From Everyday Technology Experience to AI Acceptance in Healthcare Education: A Self-determination Theory–based Model

From Everyday Technology Experience to AI Acceptance in Healthcare Education: A Self-determination Theory–based Model | 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 From Everyday Technology Experience to AI Acceptance in Healthcare Education: A Self-determination Theory–based Model Stefano Ardenghi, Marco Bani, Selena Russo, Niccolò Cremona, Federico Zorzi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9254457/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The integration of artificial intelligence (AI) into healthcare requires preparing professionals to engage critically with intelligent systems. Moving beyond isolated predictors, this study investigates a multi-layered psychological model of AI acceptance, drawing on Self-Determination Theory (SDT), and proposes a psychologically grounded model in which the satisfaction of basic psychological needs serves as a proximal mechanism shaping attitudes toward AI. A cross-sectional survey was conducted with 233 Italian healthcare students and professionals. Participants completed validated measures of attitudes toward AI, psychological need satisfaction in relation to technology (autonomy, competence, relatedness), personality traits (Big Five and Dark Triad), conspiracy beliefs, and AI literacy. Hierarchical regression analyses were used to test the incremental contribution of motivational, dispositional, and cognitive factors. Additionally, comparative evaluations were conducted to assess the perceived competence of AI versus human professionals across different clinical domains. The final models explained a substantial proportion of the variance in both positive and negative attitudes. SDT variables provided strong incremental validity, emerging as the primary factors in promoting AI acceptance and mitigating AI aversion. Regarding comparative competence, AI was perceived as outperforming humans in system-level and analytical tasks, but significantly less skilled in domains requiring psychological and relational support. Findings support a theory-driven account of AI acceptance in healthcare education, highlighting psychological need satisfaction as a key mechanism by which individuals evaluate emerging technologies. This extends SDT to the domain of human–AI interaction, suggesting that acceptance depends not only on knowledge or exposure, but on whether engagement with AI supports autonomy, competence, and relatedness. AI training fostering a meaningful and responsible integration of AI in healthcare should move beyond technical instruction to include participatory, competence-building, and reflective approaches that address learners’ motivational needs. Artificial intelligence Healthcare Self-Determination Theory Personality traits AI literacy Introduction Artificial intelligence (AI) is transforming the healthcare sector by providing advanced solutions for early diagnosis (K. Chen et al., 2025 ), clinical data management (Gupta & Kumar, 2023 ), and therapeutic decision support (Karuppan Perumal et al., 2025 ). AI-powered analytics are crucial to advancing precision medicine by enabling personalized treatments based on individual patient profiles (Rishi Reddy Kothinti, 2024 ). These developments have the potential to significantly improve both the effectiveness and overall quality of healthcare services. The integration of AI into healthcare settings presents significant challenges related to the education and training of professionals (Love et al., 2025 ; Masoumian Hosseini et al., 2024 ; Younis et al., 2024 ). Today’s healthcare students and professionals are not only required to operate advanced technological tools but also cultivate a critical understanding of the capabilities, limitations, and ethical considerations associated with intelligent technologies (Goankar et al., 2020 ). Consequently, health professions education is facing the challenge of preparing both students and practitioners to engage with AI in informed and responsible ways. However, the successful integration of AI into clinical practice relies not only on the availability of technology but also on the subjective perceptions healthcare professionals develop towards