Expanding the Scope of Ai Readiness: Validation of the Mairs Scale Among Dental, Nursing, and Midwifery Students

Expanding the Scope of Ai Readiness: Validation of the Mairs Scale Among Dental, Nursing, and Midwifery Students | 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 Expanding the Scope of Ai Readiness: Validation of the Mairs Scale Among Dental, Nursing, and Midwifery Students Ahmet Düha KOÇ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6414139/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 Artificial intelligence (AI) is rapidly transforming healthcare. However, validated tools to assess AI readiness in non-medical health disciplines remain scarce. This study aimed to adapt and validate the MAIRS scale among dental, nursing, and midwifery students. Methods A cross-sectional study involving 376 students was conducted. Exploratory and confirmatory factor analysis, along with structural equation modeling, were used to validate the adapted scale. Internal consistency and predictive validity were evaluated. Results The adapted MAIRS scale retained a four-factor structure with excellent reliability (Cronbach’s α = 0.89–0.92). AI Usage Awareness was the strongest predictor of AI Knowledge Level (β = 1.05, p < 0.001). Students demonstrated high awareness and ethical concern but limited technical understanding. Conclusion The MAIRS scale is valid and reliable for assessing AI readiness in non-medical health education. Findings highlight the urgent need to integrate AI education into undergraduate health curricula. Artificial Intelligence Dental Education Nursing Education AI Readiness Health Sciences Figures Figure 1 INTRODUCTION Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, enabling real-time decision support, and improving patient outcomes across a wide range of clinical domains [ 1 , 2 , 3 ]. The integration of AI into healthcare is not limited to physicians; professionals in dentistry, nursing, and allied health are also encountering AI tools in diagnostic imaging, patient monitoring, and clinical decision-making [ 4 , 5 ]. However, the incorporation of AI education into healthcare curricula remains uneven. While some medical schools have initiated AI literacy programs, similar efforts in dental and nursing education are scarce [ 6 , 7 ]. This disparity poses a risk of digital illiteracy among future healthcare workers, potentially compromising patient safety and system efficiency. To address this challenge, health education must ensure that students possess not only basic AI knowledge, but also a critical understanding of ethical, legal, and practical aspects of AI deployment [ 8 , 9 ]. In response, the concept of AI readiness has emerged–defined as a multidimensional construct involving awareness, knowledge, vision, and ethics–enhanced environments [ 10 ]. Despite the growing relevance of AI readiness, validated measurement tools applicable across healthcare disciplines are scarce. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) is a notable instrument, developed specifically for medical students to assess awareness, knowledge, vision, and ethical dimensions of AI [ 10 ]. However, its applicability to non-medical students has not been formally validated. This study aims to fill that gap by adapting and validating the MAIRS scale among students in dentistry, nursing, and midwifery. Establishing a reliable and generalizable tool for assessing AI readiness in diverse healthcare disciplines is essential for guiding curriculum development and aligning education with the demands of an AI-integrated health system. METHODS Study Design This study employed a cross-sectional, instrument validation design to assess the psychometric properties of the Medical Artificial Intelligence Readiness Scale (MAIRS) among undergraduate students in dental, nursing, and midwifery programs. The methodology followed the STROBE guidelines for observational studies to ensure scientific rigor and transparency [ 11 ]. Participants and Sampling A total of 376 undergraduate students from a Turkish public university were recruited using stratified random sampling to ensure balanced representation across disciplines. Inclusion criteria included active enrollment in health-related undergraduate programs and voluntary participation. Exclusion criteria were incomplete responses or lack of informed consent. The sample size was determined based on accepted psychometric validation principles, with recommendations suggesting at least 10 respondents per item for structural equation modeling [ 12 ]. The final sample size exceeded this benchmark. Instrument The original MAIRS scale, developed by Karaca et al. [ 10 ], includes 22 items across four subscales: AI Usage Awareness, AI Knowledge Level, AI Vision, and AI Ethics. The scale was translated and adapted linguistically and contextually for non-medical health sciences students. Expert validation was conducted by six academic professionals in artificial intelligence and health education, and content validity was evaluated according to Lynn’s method [ 13 ]. Each item used a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). Data Collection Data were collected via an anonymous online survey platform between February and May 2024. Informed consent was obtained electronically prior to participation. The study protocol received ethical approval from the Karabük University Ethics Committee (Approval No: 2023/1215), in accordance with the Declaration of Helsinki. Statistical Analysis All statistical procedures were conducted using RStudio (version 4.4.3). Exploratory Factor Analysis (EFA) EFA was conducted to uncover the underlying factor structure using principal component analysis with varimax rotation. Sampling adequacy was confirmed using the Kaiser-Meyer-Olkin (KMO) measure, and Bartlett’s test of sphericity assessed the appropriateness of factor analysis [ 14 ]. Confirmatory Factor Analysis (CFA) CFA was performed using the lavaan package in R. Model fit was evaluated using the following criteria [ 15 ]: • CFI ≥ 0.90 • TLI ≥ 0.90 • RMSEA ≤ 0.08 • SRMR ≤ 0.08 Internal Consistency and Reliability Reliability was assessed using Cronbach’s alpha and Composite Reliability (CR). An alpha value above 0.80 and CR above 0.70 were considered acceptable [ 16 ]. Structural Equation Modeling (SEM) SEM was used to examine hypothesized relationships among latent variables. The significance of standardized path coefficients was reported, and model fit indices mirrored those used in CFA. RESULTS Descriptive Statistics A total of 376 participants completed the survey, comprising students from dental (32%), nursing (38%), and midwifery (30%) programs. The sample was predominantly female (78.4%), reflecting the demographic structure of the participating disciplines. The average age was 21.3 years (SD = 1.6). The distribution of mean scores and standard deviations across the four AI readiness subscales is illustrated in Fig. 1 . While students demonstrated moderate levels of AI Usage Awareness and Vision, their technical knowledge lagged behind, underscoring a critical readiness gap. The highest mean score was observed in the AI Vision subscale (M = 3.42, SD = 0.68), followed by AI Usage Awareness (M = 3.21, SD = 0.76), and AI Ethics (M = 3.18, SD = 0.73). The lowest mean was recorded in AI Knowledge Level (M = 2.95, SD = 0.81), suggesting a general lack of technical understanding despite high conceptual and ethical awareness. Internal Consistency Reliability analysis demonstrated high internal consistency across all four subscales (see Table 1 ). These values exceed the conventional threshold of 0.80 for psychological instruments, supporting the reliability of the adapted scale [ 12 ]. Table 1 Internal consistency reliability of MAIRS subscales Subscale Cronbach’s Alpha AI Usage Awareness 0.9 AI Knowledge Level 0.92 AI Vision 0.89 AI Ethics 0.91 Exploratory Factor Analysis (EFA) The Kaiser-Meyer-Olkin (KMO) measure was 0.94, indicating excellent sampling adequacy. Bartlett’s Test of Sphericity was statistically significant (χ²(231) = 3745.32, p < 0.001), confirming the appropriateness of factor analysis. EFA using principal component analysis with varimax rotation revealed a four-factor structure aligned with the original scale, explaining 58.6% of total variance: Factor 1: AI Usage Awareness (7 items) Factor 2: AI Knowledge Level (7 items) Factor 3: AI Vision (3 items) Factor 4: AI Ethics (5 items) All items demonstrated strong factor loadings (> 0.70), and no cross-loading was observed. Confirmatory Factor Analysis (CFA) CFA validated the four-factor model using maximum likelihood estimation. The model demonstrated acceptable fit across multiple indices (see Table 2 ). All standardized factor loadings were significant ( p < 0.001) and exceeded 0.60, confirming convergent validity [ 15 ]. No model modifications were necessary, and residuals remained within acceptable limits. Table 2 Fit indices for the confirmatory factor analysis (CFA) model Fit Index Value CFI 0.923 TLI 0.910 RMSEA 0.079 SRMR 0.065 Composite Reliability and Average Variance Extracted Composite Reliability (CR) values ranged from 0.84 to 0.93 Average Variance Extracted (AVE) values ranged from 0.62 to 0.74 These metrics support strong construct validity, consistent with the recommendations of Fornell and Larcker [ 16 ]. Structural Equation Modeling (SEM) SEM was employed to test the predictive pathways among latent variables, examining the effects of AI Usage Awareness, AI Vision, and AI Ethics on AI Knowledge Level. The model showed good overall fit across multiple indices (see Table 3 ). Path coefficients indicated strong and statistically significant relationships (see Table 4 ). Table 3 Fit indices for the structural equation model (SEM) Fit Index Value CFI 0.918 TLI 0.902 RMSEA 0.076 SRMR 0.062 Path coefficients were as follows: Table 4 Standardized path coefficients predicting AI Knowledge Level Path Standardized β p-value Usage Awareness → Knowledge 1.046 < 0.001 Vision → Knowledge 0.87 0.002 Ethics → Knowledge 0.62 0.015 These results suggest that students with greater AI awareness and future-oriented vision are more likely to exhibit higher levels of AI knowledge, reinforcing the experiential and motivational dimensions of digital readiness [ 17 , 18 ]. DISCUSSION This study validated the Medical Artificial Intelligence Readiness Scale (MAIRS) among undergraduate students in dentistry, nursing, and midwifery—populations traditionally underrepresented in AI education research. The findings demonstrate that the scale retains strong psychometric properties in non-medical cohorts, suggesting that the four domains of AI readiness—Usage Awareness, Knowledge Level, Vision, and Ethics—are robust constructs that transcend disciplinary boundaries. Alignment with Existing Literature Our results echo previous findings that AI awareness is relatively high, while technical knowledge remains limited among healthcare students [ 19 , 20 ]. This discrepancy reflects a global trend in which AI is perceived as relevant but remains poorly understood on a functional level, particularly among nursing and dental students who lack structured exposure to digital Technologies [ 21 , 22 ]. Moreover, the strong predictive role of AI Usage Awareness on Knowledge Level (β = 1.046, p < 0.001) supports experiential learning theories, which emphasize that familiarity with tools increases conceptual understanding and motivation to learn [ 17 ]. This suggests that mere curricular inclusion of AI content may be insufficient unless paired with active, hands-on learning environments. Implications for AI Curriculum Design The scale’s validation for non-medical health students highlights a critical gap in curriculum design. Current health education programs often fail to prepare students for interdisciplinary, technology-rich clinical environments [ 1 , 7 ]. Given the increasing use of AI in diagnostics, triage, and decision support, a digitally literate workforce must include nurses, midwives, and dental professionals—not just physicians [ 23 ]. AI Vision, defined as the ability to conceptualize future roles of AI in healthcare, also significantly predicted knowledge. This aligns with educational psychology literature suggesting that future-oriented cognition fosters deeper engagement and willingness to acquire technical skills [ 8 , 24 ]. Interestingly, AI Ethics, while statistically significant, had the weakest predictive value. This may indicate that ethical considerations are often perceived as abstract or secondary to practical knowledge. Given the high-stakes nature of AI in patient