internal consistency (α = .82). The scale’s reliability was reaffirmed in the current study, with a Cronbach’s alpha of .88. Procedure This study was approved by the Non-Interventional Clinical Research Ethics Committee of Sivas Cumhuriyet University, Faculty of Medicine (Approval No: 2025-01/45, Date: 16.01.2025). Before adapting the target measurement tool into Turkish, formal permission was obtained from the original developer of the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) ( 10 ). The adaptation process employed a thorough translation and back-translation methodology to ensure that the original and Turkish versions maintained both semantic and linguistic equivalence ( 16 ). Two independent researchers, fluent in English, translated the AAIQ into Turkish. The research team then reviewed and combined these forward translations into a single version. An academic expert in English Language and Literature subsequently back-translated the synthesized Turkish version into English. This back-translated version was submitted to the original scale developer, who confirmed its semantic accuracy and cultural appropriateness. A pilot study was conducted with 10 medical students to evaluate the clarity and comprehensibility of the adapted survey items. Due to the positive feedback from participants, no major revisions were needed, allowing the primary data collection phase to proceed as planned. The finalized survey instruments were distributed online using Google Forms between January and February 2025. Medical faculty members helped share the survey link with students. Participants received detailed information about the purpose of the study, and electronic informed consent was obtained prior to participation. Data Analysis Before conducting the primary analyses, the dataset was thoroughly examined for data entry errors, missing values, and multivariate outliers. The outliers were screened using standardized z-scores and Mahalanobis distance, and no significant outliers were identified. The data distribution was assessed using skewness and kurtosis statistics, with all values falling within the acceptable range of -1.0 to + 1.0, indicating that the data approximated a normal distribution ( 17 ). Statistical analyses included descriptive statistics, construct validity, convergent validity, and reliability testing. Construct validity of the Turkish version of the AAIQ was assessed using Confirmatory Factor Analysis (CFA). Model fit was evaluated based on multiple indices as recommended by Kline ( 18 ) and Schreiber et al. ( 19 ), including the Chi-square statistic (χ²), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). To evaluate convergent validity, Pearson correlation coefficients were calculated between the AAIQ scores and those of the Digital Native Assessment Scale (DNAS) and the Individual Innovativeness Scale (IIS). Internal consistency reliability was examined using Cronbach’s alpha, along with Composite Reliability (CR) and Average Variance Extracted (AVE) values, to further support the instrument's psychometric soundness. RESULTS Descriptive Statistics This section presents findings regarding the participants’ educational exposure to artificial intelligence (AI), their attitudes toward AI integration in medical education, and their usage patterns of AI applications across academic and professional contexts. A majority of the participants (81.1%) expressed support for the incorporation of AI applications into medical and residency training curricula. However, only 21.0% reported receiving formal training on AI, whether online or offline. Similarly, a substantial portion (80.3%) stated that they had not enrolled in any AI-related course as part of their medical education. While 46.6% indicated utilizing AI applications during medical coursework, the remaining participants reported no use. Detailed statistics are presented in Table 2 . Table 2 Participants’ educational experiences on AI f % Have you previously undergone any training on AI, whether through online or offline channels? Yes 50 21.0 No 188 79.0 Have you ever enrolled in a course on AI as part of your medical education? Yes 47 19.7 No 191 80.3 Have you utilized artificial intelligence applications in any of your medical courses? Yes 111 46.6 No 127 53.4 Do you agree that artificial intelligence should be included in medical school and residency training curricula? Yes 193 81.1 No 45 18.9 Furthermore, the frequency of AI use among participants across different purposes was explored. The data revealed that AI tools were most commonly used for academic-related tasks, such as preparing for exams, conducting research, and completing homework. The least common usage was for grammar and spelling verification, though this still showed moderate engagement. The frequency distribution of usage across various purposes is provided in Table 3 . Table 3 Frequency of participants' use of AI for various purposes Never Seldom Sometimes Often Always f % f % f % f % f % How often do you utilize AI to prepare for your exams? 45 18.9 52 21.8 40 16.8 32 13.4 69 29.0 How often do you utilize AI in your homework? 39 16.4 43 18.1 48 20.2 46 19.3 62 26.1 How often do you utilize AI to conduct your research? 36 15.1 50 21.0 47 19.7 43 18.1 62 26.1 How often do you utilize AI for personal development and to enhance skills in various domains? 44 18.5 48 20.2 43 18.1 39 16.4 64 26.9 How often do you utilize AI to verify the quality of writing in terms of spelling and grammar? 63 26.5 29 12.2 44 18.5 46 19.3 56 23.5 Participants were also asked to identify the AI applications they were familiar with, had used in daily life, had applied professionally in healthcare, or had utilized within their medical education. The most widely recognized AI tools among participants included ChatGPT (f = 123), Google Assistant (f = 72), and Siri (f = 49). In terms of actual usage in daily life, ChatGPT was again the most frequently used application (f = 98), followed by Google Assistant (f = 47) and Siri (f = 31). More specialized health-related AI tools with high awareness included IBM Watson Health (f = 52), DeepMind Health (f = 27), and Aidoc (f = 16). However, actual use of such tools in a professional or clinical education context remained relatively low: IBM Watson Health (f = 18), ChatGPT (f = 10), Aidoc (f = 7), and others, such as Google Health, DeepMind Health, and Zebra Medical Vision, showed limited engagement. Regarding the use of AI tools specifically within medical courses, the most commonly reported tools were ChatGPT (f = 38), IBM Watson Health (f = 5), DeepMind Health (f = 4), Aidoc (f = 4), and others, including PathAI, Medscape AI Search, and XERTE (each with lower frequencies). Construct Validity of the AAIQ Turkish Form To evaluate the construct validity of the Turkish version of the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ), Confirmatory Factor Analysis (CFA) was conducted based on the scale’s original unidimensional structure. Given the sensitivity of χ² to sample size, the overall fit was evaluated using multiple goodness-of-fit indices. CFA supported the AAIQ's original unidimensional structure, which demonstrated good model fit indices: χ² (35) = 42.51, χ²/df = 1.21, SRMR = .06, RMSEA = .07, TLI = .99, and CFI = .99. These values fulfill the conventional criteria for a good model fit, as outlined in the literature ( 17 – 22 ), and confirm the unidimensionality of the scale in the context of Turkish medical students. The goodness-of-fit statistics are summarized in Table 4 . Table 4 Goodness-of-Fit Indices for the AAIQ Turkish Version