these tools, particularly in relation to how they assess AI’s capabilities in comparison to human clinicians (Schepman & Rodway, 2020 ). Empirical studies show that AI tends to be perceived as more competent than humans in data-intensive, structured, and analytical tasks – such as diagnostics, risk stratification, or resource management – where it can outperform humans in accuracy and speed (Asan et al., 2020 ; Kaplan et al., 2023 ). Conversely, AI is often seen as less effective in domains that require emotional intelligence, ethical sensitivity, and relational competence, such as psychological support or shared decision-making (Longoni et al., 2019 ; Montemayor et al., 2022 ). These evaluations are shaped by a range of individual-level variables that play a key role in influencing the acceptance or rejection of digital innovations. Among sociodemographic variables, several predictors have consistently emerged in shaping a more positive attitude toward AI, such as being male, younger, and having higher academic qualifications (Kozak & Fel, 2024 ). Despite growing interest in attitudes toward AI in healthcare, existing research has been largely descriptive, focusing on sociodemographic differences or isolated psychological predictors of acceptance. While these studies provide useful insights, they often lack a coherent theoretical framework to explain why individuals develop favorable or unfavorable attitudes toward AI. Furthermore, most studies fail to distinguish between the effects of stable, distal individual traits (such as personality) and more proximal, experiential factors. As a result, current evidence remains fragmented, limiting its contribution to the design of educational interventions to foster meaningful and critical engagement with AI technologies (Habib et al., 2024 ). For instance, dispositional traits, such as personality dimensions (Ozbey & Yasa, 2025 ; Salem et al., 2024 ; Zabel et al., 2025 ) and socially aversive characteristics (e.g., Dark Triad traits; Wissing & Reinhard, 2018 ), may influence openness to innovation, interpersonal trust, and strategic orientations toward technology. Similarly, conspiracy beliefs (Stein et al., 2024 ) may reflect broader tendencies toward epistemic distrust, which can negatively affect perceptions of complex and opaque systems such as AI. Finally, AI literacy represents a cognitive dimension that may enable individuals to better understand and critically appraise AI applications, while also increasing awareness of their ethical implications (Asio & Gadia, 2024 ; Laupichler et al., 2024 ). However, rather than acting as independent and isolated predictors, these factors can be conceptualized as contributing to a multi-layered psychological model in which proximal motivational processes remain central. To address this limitation, the present study draws on Self-Determination Theory (SDT; Deci & Ryan, 1985 ) to propose a multi-layered psychological model of AI acceptance in healthcare. According to SDT, individuals thrive when three fundamental psychological needs are met: autonomy (the sense of volition and agency), competence (the feeling of effectiveness and capability), and relatedness (the experience of meaningful connection). When these core needs are fulfilled, people are more inclined to participate in activities that they find personally valuable and intrinsically rewarding. Rather than assessing these needs in the abstract, we argue that the everyday technological ecosystem acts as a crucial bridge to AI acceptance. Recent research underscores the relevance of SDT in shaping attitudes toward AI and technology (Li et al., 2025 ). Individuals who perceive greater autonomy, competence, and relatedness in their technological interactions tend to report more favorable attitudes, higher perceived usefulness, and greater engagement with intelligent systems (Bergdahl et al., 2023 ). In this study, we adopt a hierarchical approach to conceptualize how motivational, dispositional, and cognitive processes interplay. We posit that dispositional traits (such as personality dimensions) and epistemic attitudes (like conspiracy beliefs) act as distal modulators, setting a baseline orientation toward innovation and trust. Similarly, AI literacy represents a cognitive baseline