care—where algorithmic bias, privacy, and explainability are critical—this disconnection poses a risk. Ethics instruction should be made more concrete and embedded in real-world case simulations. Methodological Contributions From a psychometric perspective, this study contributes to the literature by extending the use of MAIRS beyond medical students, offering a validated and reliable tool for cross-disciplinary AI readiness assessment. The use of both EFA and CFA on an adequately powered sample enhances the scale’s construct validity. Furthermore, the SEM approach provided insights into the interdependency of readiness domains—an area rarely explored in previous validation studies. Policy and Institutional Considerations The findings should prompt institutions to move beyond pilot programs or elective modules and integrate AI education into core health curricula. Successful implementation requires: Educator training in AI and pedagogy Use of interdisciplinary teaching teams Inclusion of simulations and digital case-based learning Continuous evaluation using validated tools like MAIRS The World Health Organization’s Global Strategy on Digital Health (2021) emphasizes that digital competency is not optional—it is foundational to health system resilience. Thus, institutional leaders must recognize AI readiness as a strategic priority. CONCLUSION As artificial intelligence (AI) continues to transform healthcare delivery, the readiness of future healthcare professionals to engage with AI technologies has become a strategic educational priority. This study successfully validated the Medical Artificial Intelligence Readiness Scale (MAIRS) among students in dentistry, nursing, and midwifery—disciplines where AI adoption is increasing but formal education remains limited. The findings confirmed a robust four-factor structure—AI Usage Awareness, Knowledge Level, Vision, and Ethics—with high internal consistency and predictive validity. Importantly, while awareness and ethical interest in AI were relatively strong, significant gaps in technical knowledge were evident. These discrepancies highlight a critical need for comprehensive, interdisciplinary AI curricula that combine conceptual understanding, hands-on experience, and ethical reasoning. The validated MAIRS tool offers a practical framework for benchmarking and monitoring AI readiness across diverse student populations. By identifying readiness gaps, the instrument can support curriculum developers, academic institutions, and policymakers in designing targeted educational interventions. Ultimately, enhancing AI literacy across all health professions will ensure that technological advancement aligns with clinical safety, ethical responsibility, and patient-centered care. LIMITATIONS Several limitations should be acknowledged. First, the sample was drawn from a single university in Turkey, which may limit the generalizability of the findings to other cultural or institutional contexts. Second, the cross-sectional design precludes inferences about changes in AI readiness over time or causal relationships among variables. Third, the study relied on self-reported measures, which may introduce social desirability bias, particularly in responses related to ethics and awareness. Lastly, although the MAIRS scale was adapted for non-medical students, qualitative feedback or cognitive interviews were not incorporated into the validation process—future studies may benefit from mixed-method approaches to enhance interpretability and depth. PRACTICAL IMPLICATIONS The validated MAIRS scale provides a robust tool for health education institutions to systematically assess AI readiness in non-medical student populations. Its application can guide curriculum development by identifying specific areas—such as technical knowledge—that require targeted intervention. Integrating AI education into core curricula for nursing, dental, and midwifery programs is no longer optional; it is essential to ensure equitable preparedness across the healthcare workforce. Educational interventions should prioritize: Case-based and simulation-based AI learning Practical exposure to clinical AI tools (e.g., radiology, diagnostics) Ethical deliberation embedded in real-world contexts Interprofessional training that reflects AI’s team-based use in practice Institutional leaders should also invest in faculty development to ensure educators possess the necessary AI literacy to effectively deliver content. National education policies may consider mandating digital health and AI competencies across all health sciences curricula. FUTURE RESEARCH DIRECTIONS Future studies should aim to validate the MAIRS scale in multinational and multilingual contexts to examine its cross-cultural stability. Longitudinal research designs are needed to capture how AI readiness evolves over time and in response to curricular interventions. Additionally, replication of this study in postgraduate and professional settings could provide insights into lifelong AI learning needs. Suggested avenues include: Experimental studies testing specific AI teaching methods (e.g., flipped classrooms, gamification) Qualitative studies (focus groups, interviews) to explore student perceptions of AI and ethics Cross-disciplinary comparisons to assess readiness variations between health professions AI readiness as a predictor of clinical performance or decision-making quality Such research will help ensure that AI integration in health education is evidence-based, inclusive, and ethically grounded. Abbreviations AI – Artificial Intelligence MAIRS – Medical Artificial Intelligence Readiness Scale CFA – Confirmatory Factor Analysis EFA – Exploratory Factor Analysis SEM – Structural Equation Modeling RMSEA – Root Mean Square Error of Approximation SRMR – Standardized Root Mean Square Residual KMO – Kaiser-Meyer-Olkin Measure Declarations Ethics approval and consent to participate Ethical approval for the study was granted by the Karabük University Ethics Committee (Approval No: 2023/1215). All participants provided informed consent electronically prior to data collection. The study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. This study does not include any individual-level data or images requiring consent. Availability of data and materials The dataset supporting the findings of this study is available from the corresponding author upon reasonable request. Competing interests The author declares no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions Ahmet Düha Koç: Conceptualization, Methodology, Data Analysis, Writing – Original Draft, Review & Editing. The author approved the final manuscript. Acknowledgements Not applicable. References Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7 Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29. https://doi.org/10.1038/s41591-018-0316-z Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept 'black box' medicine? Ann Intern Med. 2019;172(1):59–60. https://doi.org/10.7326/M19-2548 Hashimoto DA, Witkowski E, Gao L, Meireles OR, Rosman G. 