Fit Indices Cut-off for Good Fit Measure χ 2 /df χ 2 /df ≤ 2 (Tabachnik and Fidell, 2007) 1.21 SRMR SRMR ≤ .08 (Hu & Bentler, 1999) .06 RMSEA RMSEA ≤ .07 (Steiger, 2007) .07 TLI TLI ≥ .95 (Schreiber et al. 2006) .99 CFI CFI ≥ .95 (Browne & Cudeck, 1993) .99 The standardized factor loadings for the 10 AAIQ items ranged from .43 to .96, all exceeding the acceptable threshold of .40 suggested by Pituch and Stevens ( 23 , 24 ). These values support the factorial integrity of the Turkish version. Detailed loadings and confidence intervals are provided in Table 5 . Table 5 Factor loadings for the AAIQ Turkish form items Items Standardized factor loadings Std. Error P 1st Item 0.81 0.047 < .001 2nd Item 0.96 0.049 < .001 3rd Item 0.73 0.046 < .001 4th Item 0.96 0.048 < .001 5th Item 0.66 0.044 < .001 6th Item 0.87 0.047 < .001 7th Item 0.96 0.050 < .001 8th Item 0.86 0.047 < .001 9th Item 0.80 0.049 < .001 10th Item 0.43 0.038 < .001 Note. 95% CIs are reported for unstandardized loadings; standardized loadings are presented without CIs. Convergent Validity of the AAIQ Turkish Form To assess convergent validity, Pearson correlation coefficients were calculated between the AAIQ scores and scores obtained from two theoretically related constructs: the Individual Innovativeness Scale (IIS) and the Digital Native Assessment Scale (DNAS). As hypothesized, significant positive correlations were observed: AAIQ–IIS ( r = .24, p < .01) and AAIQ–DNAS ( r = .22, p < .01). These findings provide empirical support for the convergent validity of the Turkish adaptation of the AAIQ in Table 6 . Table 6 Correlations Among Attitudes Towards AI, Innovativeness, and Digital Nativeness 1. 1. 2. 3. Attitudes towards AI (AAIQ) - 2. Innovativeness levels (IIS) .24** - 3. Digital nativeness levels (DNAS) .22** .35** - ** Correlation is significant at the 0.01 level *Correlation is significant at the 0.05 level Reliability of the AAIQ Turkish Form To evaluate the internal consistency reliability, Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE) values were computed. The Cronbach’s alpha coefficient for the 10-item scale was .88, indicating strong internal consistency. Furthermore, the CR was calculated as .95, and the AVE as .67, exceeding the commonly accepted thresholds of .70 and .50, respectively ( 25 – 27 ). Item-total correlations ranged from .48 to .81, further supporting the scale's reliability and internal coherence. These results demonstrate that the Turkish version of the AAIQ is a psychometrically robust instrument for assessing medical students’ attitudes toward artificial intelligence. DISCUSSION With the rapid advancement of digital technologies, artificial intelligence (AI) has emerged as a transformative force across numerous domains, particularly in healthcare. AI systems are now instrumental in enhancing diagnostic accuracy, streamlining clinical workflows, and expanding the reach of precision medicine( 28 – 30 ). In parallel, the need to prepare healthcare professionals—especially future physicians—for AI-integrated clinical environments has become increasingly evident. Despite these developments, valid and reliable instruments for assessing medical students’ attitudes toward AI remain scarce in Turkey ( 32 – 38 ). This study addressed this gap by adapting the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) into Turkish and evaluating its psychometric properties among medical students. The CFA results confirmed the unidimensional factor structure of the original scale, with all model fit indices indicating a good fit. Factor loadings ranged from .43 to .96, mirroring those reported in the original development study ( 10 ). Additionally, the Turkish version demonstrated excellent internal consistency (α = .88), composite reliability (CR = .95), and average variance extracted (AVE = .67), aligning with recommended psychometric thresholds ( 39 ). These findings validate the Turkish form of the AAIQ as a reliable and structurally robust tool for measuring medical students' attitudes toward AI. The findings related to convergent validity reveal a significant positive correlation between participants' levels of innovativeness and their attitudes toward AI. This aligns with previous research indicating that as individuals' levels of innovativeness increase, they are more likely to use AI and develop more positive attitudes toward it ( 40 – 43 ). Additionally, a positive correlation was observed between participants' digital nativeness and their attitudes toward AI. Our findings align with existing literature indicating that digital natives are more likely to adopt new technologies, including AI-based technologies. ( 44 , 45 ). The relationships among these variables provide evidence for the convergent validity of the Turkish version of the AAIQ. Beyond psychometrics, our findings indicate that Turkish medical students are highly prepared to engage with AI-enabled healthcare. This suggests the need for incorporating AI literacy into the curriculum, focusing on aspects such as data quality, bias, explainability, and human oversight. Additionally, clear institutional policies, faculty development, and monitoring of inequities are essential for governance. By embedding these skills into both undergraduate and residency training, we can better align student enthusiasm with the safe, effective, and equitable use of AI in clinical and public health settings. Beyond psychometric validation, the study provides valuable insights into the current state of AI engagement among medical students in Turkey. Most students reported using AI tools such as ChatGPT, Google Assistant, and IBM Watson Health for academic tasks, including exam preparation, research, and homework. These findings are consistent with international studies, highlighting widespread adoption of generative AI tools in medical education settings ( 46 – 49 ). ChatGPT stood out as the most frequently recognized and utilized among these tools, likely due to its accessibility, popularity, and capacity to generate quick, coherent responses. Despite this high level of informal usage, formal exposure to AI in medical education remains limited. Only 21.0% of students had received any form of AI training, and 80.3% had never taken a course on the subject. Nevertheless, 81.1% of students endorsed the inclusion of AI in medical school and residency curricula. These results echo previous findings from national and international studies, demonstrating that while most medical students view AI as beneficial to patient care and medical decision-making, few receive formal instruction on its use ( 50 – 53 ). The contrast between widespread academic usage of general AI tools and the limited integration of health-specific AI applications (e.g., Aidoc, DeepMind Health, PathAI) into professional practice is particularly notable. This suggests a gap between students' personal interests and institutional readiness. While AI is frequently used for general academic support, its application in clinical decision support or diagnostic tools remains underutilized in student training. Addressing this disconnect is critical. To ensure that future physicians are prepared to work effectively alongside AI systems, curriculum developers must prioritize structured education on AI’s clinical, ethical, and technological dimensions. Our findings enhance the original AAIQ by demonstrating strong structural validity and reliability within the Turkish context and connecting scores to a significant outcome—support for curriculum integration. From a governance standpoint, the WHO guidance on large