that enables critical appraisal. However, we hypothesize that the proximal experience of satisfaction of the basic psychological needs for autonomy, competence, and relatedness in everyday interactions with technology will provide unique, incremental predictive value over and above these traits and cognitive characteristics. By integrating SDT with individual difference variables, this study seeks to contribute to a more conceptually coherent understanding of AI acceptance in health professions education. Within this framework, we aim to (1) test a theoretically grounded, hierarchical model of general attitudes toward AI among healthcare students and professionals, isolating the specific contribution of SDT variables, and (2) explore how AI is perceived in terms of its comparative competence across clinically relevant domains (system-level, clinical, and relational tasks). Based on prior research and relevant theoretical frameworks, we hypothesize that: H1: Sociodemographic factors will be significantly associated with attitudes toward AI. H1a: Males will report more positive attitudes toward AI compared to female participants. H1b: Younger individuals will exhibit more favorable attitudes toward AI. H1c: Healthcare professionals (as a proxy for higher educational attainment) will report more positive attitudes toward AI compared to students. H2: Personality dimensions are expected to influence AI-related attitudes. H2a: Higher levels of agreeableness and openness to experience, and lower levels of neuroticism, will be associated with more positive attitudes toward AI. H2b: Elevated scores on Dark Triad traits will correlate with more negative attitudes toward AI. H2c: Higher endorsement of conspiracy beliefs will predict greater aversion and distrust toward AI systems. H3: Higher levels of AI literacy will be positively associated with acceptance of AI. H4: The extent to which daily technology use supports users' basic psychological needs (autonomy, competence, and relatedness) will positively predict optimistic attitudes toward AI and significantly mitigate negative concerns and mistrust. H5: AI will be perceived as more competent than human healthcare professionals in system-level tasks, but less skilled in domains requiring psychological support. Methods Procedure This study employed a cross-sectional survey design. Participants were recruited voluntarily from university programs, professional associations, and social media groups for healthcare professionals/students in Italy between December 2024 and May 2025. The target population included healthcare professionals (physicians, nurses, other allied health professionals) and students enrolled in medicine, nursing, and related health science programs in Italy. Data were collected using an anonymous online questionnaire administered via Qualtrics. An introductory page explained the study’s purpose, assured confidentiality and anonymity, outlined the voluntary nature of participation, and contained the informed consent form. Participants indicated their consent before proceeding to the questionnaire, which took approximately 20 minutes to complete. The study received approval from the Research Evaluation Committee of the Department of Psychology of the University of Milano-Bicocca (protocol number: RM-2024-905) and was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki. Materials To assess participants’ general attitudes toward AI, the Italian translation of the General Attitudes Towards AI Scale (GAAIS; Schepman & Rodway, 2020 ; Sacco et al., 2025 ) was employed. The scale consists of 20 items and is composed of 2 subscales: the Positive subscale includes 12 items that capture perceived benefits, interest, and optimism regarding AI (e.g., “I am interested in using artificially intelligent systems in my daily life”), while the Negative subscale includes 8 items, assessing concerns, discomfort, and mistrust toward AI (e.g., “I think Artificial Intelligence is dangerous”). Responses are rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating more positive (or less negative, depending on the subscale) attitudes toward AI. To assess personality traits, the Italian validation of the revised Ten Item Personality Inventory (TIPI-R; Chiorri et al., 2015 ; Gosling et al., 2003 ), a brief measure of the Big Five personality dimensions, was used. The TIPI-R evaluates 5 core personality traits: Extraversion (the tendency to experience positive emotions, be energetic, decisive, sociable, and talkative), Agreeableness (the tendency to be compassionate, cooperative, and well-disposed toward others), Conscientiousness (the tendency to be organized, reliable, self-disciplined, and task-focused), Neuroticism (the tendency to experience negative emotional states such as anger, anxiety, or sadness), and Openness to Experience (the tendency to be curious, imaginative, and appreciative of art, adventure, and novelty). The scale consists of 10 items, with 2 items for each personality trait. Responses are given on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with higher mean scores indicating a stronger presence of the corresponding personality trait. To assess socially aversive personality traits, we administered the Italian validated version of the Short Dark Triad (SD3; Jones & Paulhus, 2014 ; Somma et al., 2020 ). The SD3 is a 27-item self-report questionnaire specifically developed to measure the 3 core components of the so-called “Dark Triad”: Narcissism, Machiavellianism, and Psychopathy. The Machiavellianism subscale (e.g., “Make sure your plans benefit yourself, not others”) assesses manipulative interpersonal strategies, strategic planning, and a cynical view of human nature. The Narcissism subscale (e.g., “Many group activities tend to be dull without me”) captures traits such as grandiosity, entitlement, and a need for admiration. The Psychopathy subscale (e.g., “People who mess with me always regret it”) reflects impulsivity, callousness, and a lack of empathy or remorse. Each subscale consists of nine items, and responses are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating a stronger presence of the corresponding personality trait. To assess general conspiratorial thinking, the Italian validated version of the Conspiracy Mentality Questionnaire (CMQ; Bruder et al., 2013 ; Candini et al., 2023 ) was used. The CMQ is a brief, unidimensional measure consisting of 5 items (e.g., “I think that government agencies closely monitor all citizens”) designed to capture generic beliefs in conspiracy theories, independent of specific events or contexts. Participants respond using a 7-point Likert scale, ranging from 1 (certainly not true) to 7 (certainly true), with higher mean scores indicating a stronger conspiracy mentality. To assess participants’ level of AI literacy, the Italian translation of the Artificial Intelligence Literacy Scale (AILS; B. Wang et al., 2023 ) was administered. It evaluates 4 core dimensions of AI literacy, each consisting of 3 items: Awareness (e.g., “I can distinguish between smart devices and non-smart devices”), Usage (e.g., “I can skillfully use AI applications or products to help me with my daily work”), Evaluation (e.g., “I can evaluate the capabilities and limitations of an AI application or product after using it for a while”), and Ethics (e.g., “I am never alert to privacy and information security issues when using AI applications or products” – reverse scored). The AILS consists of 12 self-report items presented as statements describing behaviors, abilities, or attitudes toward AI-related tasks. Respondents indicate their level of agreement on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating higher levels of AI literacy across the measured dimensions. To assess the perceived impact of technology on the satisfaction of basic psychological needs in daily life, the Italian translation of the TENS-Life scale was adopted. It was originally introduced by Peters et al. (Peters et al., 2018 ), and conceptually grounded in the Basic Psychological Need Satisfaction and Frustration Scale (B. Chen et al., 2015 ). The scale measures the extent to which the use of technology supports or undermines the satisfaction of the 3 basic psychological needs defined by Self-Determination Theory (Deci & Ryan, 1985 ): Autonomy (e.g., “The new technologies intrude in my life” – reverse scored), Competence (e.g., “Using the new technologies has made me feel insecure about my abilities” – reverse scored), and Relatedness (e.g., “Because of these new technologies, I feel closer