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JAMA. 2019;321(23):2281–2282. https://doi.org/10.1001/jama.2019.4914 Karaca Ö, Çalışkan SA, Demir K. Medical Artificial Intelligence Readiness Scale for medical students (MAIRS-MS): Development, validity, and reliability study. BMC Med Educ. 2021;21(1):112. https://doi.org/10.1186/s12909-021-02546-6 von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, et al. The STROBE statement: Guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. https://doi.org/10.1371/journal.pmed.0040296 Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 8th ed. Pearson; 2019. ISBN: 9781473756540 Lynn MR. Determination and quantification of content validity. Nurs Res. 1986;35(6):382–385. Williams B, Onsman A, Brown T. Exploratory Factor Analysis: A Five-Step Guide for Novices. Australas J Paramedicine. 2010;8:1–13. https://doi.org/10.33151/ajp.8.3.93 Hu LT, Bentler PM. 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The integration of AI into healthcare is not limited to physicians; professionals in dentistry, nursing, and allied health are also encountering AI tools in diagnostic imaging, patient monitoring, and clinical decision-making [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the incorporation of AI education into healthcare curricula remains uneven. While some medical schools have initiated AI literacy programs, similar efforts in dental and nursing education are scarce [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This disparity poses a risk of digital illiteracy among future healthcare workers, potentially compromising patient safety and system efficiency.\u003c/p\u003e \u003cp\u003eTo address this challenge, health education must ensure that students possess not only basic AI knowledge, but also a critical understanding of ethical, legal, and practical aspects of AI deployment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In response, the concept of AI readiness has emerged\u0026ndash;defined as a multidimensional construct involving awareness, knowledge, vision, and ethics\u0026ndash;enhanced environments [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the growing relevance of AI readiness, validated measurement tools applicable across healthcare disciplines are scarce. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) is a notable instrument, developed specifically for medical students to assess awareness, knowledge, vision, and ethical dimensions of AI [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, its applicability to non-medical students has not been formally validated.\u003c/p\u003e \u003cp\u003eThis study aims to fill that gap by adapting and validating the MAIRS scale among students in dentistry, nursing, and midwifery. Establishing a reliable and generalizable tool for assessing AI readiness in diverse healthcare disciplines is essential for guiding curriculum development and aligning education with the demands of an AI-integrated health system.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional, instrument validation design to assess the psychometric properties of the Medical Artificial Intelligence Readiness Scale (MAIRS) among undergraduate students in dental, nursing, and midwifery programs. The methodology followed the STROBE guidelines for observational studies to ensure scientific rigor and transparency [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Sampling\u003c/h3\u003e\n\u003cp\u003eA total of 376 undergraduate students from a Turkish public university were recruited using stratified random sampling to ensure balanced representation across disciplines. Inclusion criteria included active enrollment in health-related undergraduate programs and voluntary participation. Exclusion criteria were incomplete responses or lack of informed consent.\u003c/p\u003e \u003cp\u003eThe sample size was determined based on accepted psychometric validation principles, with recommendations suggesting at least 10 respondents per item for structural equation modeling [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The final sample size exceeded this benchmark.\u003c/p\u003e\n\u003ch3\u003eInstrument\u003c/h3\u003e\n\u003cp\u003eThe original MAIRS scale, developed by Karaca et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], includes 22 items across four subscales: AI Usage Awareness, AI Knowledge Level, AI Vision, and AI Ethics. The scale was translated and adapted linguistically and contextually for non-medical health sciences students. Expert validation was conducted by six academic professionals in artificial intelligence and health education, and content validity was evaluated according to Lynn\u0026rsquo;s method [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEach item used a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 5\u0026thinsp;=\u0026thinsp;Strongly Agree).