multimodal models emphasizes the importance of safety, transparency, and institutional capacity for the responsible use of AI in health ( 54 ). Meanwhile, the EU Artificial Intelligence Act introduces risk-based obligations that will influence how generative AI is utilized in educational and institutional settings ( 55 ). In this context, a validated and equitable attitudinal measure like the Turkish AAIQ can aid in needs assessment, curriculum design, and impact evaluation for AI literacy and ethics training in medical schools. Recent reports in medical education have shown an increasing interest in AI literacy and the positive effects of coursework on student confidence and readiness ( 56 – 60 ). Our finding that 81.1% of students support the integration of AI into the curriculum reflects their readiness to expand evidence-based modules and faculty development, while also ensuring that equity and patient safety considerations are prioritized during implementation. In conclusion, this study contributes a validated Turkish version of the AAIQ, filling an important methodological gap in the literature. It also underscores the urgency of integrating AI-focused training into medical education to better align with students’ interests, technological realities, and future professional demands. Limitations This study has several limitations that should be considered when interpreting the findings. First, the sample was limited to medical students in Turkey, and most participants were drawn from preclinical years, which may affect the generalizability of the results to clinical trainees or practicing physicians. Second, convenience sampling may introduce selection bias, as students interested in technology or AI might have been more inclined to participate. Finally, self-report measures may be subject to social desirability bias, potentially influencing participants' attitudes toward AI. Future Research Directions Future research should replicate these findings across more diverse samples, including students from different health-related disciplines, clinical-year trainees, and healthcare professionals. Additionally, longitudinal studies could explore how exposure to AI-integrated curricula affects attitudes. Further validation studies could also investigate the predictive validity of the AAIQ by examining its relationship with actual behavior or performance in AI-supported medical tasks. Exploring the impact of structured AI education programs on student attitudes and competencies may also provide valuable insights for curriculum development. Declarations Ethics approval and consent to participate This study was approved by the Non-Interventional Clinical Research Ethics Committee of Sivas Cumhuriyet University, Faculty of Medicine (Approval No: 2025-01/45, Date: 16.01.2025). The study was conducted per the ethical standards of this institutional ethics committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Electronic informed consent was obtained from all participants. Consent for publication Not applicable. This manuscript does not contain any person's data in any form (including individual details, images, or videos). Trial registration: Not applicable This study did not involve a clinical trial and therefore does not require a clinical trial registration number. The research was based on the adaptation and validation of a questionnaire, and no interventions or procedures involving human participants were performed beyond standard survey data collection. Competing interests The authors declare that they have no competing interests. Funding This research received no external funding from public, commercial, or not-for-profit funding agencies. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgements The authors thank the original Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) developers for granting permission to adapt the instrument into Turkish. We also thank the medical students who participated in this study for their valuable contributions. Authors’ information Dr. Gülay Esen – Specialist in Anesthesiology and Reanimation, Private Egepol International Hospital, İzmir, Turkey Dr. Erol Esen – Associate Professor, Department of Counseling and Guidance, Manisa Celal Bayar University, Manisa, Turkey Authors’ contributions G.E. conceptualized the study, performed the data collection and statistical analysis, and drafted the manuscript. E.E. contributed to the study design, instrument adaptation, data interpretation, and manuscript revision. Both authors reviewed and approved the final version of the manuscript. References Goodfellow I, Bengio Y, Courville A. Deep Learning . Cambridge: MIT Press; 2016. He J, Baxter SL, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nature Medicine . 2019;25(1):30–36. Wang F, Preininger A. AI in health: State of the art, challenges, and future directions. Yearb Med Inform . 2019;28:16–26. Alagappan M, Brown JRG, Mori Y, Berzin TM. 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The Journal of psychology. 2022 Jan 26;156(1):68-94. Tang SM. Barriers to artificial intelligence adoption in teaching: the moderating role of personal innovativeness. International Journal of Educational Management. 2025 Aug 15:1-2. Kaya G, Büyükyılmaz F, Çulha Y, Akyürek P. Investigation of the relationship between medical artificial intelligence readiness and individual innovativeness levels in nursing students. Nurse education today. 2025 Aug 1;151:106721. Haggenmüller S, Krieghoff-Henning E, Jutzi T, Trapp N, Kiehl L, Utikal JS, Fabian S, Brinker TJ. Digital natives’ preferences on mobile artificial intelligence apps for skin cancer diagnostics: survey study. JMIR mHealth and uHealth. 2021 Aug 27;9(8):e22909. Chao PJ, Hsu TH, Liu TP, Cheng YH. Knowledge of and competence in artificial intelligence: Perspectives of Vietnamese digital-native students. IEEE Access . 2021;9:75751–75760. Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and generative AI in medical education: Potential impact and opportunity. Acad Med . 2024;99(1):22–27. Zhang JS, Yoon C, Williams DKA, Pinkas A. Exploring ChatGPT use among medical students in the United States. J Med Educ Curric Dev . 2024;11:23821205241264695. Roos J, Kasapovic A, Jansen T, Kaczmarczyk R. Artificial intelligence in medical education: Comparative analysis of ChatGPT, Bing, and medical students in Germany. JMIR Med Educ . 2023;9(1):e46482. Yılmaz Y, Uzelli Yılmaz D, Yıldırım D, Akın Korhan E, Özer Kaya D. Sağlıkta yapay zekâya yönelik görüşler: Sağlık bilimleri fakültesi öğrencileri. Süleyman Demirel Univ J Health Sci . 2021;12(3):297–308. Shinners L, Grace S, Smith S, Stephens A, Aggar C. Exploring healthcare professionals’ perceptions of artificial intelligence: Piloting the Shinners AI Perception Tool. Digit Health . 2022;8:1–8. Eker A, Çalışkan AA, Zorali A, Kaynak B, Derin ME. Medical students’ knowledge and attitudes about artificial intelligence: A cross-sectional survey. World Med Educ . 2023;22(67):41–51. Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, et al. Medical students' attitude towards artificial intelligence: A multicentre survey. Eur Radiol . 2019;29(4):1640–1646. Öcal EE, Atay E, Önsüz MF, Altın F, Çokyiğit FK, Kılınç S, et al. Tıp fakültesi öğrencilerinin tıpta yapay zekâ ile ilgili düşünceleri. Türk Tıp Öğrencileri Araştırma Dergisi . 2020;2(1):9–16. World Health Organization. Ethics and governance of artificial intelligence for health: Guidance on large multimodal models [Internet]. Geneva: World Health Organization; 2025 [cited 2025 Sep 17]. Available from: https://www.who.int/publications/i/item/9789240084759 European Union. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Off J Eur Union [Internet]. 2024 Jul 12;L 2024/1689. Available from: https://data.europa.eu/eli/reg/2024/1689/oj Boscardin CK, Gin B, Black Golde P, Hauer KE. ChatGPT and generative artificial intelligence for medical education: Potential impact and opportunity. Acad Med. 2024;99(1):22-7. doi:10.1097/ACM.0000000000005439 Sridharan K, Sequeira RP. Artificial intelligence and medical education: Application in classroom instruction and student assessment using a pharmacology & therapeutics case study. BMC Med Educ. 2024;24:431. doi:10.1186/s12909-024-05365-7 Jackson P, Ponath Sukumaran G, Babu C, Tony MC, Jack DS, Reshma VR, Davis D, Kurian N, John A. Artificial intelligence in medical education—Perception among medical students. BMC Med Educ. 2024;24:804. doi:10.1186/s12909-024-05760-0 Sami A, Tanveer F, Sajwani K, Kiran N, Javed MA, Uzun Ozsahin D, Muhammad K, Waheed Y. Medical students’ attitudes toward AI in education: Perception, effectiveness, and its credibility. BMC Med Educ. 2025;25:82. doi:10.1186/s12909-025-06704-y Zheng L, Xiao Y. Refining AI perspectives: Assessing the impact of AI curricular on medical students’ attitudes towards artificial intelligence. BMC Med Educ. 2025;25:1115. doi:10.1186/s12909-025-07669-8 Additional Declarations No competing interests reported. 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-8977743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600624620,"identity":"15859237-16a4-4683-b40e-8f0c5b1ae0a9","order_by":0,"name":"Gülay ESEN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDCCA0D8AMJkBNE8fERpSYAwmQ1AWthI0cImASYJ6eC7ffyaRELNYTn59t5jlV9z7GTYGJgfPrqBR4vkuZwyiYRjh40NzpxLuy27LRnoMDZj4xw8WgzO8KRJJLAdTtwgkWN2W3IbM1ALD5s0YS3/DtfPn//GrFhyWz0xWtiPSSS2HU5guMFjxvhx22HCWiTP8DBbJPalG244k2MszbjtOA8bMwG/8J1hf3jjwzdrefn2M4Yff26rtudnb374GJ8WYNyBIrAZzGTmAZN4lYMA+wMgUQdmMv4gqHoUjIJRMApGIgAAOqdIm+oCWiYAAAAASUVORK5CYII=","orcid":"","institution":"Özel Egepol Hastanei","correspondingAuthor":true,"prefix":"","firstName":"Gülay","middleName":"","lastName":"ESEN","suffix":""},{"id":600624621,"identity":"060a4e2f-0b55-4170-bc7d-e127776985f7","order_by":1,"name":"Erol ESEN","email":"","orcid":"","institution":"Manisa Celal Bayar University","correspondingAuthor":false,"prefix":"","firstName":"Erol","middleName":"","lastName":"ESEN","suffix":""}],"badges":[],"createdAt":"2026-02-26 12:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8977743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8977743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104779420,"identity":"f47c3a0e-11fb-40d3-8c88-b84fd6877ebc","added_by":"auto","created_at":"2026-03-17 07:40:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":864375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8977743/v1/eeaf0d60-ff44-4b58-a4e8-142d46b8218a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) for Medical Students: Psychometric Properties of the Turkish Version","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eArtificial intelligence (AI) encompasses a broad array of technologies designed to simulate human intelligence and perform tasks traditionally requiring human cognitive functions, such as reasoning, learning, and problem-solving (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). At its core, AI involves the creation of computer algorithms capable of replicating behaviors associated with intelligent human action (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This technological domain includes subfields such as machine learning, deep learning, and natural language processing, each contributing to AI systems' increasing sophistication and versatility (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAI technologies are rapidly integrated into diverse sectors, including law, finance, computer science, and industrial manufacturing (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Among these, the field of medicine has emerged as one of the most dynamically evolving areas of AI application, supported by the exponential growth of healthcare data and significant investments by the technology industry (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Today, AI tools are used across numerous medical specialties to assist clinicians in radiology, pathology, and precision oncology, contributing to improved diagnostic accuracy and personalized treatment planning (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHealth systems are increasingly adopting AI to enhance service efficiency and clinical decision-making. For example, machine learning\u0026ndash;based models are now used to predict the risk of patient admissions in emergency departments, thereby reducing unnecessary hospitalizations (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In gastroenterology, AI-integrated endoscopic systems aid in detecting and classifying pathological lesions (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while in oncology, AI contributes to cancer diagnosis and grading through automated image analysis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Beyond clinical care, AI has also become a transformative tool in medical education. AI-driven learning platforms are being developed to expose students to clinical scenarios and support experiential learning through simulated diagnostic practice (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis accelerating integration of AI into healthcare practice places new demands on medical education systems. Medical faculties are increasingly expected to prepare future physicians to understand and effectively collaborate with AI technologies. To this end, there is a pressing need to assess medical students\u0026rsquo; readiness and attitudes toward AI. Understanding their perceptions is essential to informing curriculum development and ensuring that AI adoption in healthcare is supported by a workforce with appropriate knowledge and acceptance.\u003c/p\u003e \u003cp\u003eHowever, empirical studies examining medical students' attitudes toward AI in Turkey remain limited. One significant barrier is the lack of validated and culturally adapted measurement tools. To address this gap, the present study aimed to adapt the Attitudes Towards AI Questionnaire for Health Professions\u0026rsquo; Students (AAIQ)\u0026mdash;initially developed by Al-Qerem et al. (2023) into Turkish and evaluate its psychometric properties among medical students (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). By doing so, the study seeks to contribute to the growing body of literature on AI in medical education and to facilitate future research and curriculum design in the Turkish context.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study used convenience sampling to recruit participants in various medical faculties across Turkey. Convenience sampling is a non-probability technique that allows researchers to easily access individuals who are members of the target population and are both eligible and willing to participate in the study (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A total of 238 medical students participated, comprising 56.7% female (n\u0026thinsp;=\u0026thinsp;135) and 43.3% male (n\u0026thinsp;=\u0026thinsp;103) students from both state (85.3%) and foundation universities (14.7%).