to some others”). It consists of 9 items, with 3 items for each subscale. Responses are collected using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating greater satisfaction of basic psychological needs in relation to technology use in everyday life. To investigate the perceived capability of AI to perform specific healthcare tasks compared to humans, ChatGPT was used to generate a preliminary list of key AI applications in the sector. Leveraging its capacity to synthesize large volumes of information, this tool offered a valuable foundation for identifying and organizing intervention domains. The list was then validated through expert consultation to ensure relevance and accuracy. The resulting set of 14 items extends the work of Schepman and Rodway (Schepman & Rodway, 2020 ), focusing specifically on healthcare-related domains. Each item is rated on a 5-point Likert scale ranging from 1 (AI much less capable than humans) to 5 (AI much more capable than humans), with higher mean scores indicating that respondents perceive AI as more capable than humans in performing specific healthcare tasks. An exploratory factor analysis (EFA) was conducted to identify the underlying latent structure of the scale. A Kaiser-Meyer-Olkin value of .848 and a significant Bartlett’s test of sphericity (p < .001) confirmed the suitability of the data for factor analysis. The EFA was performed using Principal Component Analysis as the extraction method with Direct Oblimin rotation to allow for factor correlation. The scree plot and the number of eigenvalues greater than 1.00 both suggested retaining a three-factor solution, which accounted for 53.71% of the total cumulative variance. The 3 factors were subsequently interpreted and labeled as follows: Clinical care (8 items; e.g. “AI algorithms can analyze individual patient characteristics – genetic, clinical, and environmental – and suggest optimal treatment plans for each specific case”), Personal support (2 items; e.g., “AI-based tools can assist patients with psychological disorders through automated emotional support, online cognitive-behavioral therapy, or monitoring changes in behavior”), and System management (4 items; e.g., “AI can streamline and automate administrative tasks such as managing electronic health records, scheduling appointments, and processing invoices”). In light of the three identified factors and the content of the items, the questionnaire was named ROBOTH (Representations Of Benefits Of Technology in Healthcare), reflecting its focus on perceived advantages of AI across different domains of healthcare practice (Table S1). Statistical analyses All statistical analyses were conducted using IBM SPSS Statistics (Version 29.0.2.0). Initially, descriptive analyses were performed to explore the distribution of all variables. These included the calculation of frequencies, percentages, means, standard deviations, minimum, maximum, range, as well as indices of skewness and kurtosis to assess the assumption of normality. Given the approximate normal distribution of the data, we proceeded with parametric inferential analyses. To investigate the predictors of positive and negative attitudes toward AI (GAAIS) and test Hypotheses 1 to 4, we conducted two hierarchical linear regression analyses. Predictors were entered in four sequential steps reflecting both methodological and theoretical considerations. At Step 1, sociodemographic variables were entered (biological sex, age, and profession) as control variables; at Step 2, personality-related variables (specifically the dimensions from the TIPI-R, the SD3, and the CMQ) were entered to account for relatively stable individual differences; at Step 3, cognitive variables (AI literacy), were added to capture knowledge- and belief-based influences on attitudes toward AI. In Step 4, SDT variables (TENS-Life) were entered to assess their incremental contribution beyond sociodemographic, dispositional, and cognitive factors. For each step, we reported unstandardized regression coefficients (B), p-values (p), R-squared (R²), change in adjusted R-squared (ΔR²), and F-change statistics to assess model improvement across steps. In addition, independent-samples t-tests and one-way analyses of variance (with Tukey post hoc correction) were used to explore group differences on the ROBOTH scale across levels of sociodemographic variables. Results 336 participants accessed the survey; however, 9 subjects did not give informed consent and could not proceed with the survey. 