\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected via an anonymous online survey platform between February and May 2024. Informed consent was obtained electronically prior to participation. The study protocol received ethical approval from the Karab\u0026uuml;k University Ethics Committee (Approval No: 2023/1215), in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical procedures were conducted using RStudio (version 4.4.3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExploratory Factor Analysis (EFA)\u003c/h2\u003e \u003cp\u003eEFA was conducted to uncover the underlying factor structure using principal component analysis with varimax rotation. Sampling adequacy was confirmed using the Kaiser-Meyer-Olkin (KMO) measure, and Bartlett\u0026rsquo;s test of sphericity assessed the appropriateness of factor analysis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConfirmatory Factor Analysis (CFA)\u003c/h3\u003e\n\u003cp\u003eCFA was performed using the lavaan package in R. Model fit was evaluated using the following criteria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]:\u003c/p\u003e\n\u003ch3\u003e• CFI ≥ 0.90\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.90\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u0026bull; RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.08\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003e\u0026bull; SRMR\u0026thinsp;\u0026le;\u0026thinsp;0.08\u003c/h2\u003e \u003cp\u003e \u003cem\u003eInternal Consistency and Reliability\u003c/em\u003e \u003c/p\u003e \u003cp\u003eReliability was assessed using Cronbach\u0026rsquo;s alpha and Composite Reliability (CR). An alpha value above 0.80 and CR above 0.70 were considered acceptable [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStructural Equation Modeling (SEM)\u003c/h2\u003e \u003cp\u003eSEM was used to examine hypothesized relationships among latent variables. The significance of standardized path coefficients was reported, and model fit indices mirrored those used in CFA.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eA total of 376 participants completed the survey, comprising students from dental (32%), nursing (38%), and midwifery (30%) programs. The sample was predominantly female (78.4%), reflecting the demographic structure of the participating disciplines. The average age was 21.3 years (SD\u0026thinsp;=\u0026thinsp;1.6).\u003c/p\u003e \u003cp\u003eThe distribution of mean scores and standard deviations across the four AI readiness subscales is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. While students demonstrated moderate levels of AI Usage Awareness and Vision, their technical knowledge lagged behind, underscoring a critical readiness gap.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe highest mean score was observed in the AI Vision subscale (M\u0026thinsp;=\u0026thinsp;3.42, SD\u0026thinsp;=\u0026thinsp;0.68), followed by AI Usage Awareness (M\u0026thinsp;=\u0026thinsp;3.21, SD\u0026thinsp;=\u0026thinsp;0.76), and AI Ethics (M\u0026thinsp;=\u0026thinsp;3.18, SD\u0026thinsp;=\u0026thinsp;0.73). The lowest mean was recorded in AI Knowledge Level (M\u0026thinsp;=\u0026thinsp;2.95, SD\u0026thinsp;=\u0026thinsp;0.81), suggesting a general lack of technical understanding despite high conceptual and ethical awareness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInternal Consistency\u003c/h2\u003e \u003cp\u003eReliability analysis demonstrated high internal consistency across all four subscales (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These values exceed the conventional threshold of 0.80 for psychological instruments, supporting the reliability of the adapted scale [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\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\u003eInternal consistency reliability of MAIRS subscales\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubscale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Usage Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Knowledge Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Vision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExploratory Factor Analysis (EFA)\u003c/h2\u003e \u003cp\u003eThe Kaiser-Meyer-Olkin (KMO) measure was 0.94, indicating excellent sampling adequacy. Bartlett\u0026rsquo;s Test of Sphericity was statistically significant (χ\u0026sup2;(231)\u0026thinsp;=\u0026thinsp;3745.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the appropriateness of factor analysis.\u003c/p\u003e \u003cp\u003eEFA using principal component analysis with varimax rotation revealed a four-factor structure aligned with the original scale, explaining 58.6% of total variance:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFactor 1: AI Usage Awareness (7 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactor 2: AI Knowledge Level (7 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactor 3: AI Vision (3 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFactor 4: AI Ethics (5 items)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll items demonstrated strong factor loadings (\u0026gt;\u0026thinsp;0.70), and no cross-loading was observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis (CFA)\u003c/h2\u003e \u003cp\u003eCFA validated the four-factor model using maximum likelihood estimation. The model demonstrated acceptable fit across multiple indices (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All standardized factor loadings were significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and exceeded 0.60, confirming convergent validity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. No model modifications were necessary, and residuals remained within acceptable limits.\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\u003eFit indices for the confirmatory factor analysis (CFA) model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.910\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.079\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eComposite Reliability and Average Variance Extracted\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eComposite Reliability (CR) values ranged from 0.84 to 0.93\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAverage Variance Extracted (AVE) values ranged from 0.62 to 0.74 \u003cp\u003eThese metrics support strong construct validity, consistent with the recommendations of Fornell and Larcker [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStructural Equation Modeling (SEM)\u003c/h2\u003e \u003cp\u003eSEM was employed to test the predictive pathways among latent variables, examining the effects of AI Usage Awareness, AI Vision, and AI Ethics on AI Knowledge Level. The model showed good overall fit across multiple indices (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePath coefficients indicated strong and statistically significant relationships (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eFit indices for the structural equation model (SEM)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.918\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.902\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.076\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.062\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\u003ePath coefficients were as follows:\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\u003eStandardized path coefficients predicting AI Knowledge Level\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage Awareness \u0026rarr; Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision \u0026rarr; Knowledge\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\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics \u0026rarr; Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\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\u003eThese results suggest that students with greater AI awareness and future-oriented vision are more likely to exhibit higher levels of AI knowledge, reinforcing the experiential and motivational dimensions of digital readiness [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study validated the Medical Artificial Intelligence Readiness Scale (MAIRS) among undergraduate students in dentistry, nursing, and midwifery\u0026mdash;populations traditionally underrepresented in AI education research. The findings demonstrate that the scale retains strong psychometric properties in non-medical cohorts, suggesting that the four domains of AI readiness\u0026mdash;Usage Awareness, Knowledge Level, Vision, and Ethics\u0026mdash;are robust constructs that transcend disciplinary boundaries.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eAlignment with Existing Literature\u003c/h2\u003e \u003cp\u003eOur results echo previous findings that AI awareness is relatively high, while technical knowledge remains limited among healthcare students [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This discrepancy reflects a global trend in which AI is perceived as relevant but remains poorly understood on a functional level, particularly among nursing and dental students who lack structured exposure to digital Technologies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the strong predictive role of AI Usage Awareness on Knowledge Level (β\u0026thinsp;=\u0026thinsp;1.046, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) supports experiential learning theories, which emphasize that familiarity with tools increases conceptual understanding and motivation to learn [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This suggests that mere curricular inclusion of AI content may be insufficient unless paired with active, hands-on learning environments.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003eImplications for AI Curriculum Design\u003c/h2\u003e \u003cp\u003eThe scale\u0026rsquo;s validation for non-medical health students highlights a critical gap in curriculum design. Current health education programs often fail to prepare students for interdisciplinary, technology-rich clinical environments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given the increasing use of AI in diagnostics, triage, and decision support, a digitally literate workforce must include nurses, midwives, and dental professionals\u0026mdash;not just physicians [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAI Vision, defined as the ability to conceptualize future roles of AI in healthcare, also significantly predicted knowledge. This aligns with educational psychology literature suggesting that future-oriented cognition fosters deeper engagement and willingness to acquire technical skills [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, AI Ethics, while statistically significant, had the weakest predictive value. This may indicate that ethical considerations are often perceived as abstract or secondary to practical knowledge. Given the high-stakes nature of AI in patient care\u0026mdash;where algorithmic bias, privacy, and explainability are critical\u0026mdash;this disconnection poses a risk. Ethics instruction should be made more concrete and embedded in real-world case simulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMethodological Contributions\u003c/h2\u003e \u003cp\u003eFrom a psychometric perspective, this study contributes to the literature by extending the use of MAIRS beyond medical students, offering a validated and reliable tool for cross-disciplinary AI readiness assessment. The use of both EFA and CFA on an adequately powered sample enhances the scale\u0026rsquo;s construct validity. Furthermore, the SEM approach provided insights into the interdependency of readiness domains\u0026mdash;an area rarely explored in previous validation studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003ePolicy and Institutional Considerations\u003c/h2\u003e \u003cp\u003eThe findings should prompt institutions to move beyond pilot programs or elective modules and integrate AI education into core health curricula. Successful implementation requires:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEducator training in AI and pedagogy\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse of interdisciplinary teaching teams\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInclusion of simulations and digital case-based learning\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eContinuous evaluation using validated tools like MAIRS\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe World Health Organization\u0026rsquo;s Global Strategy on Digital Health (2021) emphasizes that digital competency is not optional\u0026mdash;it is foundational to health system resilience. Thus, institutional leaders must recognize AI readiness as a strategic priority.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAs artificial intelligence (AI) continues to transform healthcare delivery, the readiness of future healthcare professionals to engage with AI technologies has become a strategic educational priority. This study successfully validated the Medical Artificial Intelligence Readiness Scale (MAIRS) among students in dentistry, nursing, and midwifery\u0026mdash;disciplines where AI adoption is increasing but formal education remains limited.