\u003c/p\u003e \u003cp\u003eParticipants\u0026rsquo; ages ranged from 17 to 32 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.32, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.58). Socioeconomic status was assessed using a self-reported 10-point Likert-type scale (1\u0026thinsp;=\u0026thinsp;lowest, 10\u0026thinsp;=\u0026thinsp;highest), yielding a mean score of 5.55 (SD\u0026thinsp;=\u0026thinsp;2.03). Detailed demographic characteristics of the sample are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eSociodemographic Characteristics of Participants\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eState University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.3\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\u003eFoundation university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st year\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\u003e23.9\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\u003e2nd year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.5\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\u003e3rd year\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\u003e20.2\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\u003e4th year\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\u003e12.6\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\u003e5th year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.8\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\u003e6th year\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\u003e13.0\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\n\u003ch3\u003eData Collection Instruments\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAttitudes Towards Artificial Intelligence Questionnaire (AAIQ)\u003c/h2\u003e \u003cp\u003eThe AAIQ, developed by Al-Qerem et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), is a unidimensional instrument of 10 items designed to assess health profession students' attitudes toward AI. Items are rated on a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). Total scores range from 10 to 50, with higher scores indicating more favorable attitudes toward AI use in healthcare. In the original development study, the scale demonstrated good internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.87) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In the current study, the Turkish version of the AAIQ yielded a Cronbach\u0026rsquo;s alpha coefficient of .88, indicating high internal consistency.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDigital Native Assessment Scale (DNAS)\u003c/h3\u003e\n\u003cp\u003eOriginally developed by Teo (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), the DNAS assesses university students\u0026rsquo; perceptions of themselves as digital natives. The Turkish adaptation was conducted by Teo et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The scale comprises 21 items across four subscales: growing up with technology, comfort with multitasking, preference for visual communication, and preference for instant gratification and rewards. A total score is computed by summing the subscale scores. Each item is rated on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly disagree, 7\u0026thinsp;=\u0026thinsp;Strongly agree), with total scores ranging from 21 to 147.\u003c/p\u003e \u003cp\u003eIn the Turkish validation study, construct validity indicators were acceptable (χ\u0026sup2; = 673.539; χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.893; TLI = .90; CFI = .91; RMSEA = .07; SRMR = .068) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Composite reliability (CR) and average variance extracted (AVE) values were reported as .83 and .50, respectively (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The DNAS demonstrated good reliability in the present study, with Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.84.\u003c/p\u003e\n\u003ch3\u003eIndividual Innovativeness Scale (IIS)\u003c/h3\u003e\n\u003cp\u003eThe IIS, developed by Hurt et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), measures an individual\u0026rsquo;s general tendency toward innovation. The scale consists of 20 items, of which 12 are positively worded and eight are negatively worded. Items are rated on a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). The Turkish adaptation was performed by Kılı\u0026ccedil;er and Odabaşı (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and demonstrated acceptable internal consistency (α\u0026thinsp;=\u0026thinsp;.82). The scale\u0026rsquo;s reliability was reaffirmed in the current study, with a Cronbach\u0026rsquo;s alpha of .88.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003e This study was approved by the Non-Interventional Clinical Research Ethics Committee of Sivas Cumhuriyet University, Faculty of Medicine (Approval No: 2025-01/45, Date: 16.01.2025). Before adapting the target measurement tool into Turkish, formal permission was obtained from the original developer of the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The adaptation process employed a thorough translation and back-translation methodology to ensure that the original and Turkish versions maintained both semantic and linguistic equivalence (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Two independent researchers, fluent in English, translated the AAIQ into Turkish. The research team then reviewed and combined these forward translations into a single version. An academic expert in English Language and Literature subsequently back-translated the synthesized Turkish version into English. This back-translated version was submitted to the original scale developer, who confirmed its semantic accuracy and cultural appropriateness.\u003c/p\u003e \u003cp\u003eA pilot study was conducted with 10 medical students to evaluate the clarity and comprehensibility of the adapted survey items. Due to the positive feedback from participants, no major revisions were needed, allowing the primary data collection phase to proceed as planned. The finalized survey instruments were distributed online using Google Forms between January and February 2025. Medical faculty members helped share the survey link with students. Participants received detailed information about the purpose of the study, and electronic informed consent was obtained prior to participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eBefore conducting the primary analyses, the dataset was thoroughly examined for data entry errors, missing values, and multivariate outliers. The outliers were screened using standardized z-scores and Mahalanobis distance, and no significant outliers were identified. The data distribution was assessed using skewness and kurtosis statistics, with all values falling within the acceptable range of -1.0 to +\u0026thinsp;1.0, indicating that the data approximated a normal distribution (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical analyses included descriptive statistics, construct validity, convergent validity, and reliability testing. Construct validity of the Turkish version of the AAIQ was assessed using Confirmatory Factor Analysis (CFA). Model fit was evaluated based on multiple indices as recommended by Kline (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and Schreiber et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), including the Chi-square statistic (χ\u0026sup2;), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR).\u003c/p\u003e \u003cp\u003eTo evaluate convergent validity, Pearson correlation coefficients were calculated between the AAIQ scores and those of the Digital Native Assessment Scale (DNAS) and the Individual Innovativeness Scale (IIS). Internal consistency reliability was examined using Cronbach\u0026rsquo;s alpha, along with Composite Reliability (CR) and Average Variance Extracted (AVE) values, to further support the instrument's psychometric soundness.