20 subjects were excluded due to failure to answer one of the control items. Control items were embedded in some questionnaires to detect participants responding randomly (e.g., “This is a control item; answer 'never' to confirm that you have read it”). Finally, 74 subjects were excluded due to mostly incomplete answers. Therefore, the final sample consisted of 233 participants, all of whom were Italian. The majority of participants were female, between 18 and 25 years old, and enrolled in healthcare-related degree programs (Table 1 ). Table 1 Sample characteristics (N = 233) Biological sex N % Male 68 29.2 Female 165 70.8 Age (years) 18–25 121 51.9 26–35 44 18.9 36–45 30 12.9 46–55 13 5.6 56–65 18 7.7 66–75 7 3.0 Profession Healthcare professional 99 42.5 Healthcare student 134 57.5 Table 2 presents the descriptive statistics for all study measures. All variables met the criteria for normality, with skewness and kurtosis values falling within the acceptable range. Table 2 Descriptive statistics for study measures GAAIS Mean (95% CI) SD α min-Max Range Skewness Kurtosis Positive 3.36 (3.312;3.468) .60 .92 1.20–4.68 3.48 − .509 .507 Negative 3.04 (2.976;3.168) .72 .88 1.04–4.64 3.60 − .253 − .459 TIPI-R Extraversion 3.07 (2.866;3.368) 1.48 .52 1.00–6.00 5.00 .064 − .727 Agreeableness 5.15 (5.005;5.286) 1.03 .45 2.00–7.00 5.00 − .267 − .493 Conscientiousness 5.23 (5.066;5.391) 1.19 .41 1.00–7.00 6.00 − .635 .132 Neuroticism 3.59 (3.411;3.780) 1.36 .37 1.00–7.00 6.00 .097 − .639 Openness to experience 4.69 (4.552;4.843) 1.07 .50 1.50-7.00 5.50 − .180 − .103 SD3 Machiavellianism 2.79 (2.709;2.863) .56 .78 1.44–4.11 2.67 − .093 − .390 Narcissism 2.73 (2.653;2.801) .55 .80 1.44–4.44 3.00 .365 .335 Psychopathy 3.09 (2.986;3.201) .79 .74 1.50–4.33 2.83 .746 .955 CMQ 4.36 (4.214;4.501) 1.05 .85 1.20–6.60 5.40 − .597 .218 AILS Awareness 3.33 (3.238;3.419) .67 .73 1.33-5.00 3.67 − .411 − .070 Usage 3.39 (3.279;3.496) .79 .75 1.00–5.00 4.00 − .528 − .001 Evaluation 3.34 (3.247;3.432) .68 .78 1.00–5.00 4.00 − .301 .272 Ethics 3.51 (3.403;3.607) .75 .73 1.33-5.00 3.67 − .273 .014 TENS-Life Autonomy 3.70 (3.567;3.833) .98 .82 1.00–5.00 4.00 − .453 − .622 Competence 3.86 (3.733;3.993) .96 .84 1.33-5.00 3.67 − .576 − .680 Relatedness 2.57 (2.430;2.703) 1.01 .80 1.00-4.67 3.67 − .149 -1.065 ROBOTH Clinical care 3.32 (3.219;3.413) .69 .80 1.50-5.00 3.50 − .166 .091 Personal support 2.15 (2.036;2.271) .84 .48 1.00–5.00 4.00 .563 .028 System management 3.90 (3.808;3.998) .68 .73 2.00–5.00 3.00 − .506 − .111 The first step of the hierarchical multiple regression model to examine the predictors of GAAIS Positive (Table 3 ) explained 7.8% of the variance. Among these sociodemographic control variables, Sex emerged as a significant predictor, indicating that males reported higher levels of positive attitudes towards AI compared to females. Step 2 resulted in a significant model improvement, accounting for an additional 4.5% of the variance and increasing the total explained variance to 12.3%. In this step, Sex remained a significant predictor, while TIPI-R Openness to experience and SD3 Machiavellianism emerged as unique positive predictors of GAAIS Positive. Step 3, which introduced the cognitive variables related to AI literacy, led to a further significant increase of 12.0% in explained variance. Sex, TIPI-R Openness to experience, and SD3 Machiavellianism maintained their significance. Furthermore, AILS Usage emerged as a significant positive predictor, whereas AILS Ethics made a significant negative contribution to the model. The final step (Step 4) evaluated the incremental contribution of the SDT variables and explained an additional 8.4% of the variance. The complete model accounted for 32.7% of the total variance in GAAIS Positive. In this final model, Sex, TIPI-R Openness to experience, and SD3 Machiavellianism remained significant. Notably, the effect of AILS Usage became non-significant at this stage, while AILS Ethics continued to contribute negatively to the model Among the newly added variables, TENS-Life