\u003c/p\u003e \u003cp\u003eThe findings confirmed a robust four-factor structure\u0026mdash;AI Usage Awareness, Knowledge Level, Vision, and Ethics\u0026mdash;with high internal consistency and predictive validity. Importantly, while awareness and ethical interest in AI were relatively strong, significant gaps in technical knowledge were evident. These discrepancies highlight a critical need for comprehensive, interdisciplinary AI curricula that combine conceptual understanding, hands-on experience, and ethical reasoning.\u003c/p\u003e \u003cp\u003eThe validated MAIRS tool offers a practical framework for benchmarking and monitoring AI readiness across diverse student populations. By identifying readiness gaps, the instrument can support curriculum developers, academic institutions, and policymakers in designing targeted educational interventions. Ultimately, enhancing AI literacy across all health professions will ensure that technological advancement aligns with clinical safety, ethical responsibility, and patient-centered care.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the sample was drawn from a single university in Turkey, which may limit the generalizability of the findings to other cultural or institutional contexts. Second, the cross-sectional design precludes inferences about changes in AI readiness over time or causal relationships among variables. Third, the study relied on self-reported measures, which may introduce social desirability bias, particularly in responses related to ethics and awareness. Lastly, although the MAIRS scale was adapted for non-medical students, qualitative feedback or cognitive interviews were not incorporated into the validation process\u0026mdash;future studies may benefit from mixed-method approaches to enhance interpretability and depth.\u003c/p\u003e \u003c/div\u003e"},{"header":"PRACTICAL IMPLICATIONS","content":"\u003cp\u003eThe validated MAIRS scale provides a robust tool for health education institutions to systematically assess AI readiness in non-medical student populations. Its application can guide curriculum development by identifying specific areas—such as technical knowledge—that require targeted intervention. Integrating AI education into core curricula for nursing, dental, and midwifery programs is no longer optional; it is essential to ensure equitable preparedness across the healthcare workforce.\u003c/p\u003e \u003cp\u003eEducational interventions should prioritize:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eCase-based and simulation-based AI learning\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePractical exposure to clinical AI tools (e.g., radiology, diagnostics)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEthical deliberation embedded in real-world contexts\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInterprofessional training that reflects AI’s team-based use in practice\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eInstitutional leaders should also invest in faculty development to ensure educators possess the necessary AI literacy to effectively deliver content. National education policies may consider mandating digital health and AI competencies across all health sciences curricula.\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"FUTURE RESEARCH DIRECTIONS","content":"\u003cp\u003eFuture studies should aim to validate the MAIRS scale in multinational and multilingual contexts to examine its cross-cultural stability. Longitudinal research designs are needed to capture how AI readiness evolves over time and in response to curricular interventions. Additionally, replication of this study in postgraduate and professional settings could provide insights into lifelong AI learning needs.\u003c/p\u003e\u003cp\u003eSuggested avenues include:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eExperimental studies testing specific AI teaching methods (e.g., flipped classrooms, gamification)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eQualitative studies (focus groups, interviews) to explore student perceptions of AI and ethics\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCross-disciplinary comparisons to assess readiness variations between health professions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAI readiness as a predictor of clinical performance or decision-making quality\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eSuch research will help ensure that AI integration in health education is evidence-based, inclusive, and ethically grounded.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI \u0026ndash; Artificial Intelligence \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAIRS \u0026ndash; Medical Artificial Intelligence Readiness Scale \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCFA \u0026ndash; Confirmatory Factor Analysis \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEFA \u0026ndash; Exploratory Factor Analysis \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSEM \u0026ndash; Structural Equation Modeling \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRMSEA \u0026ndash; Root Mean Square Error of Approximation \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSRMR \u0026ndash; Standardized Root Mean Square Residual \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKMO \u0026ndash; Kaiser-Meyer-Olkin Measure\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Ethical approval for the study was granted by the Karab\u0026uuml;k University Ethics Committee (Approval No: 2023/1215). All participants provided informed consent electronically prior to data collection. The study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable. This study does not include any individual-level data or images requiring consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The dataset supporting the findings of this study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author declares no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Ahmet D\u0026uuml;ha Ko\u0026ccedil;: Conceptualization, Methodology, Data Analysis, Writing \u0026ndash; Original Draft, Review \u0026amp; Editing. The author approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTopol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44\u0026ndash;56. https://doi.org/10.1038/s41591-018-0300-7\u003c/li\u003e\n\u003cli\u003eEsteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24\u0026ndash;29. https://doi.org/10.1038/s41591-018-0316-z\u003c/li\u003e\n\u003cli\u003eWang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept \u0026apos;black box\u0026apos; medicine? Ann Intern Med. 2019;172(1):59\u0026ndash;60. https://doi.org/10.7326/M19-2548\u003c/li\u003e\n\u003cli\u003eHashimoto DA, Witkowski E, Gao L, Meireles OR, Rosman G. Artificial intelligence in anesthesiology: Current techniques and clinical applications. Anesthesiology. 