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eThis section presents findings regarding the participants\u0026rsquo; educational exposure to artificial intelligence (AI), their attitudes toward AI integration in medical education, and their usage patterns of AI applications across academic and professional contexts.\u003c/p\u003e \u003cp\u003eA majority of the participants (81.1%) expressed support for the incorporation of AI applications into medical and residency training curricula. However, only 21.0% reported receiving formal training on AI, whether online or offline. Similarly, a substantial portion (80.3%) stated that they had not enrolled in any AI-related course as part of their medical education. While 46.6% indicated utilizing AI applications during medical coursework, the remaining participants reported no use. Detailed statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eParticipants\u0026rsquo; educational experiences on AI\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave you previously undergone any training on AI, whether through online or offline channels?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave you ever enrolled in a course on AI as part of your medical education?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave you utilized artificial intelligence applications in any of your medical courses?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDo you agree that artificial intelligence should be included in medical school and residency training curricula?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9\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\u003eFurthermore, the frequency of AI use among participants across different purposes was explored. The data revealed that AI tools were most commonly used for academic-related tasks, such as preparing for exams, conducting research, and completing homework. The least common usage was for grammar and spelling verification, though this still showed moderate engagement. The frequency distribution of usage across various purposes is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eFrequency of participants' use of AI for various purposes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow often do you utilize AI to prepare for your exams?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow often do you utilize AI in your homework?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow often do you utilize AI to conduct your research?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow often do you utilize AI for personal development and to enhance skills in various domains?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow often do you utilize AI to verify the quality of writing in terms of spelling and grammar?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23.5\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\u003eParticipants were also asked to identify the AI applications they were familiar with, had used in daily life, had applied professionally in healthcare, or had utilized within their medical education.\u003c/p\u003e \u003cp\u003eThe most widely recognized AI tools among participants included ChatGPT (f\u0026thinsp;=\u0026thinsp;123), Google Assistant (f\u0026thinsp;=\u0026thinsp;72), and Siri (f\u0026thinsp;=\u0026thinsp;49). In terms of actual usage in daily life, ChatGPT was again the most frequently used application (f\u0026thinsp;=\u0026thinsp;98), followed by Google Assistant (f\u0026thinsp;=\u0026thinsp;47) and Siri (f\u0026thinsp;=\u0026thinsp;31). More specialized health-related AI tools with high awareness included IBM Watson Health (f\u0026thinsp;=\u0026thinsp;52), DeepMind Health (f\u0026thinsp;=\u0026thinsp;27), and Aidoc (f\u0026thinsp;=\u0026thinsp;16). However, actual use of such tools in a professional or clinical education context remained relatively low: IBM Watson Health (f\u0026thinsp;=\u0026thinsp;18), ChatGPT (f\u0026thinsp;=\u0026thinsp;10), Aidoc (f\u0026thinsp;=\u0026thinsp;7), and others, such as Google Health, DeepMind Health, and Zebra Medical Vision, showed limited engagement.\u003c/p\u003e \u003cp\u003eRegarding the use of AI tools specifically within medical courses, the most commonly reported tools were ChatGPT (f\u0026thinsp;=\u0026thinsp;38), IBM Watson Health (f\u0026thinsp;=\u0026thinsp;5), DeepMind Health (f\u0026thinsp;=\u0026thinsp;4), Aidoc (f\u0026thinsp;=\u0026thinsp;4), and others, including PathAI, Medscape AI Search, and XERTE (each with lower frequencies).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruct Validity of the AAIQ Turkish Form\u003c/h2\u003e \u003cp\u003eTo evaluate the construct validity of the Turkish version of the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ), Confirmatory Factor Analysis (CFA) was conducted based on the scale\u0026rsquo;s original unidimensional structure. Given the sensitivity of χ\u0026sup2; to sample size, the overall fit was evaluated using multiple goodness-of-fit indices. CFA supported the AAIQ's original unidimensional structure, which demonstrated good model fit indices: χ\u0026sup2;\u003csub\u003e(35)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;42.51, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.21, SRMR = .06, RMSEA = .07, TLI = .99, and CFI = .99. These values fulfill the conventional criteria for a good model fit, as outlined in the literature (\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and confirm the unidimensionality of the scale in the context of Turkish medical students. The goodness-of-fit statistics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGoodness-of-Fit Indices for the AAIQ Turkish Version\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off for Good Fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;\u0026le;\u0026thinsp;2 (Tabachnik and Fidell, 2007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRMR \u0026le; .08 (Hu \u0026amp; Bentler, 1999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSEA \u0026le; .07 (Steiger, 2007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLI \u0026ge; .95 (Schreiber et al. 2006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFI \u0026ge;\u0026thinsp;.95 (Browne \u0026amp; Cudeck, 1993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe standardized factor loadings for the 10 AAIQ items ranged from .43 to .96, all exceeding the acceptable threshold of .40 suggested by Pituch and Stevens (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These values support the factorial integrity of the Turkish version. Detailed loadings and confidence intervals are provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor loadings for the AAIQ Turkish form items\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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized factor loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6th Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7th Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8th Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9th Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th Item\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.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 \u003cem\u003eNote. 95% CIs are reported for\u003c/em\u003e \u003cb\u003eunstandardized\u003c/b\u003e \u003cem\u003eloadings; standardized loadings are presented without CIs.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConvergent Validity of the AAIQ Turkish Form\u003c/h2\u003e \u003cp\u003eTo assess convergent validity, Pearson correlation coefficients were calculated between the AAIQ scores and scores obtained from two theoretically related constructs: the Individual Innovativeness Scale (IIS) and the Digital Native Assessment Scale (DNAS). As hypothesized, significant positive correlations were observed: AAIQ\u0026ndash;IIS (\u003cem\u003er\u003c/em\u003e = .24, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) and AAIQ\u0026ndash;DNAS (\u003cem\u003er\u003c/em\u003e = .22, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01). These findings provide empirical support for the convergent validity of the Turkish adaptation of the AAIQ in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations Among Attitudes Towards AI, Innovativeness, and Digital Nativeness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitudes towards AI (AAIQ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInnovativeness levels (IIS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.24**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital nativeness levels (DNAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e** Correlation is significant at the 0.01 level\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Correlation is significant at the 0.05 level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReliability of the AAIQ Turkish Form\u003c/h2\u003e \u003cp\u003eTo evaluate the internal consistency reliability, Cronbach\u0026rsquo;s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE) values were computed. The Cronbach\u0026rsquo;s alpha coefficient for the 10-item scale was .88, indicating strong internal consistency. Furthermore, the CR was calculated as .95, and the AVE as .67, exceeding the commonly accepted thresholds of .70 and .50, respectively (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eItem-total correlations ranged from .48 to .81, further supporting the scale's reliability and internal coherence. These results demonstrate that the Turkish version of the AAIQ is a psychometrically robust instrument for assessing medical students\u0026rsquo; attitudes toward artificial intelligence.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWith the rapid advancement of digital technologies, artificial intelligence (AI) has emerged as a transformative force across numerous domains, particularly in healthcare. AI systems are now instrumental in enhancing diagnostic accuracy, streamlining clinical workflows, and expanding the reach of precision medicine(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In parallel, the need to prepare healthcare professionals\u0026mdash;especially future physicians\u0026mdash;for AI-integrated clinical environments has become increasingly evident. Despite these developments, valid and reliable instruments for assessing medical students\u0026rsquo; attitudes toward AI remain scarce in Turkey (\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36 CR37\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This study addressed this gap by adapting the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) into Turkish and evaluating its psychometric properties among medical students.\u003c/p\u003e \u003cp\u003eThe CFA results confirmed the unidimensional factor structure of the original scale, with all model fit indices indicating a good fit. Factor loadings ranged from .43 to .96, mirroring those reported in the original development study (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, the Turkish version demonstrated excellent internal consistency (α\u0026thinsp;=\u0026thinsp;.88), composite reliability (CR = .95), and average variance extracted (AVE = .67), aligning with recommended psychometric thresholds (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These findings validate the Turkish form of the AAIQ as a reliable and structurally robust tool for measuring medical students' attitudes toward AI.\u003c/p\u003e \u003cp\u003eThe findings related to convergent validity reveal a significant positive correlation between participants' levels of innovativeness and their attitudes toward AI. This aligns with previous research indicating that as individuals' levels of innovativeness increase, they are more likely to use AI and develop more positive attitudes toward it (\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Additionally, a positive correlation was observed between participants' digital nativeness and their attitudes toward AI. Our findings align with existing literature indicating that digital natives are more likely to adopt new technologies, including AI-based technologies. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The relationships among these variables provide evidence for the convergent validity of the Turkish version of the AAIQ.\u003c/p\u003e \u003cp\u003eBeyond psychometrics, our findings indicate that Turkish medical students are highly prepared to engage with AI-enabled healthcare. This suggests the need for incorporating AI literacy into the curriculum, focusing on aspects such as data quality, bias, explainability, and human oversight. Additionally, clear institutional policies, faculty development, and monitoring of inequities are essential for governance. By embedding these skills into both undergraduate and residency training, we can better align student enthusiasm with the safe, effective, and equitable use of AI in clinical and public health settings.\u003c/p\u003e \u003cp\u003eBeyond psychometric validation, the study provides valuable insights into the current state of AI engagement among medical students in Turkey. Most students reported using AI tools such as ChatGPT, Google Assistant, and IBM Watson Health for academic tasks, including exam preparation, research, and homework. These findings are consistent with international studies, highlighting widespread adoption of generative AI tools in medical education settings (\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). ChatGPT stood out as the most frequently recognized and utilized among these tools, likely due to its accessibility, popularity, and capacity to generate quick, coherent responses.\u003c/p\u003e \u003cp\u003eDespite this high level of informal usage, formal exposure to AI in medical education remains limited. Only 21.0% of students had received any form of AI training, and 80.3% had never taken a course on the subject. Nevertheless, 81.1% of students endorsed the inclusion of AI in medical school and residency curricula. These results echo previous findings from national and international studies, demonstrating that while most medical students view AI as beneficial to patient care and medical decision-making, few receive formal instruction on its use (\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe contrast between widespread academic usage of general AI tools and the limited integration of health-specific AI applications (e.g., Aidoc, DeepMind Health, PathAI) into professional practice is particularly notable. This suggests a gap between students' personal interests and institutional readiness. While AI is frequently used for general academic support, its application in clinical decision support or diagnostic tools remains underutilized in student training. Addressing this disconnect is critical. To ensure that future physicians are prepared to work effectively alongside AI systems, curriculum developers must prioritize structured education on AI\u0026rsquo;s clinical, ethical, and technological dimensions.