2020;132(2):379\u0026ndash;394. https://doi.org/10.1097/ALN.0000000000002960\u003c/li\u003e\n\u003cli\u003eSchwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020;99(7):769\u0026ndash;774. https://doi.org/10.1177/0022034520915714\u003c/li\u003e\n\u003cli\u003eParanjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5(2):e16048. https://doi.org/10.2196/16048\u003c/li\u003e\n\u003cli\u003eRajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347\u0026ndash;1358. https://doi.org/10.1056/NEJMra1814259\u003c/li\u003e\n\u003cli\u003eMesk\u0026oacute; B, G\u0026ouml;r\u0026ouml;g M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med. 2020;3:126. https://doi.org/10.1038/s41746-020-00333-z\u003c/li\u003e\n\u003cli\u003eEmanuel EJ, Wachter RM. Artificial intelligence in health care: Will the value match the hype? JAMA. 2019;321(23):2281\u0026ndash;2282. https://doi.org/10.1001/jama.2019.4914\u003c/li\u003e\n\u003cli\u003eKaraca \u0026Ouml;, \u0026Ccedil;alışkan SA, Demir K. Medical Artificial Intelligence Readiness Scale for medical students (MAIRS-MS): Development, validity, and reliability study. BMC Med Educ. 2021;21(1):112. https://doi.org/10.1186/s12909-021-02546-6\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, et al. The STROBE statement: Guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. https://doi.org/10.1371/journal.pmed.0040296\u003c/li\u003e\n\u003cli\u003eHair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 8th ed. Pearson; 2019. ISBN: 9781473756540\u003c/li\u003e\n\u003cli\u003eLynn MR. Determination and quantification of content validity. Nurs Res. 1986;35(6):382\u0026ndash;385.\u003c/li\u003e\n\u003cli\u003eWilliams B, Onsman A, Brown T. Exploratory Factor Analysis: A Five-Step Guide for Novices. Australas J Paramedicine. 2010;8:1\u0026ndash;13. https://doi.org/10.33151/ajp.8.3.93\u003c/li\u003e\n\u003cli\u003eHu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999;6(1):1\u0026ndash;55. https://doi.org/10.1080/10705519909540118\u003c/li\u003e\n\u003cli\u003eFornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39\u0026ndash;50. https://doi.org/10.2307/3151312\u003c/li\u003e\n\u003cli\u003eKolb DA. Experiential learning: Experience as the source of learning and development. FT Press; 2014. ISBN: 9780133892406\u003c/li\u003e\n\u003cli\u003eVenkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q. 2003;27(3):425\u0026ndash;478. https://doi.org/10.2307/30036540\u003c/li\u003e\n\u003cli\u003eSng QW, Soh M, Loh VWK, et al. Attitudes toward artificial intelligence in healthcare: A cross-sectional survey among nursing students in Singapore. Nurse Educ Today. 2021;97:104706. [Unavailable DOI: https://doi.org/10.1016/j.nedt.2020.104706]. Available via Google Scholar\u003c/li\u003e\n\u003cli\u003eKolachalama VB, Garg PS. Machine learning and medical education. NPJ Digit Med. 2018;1:54. https://doi.org/10.1038/s41746-018-0061-1\u003c/li\u003e\n\u003cli\u003eChan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education. Med Educ. 2019;53(3):231\u0026ndash;238. https://doi.org/10.2196/13930\u003c/li\u003e\n\u003cli\u003eWartman SA, Combs CD. Medical education must move from the information age to the age of artificial intelligence. Acad Med. 2018;93(8):1107\u0026ndash;1109. https://doi.org/10.1097/ACM.0000000000002044\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global strategy on digital health 2020\u0026ndash;2025. Geneva: WHO; 2021. Available from: https://www.who.int/publications/i/item/9789240020924\u003c/li\u003e\n\u003cli\u003evon Bertalanffy L. General system theory: Foundations, development, applications. New York: George Braziller; 1969.\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, Dental Education, Nursing Education, AI Readiness, Health Sciences","lastPublishedDoi":"10.21203/rs.3.rs-6414139/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6414139/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eArtificial intelligence (AI) is rapidly transforming healthcare. However, validated tools to assess AI readiness in non-medical health disciplines remain scarce. This study aimed to adapt and validate the MAIRS scale among dental, nursing, and midwifery students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study involving 376 students was conducted. Exploratory and confirmatory factor analysis, along with structural equation modeling, were used to validate the adapted scale. Internal consistency and predictive validity were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe adapted MAIRS scale retained a four-factor structure with excellent reliability (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.89\u0026ndash;0.92). AI Usage Awareness was the strongest predictor of AI Knowledge Level (β\u0026thinsp;=\u0026thinsp;1.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Students demonstrated high awareness and ethical concern but limited technical understanding.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe MAIRS scale is valid and reliable for assessing AI readiness in non-medical health education. Findings highlight the urgent need to integrate AI education into undergraduate health curricula.\u003c/p\u003e","manuscriptTitle":"Expanding the Scope of Ai Readiness: Validation of the Mairs Scale Among Dental, Nursing, and Midwifery Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 06:40:23","doi":"10.21203/rs.3.rs-6414139/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":"d406a562-d868-49a9-8cfb-d0be7262604d","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-17T07:08:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 06:40:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6414139","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6414139","identity":"rs-6414139","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}