\u003c/p\u003e \u003cp\u003eOur findings enhance the original AAIQ by demonstrating strong structural validity and reliability within the Turkish context and connecting scores to a significant outcome\u0026mdash;support for curriculum integration. From a governance standpoint, the WHO guidance on large multimodal models emphasizes the importance of safety, transparency, and institutional capacity for the responsible use of AI in health (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Meanwhile, the EU Artificial Intelligence Act introduces risk-based obligations that will influence how generative AI is utilized in educational and institutional settings (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In this context, a validated and equitable attitudinal measure like the Turkish AAIQ can aid in needs assessment, curriculum design, and impact evaluation for AI literacy and ethics training in medical schools. Recent reports in medical education have shown an increasing interest in AI literacy and the positive effects of coursework on student confidence and readiness (\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Our finding that 81.1% of students support the integration of AI into the curriculum reflects their readiness to expand evidence-based modules and faculty development, while also ensuring that equity and patient safety considerations are prioritized during implementation.\u003c/p\u003e \u003cp\u003eIn conclusion, this study contributes a validated Turkish version of the AAIQ, filling an important methodological gap in the literature. It also underscores the urgency of integrating AI-focused training into medical education to better align with students\u0026rsquo; interests, technological realities, and future professional demands.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, the sample was limited to medical students in Turkey, and most participants were drawn from preclinical years, which may affect the generalizability of the results to clinical trainees or practicing physicians. Second, convenience sampling may introduce selection bias, as students interested in technology or AI might have been more inclined to participate. Finally, self-report measures may be subject to social desirability bias, potentially influencing participants' attitudes toward AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research Directions\u003c/h2\u003e \u003cp\u003eFuture research should replicate these findings across more diverse samples, including students from different health-related disciplines, clinical-year trainees, and healthcare professionals. Additionally, longitudinal studies could explore how exposure to AI-integrated curricula affects attitudes. Further validation studies could also investigate the predictive validity of the AAIQ by examining its relationship with actual behavior or performance in AI-supported medical tasks. Exploring the impact of structured AI education programs on student attitudes and competencies may also provide valuable insights for curriculum development.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Non-Interventional Clinical Research Ethics Committee of Sivas Cumhuriyet University, Faculty of Medicine (Approval No: 2025-01/45, Date: 16.01.2025). The study was conducted per the ethical standards of this institutional ethics committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Electronic informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any person's data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003eThis study did not involve a clinical trial and therefore does not require a clinical trial registration number. The research was based on the adaptation and validation of a questionnaire, and no interventions or procedures involving human participants were performed beyond standard survey data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the original Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) developers for granting permission to adapt the instrument into Turkish. We also thank the medical students who participated in this study for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr. Gülay Esen\u003c/strong\u003e – Specialist in Anesthesiology and Reanimation, Private Egepol International Hospital, İzmir, Turkey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr. Erol Esen\u003c/strong\u003e – Associate Professor, Department of Counseling and Guidance, Manisa Celal Bayar University, Manisa, Turkey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG.E.\u003c/strong\u003e conceptualized the study, performed the data collection and statistical analysis, and drafted the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE.E.\u003c/strong\u003e contributed to the study design, instrument adaptation, data interpretation, and manuscript revision.\u003c/p\u003e\n\u003cp\u003eBoth authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGoodfellow I, Bengio Y, Courville A. \u003cem\u003eDeep Learning\u003c/em\u003e. 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BMC Med Educ. 2025;25:1115. doi:10.1186/s12909-025-07669-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, scale adaptation, medical education, medical students","lastPublishedDoi":"10.21203/rs.3.rs-8977743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8977743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe rapid integration of artificial intelligence (AI) into healthcare practice has resulted in new demands on medical education systems. This study aimed to adapt the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ), originally developed for health profession students, into Turkish and examine its psychometric properties among Turkish medical students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e Through convenience sampling, 238 medical students (56.7% female; \u003cem\u003eM\u003c/em\u003e = 22.32, \u003cem\u003eSD\u003c/em\u003e= 2.58) from various state and foundation universities participated. The scale adaptation process followed a standardized translation and back-translation protocol. Confirmatory factor analysis (CFA) was conducted to examine construct validity, while convergent validity was tested via correlations with digital nativeness and individual innovativeness measures. Internal consistency was evaluated using Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCFA supported the AAIQ's original unidimensional structure, which demonstrated good model fit indices: χ²(35) = 42.51, χ²/df = 1.21, SRMR = .06, RMSEA = .07, TLI = .99, and CFI = .99. Reliability analyses indicated high internal consistency, with α = .88, CR = .95, and AVE = .67. Notably, 81.1% of participants expressed support for the integration of AI into medical education, although only 21.0% had received formal training related to AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe Turkish version of the AAIQ shows strong validity and reliability, making it a robust tool for evaluating medical students' attitudes toward artificial intelligence.\u003c/p\u003e","manuscriptTitle":"The Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) for Medical Students: Psychometric Properties of the Turkish Version","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 11:02:05","doi":"10.21203/rs.3.rs-8977743/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":"91226b59-1f0f-4411-b698-bd0c3c36125a","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T13:41:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 11:02:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8977743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8977743","identity":"rs-8977743","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}