Autonomy and TENS-Life Relatedness demonstrated unique and statistically significant positive contributions to the prediction of GAAIS Positive. Table 3 Hierarchical regression predicting participants’ positive attitude towards AI GAAIS Positive Step 1 Step 2 Step 3 Step 4 B (p) 95% CI t B (p) 95% CI t B (p) 95% CI t B (p) 95% CI t Sex 1 − .031 (< .001) − .045;-.017 -4.484 − .029 (< .001) − .044;-.014 -3.841 − .021 (.005) − .035;-.006 -2.862 − .021 (.003) − .035;-.007 -3.032 Age .001 (.829) − .006;.007 .217 .002 (.622) − .005;.008 .493 .005 (.128) − .001;.011 1.528 .004 (.202) − .002;.010 1.280 Profession .000 (.961) − .020;.019 .961 − .002 (.836) − .021;.017 − .208 − .006 (.524) − .024;.012 − .639 − .002 (.821) − .019;.015 − .226 TIPI-R Extraversion − .002 (.415) − .007;.003 − .816 − .002 (.480) − .006;.003 − .707 − .002 (.399) − .006;.002 − .845 TIPI-R Agreeableness .003 (.412) − .004;.009 .822 .003 (.343) − .003;.009 .950 .002 (.583) − .004;.007 .550 TIPI-R Conscientiousness − .003 (.276) − .009;.003 -1.094 − .004 (.219) − .009;.002 -1.232 − .005 (.072) − .010;.000 -1.806 TIPI-R Neuroticism .001 (.612) − .004;.007 .508 .001 (.804) − .004;.006 .248 − .001 (.634) − .006;.004 − .477 TIPI-R Openness to experience .010 (.002) .004;.017 3.191 .008 (.013) .002;.014 2.508 .006 (.030) .001;.012 2.187 SD3 Machiavellianism .014 (.036) .001;.027 2.110 .015 (.015) .003;.028 2.458 .014 (.019) .002;.026 2.360 SD3 Narcissism .005 (.500) − .009;.019 .676 − .001 (.874) − .014;.012 − .158 − .004 (.580) − .016;.009 − .554 SD3 Psychopathy − .004 (.441) − .015;.007 − .771 − .005 (.301) − .015;.005 -1.037 − .003 (.481) − .013;.006 − .706 CMQ .000 (.986) − .006;.006 − .018 .000 (.953) − .006;.006 .060 .002 (.534) − .004;.007 .624 AILS Awareness .004 (.441) − .007;.016 .773 .007 (.198) − .004;.018 1.291 AILS Usage .014 (.011) .003;.024 2.582 .009 (.066) − .001;.019 1.848 AILS Evaluation .010 (.066) − .001;.022 1.849 .007 (.185) − .003;.018 1.332 AILS Ethics − .012 (.007) − .021;-.003 -2.708 − .013 (.002) − .021;-.005) -3.090 TENS Autonomy .003 (.003) .001;.005 3.036 TENS Competence .001 (.644) − .002;.003 .463 TENS Relatedness .005 (<. 001) .003;.007 4.751 R 2 (ΔR 2 ) .078 (.078) .123 (.045) .243 (.120) .327 (.084) F (p) 6.866 (< .001) 3.453 (< .001) 5.190 (< .001) 6.349 (< .001) ΔF (p) 6.866 (< .001) 2.196 (.024) 8.768 (< .001) 9.059 (< .001) Notes . 1 Sex coded with “1” = male, “2” = female. The first step of the hierarchical multiple regression model to examine the predictors of the GAAIS Negative score (Table 4 ) accounted for 1.0% of the variance. Unlike the positive attitude model, this initial step was not statistically significant, and none of the sociodemographic variables (Sex, Age, or Profession) emerged as significant predictors. Step 2 resulted in a significant model improvement, accounting for an additional 7.5% of the variance and increasing the total explained variance to 8.5%. At this step, TIPI-R Agreeableness made a significant positive contribution to the score, whereas conspiracy beliefs (CMQ) emerged as a significant negative predictor. Step 3, which introduced the cognitive variables related to AI literacy, led to a further significant increase of 7.5% in explained variance, bringing the cumulative total to 16.0%. TIPI-R Agreeableness and CMQ maintained their significant predictive value. Additionally, AILS Usage emerged as a unique positive predictor of the GAAIS Negative score at this stage. The final step (Step 4) evaluated the incremental contribution of the SDT variables and explained a substantial additional 20.5% of the variance. The complete model accounted for 36.5% of the total variance in the GAAIS Negative score. In this final model, TIPI-R Agreeableness remained a significant positive predictor, and CMQ continued to contribute negatively. Crucially, all three dimensions of daily technology experience – TENS-Life Autonomy, Competence, and Relatedness – demonstrated unique and highly significant positive contributions to the prediction of the score. Notably, the previously significant effect of AILS Usage was fully attenuated and became non-significant after the inclusion of the psychological needs variables. Table 4 Hierarchical regression predicting participants’ negative attitude towards AI GAAIS Negative 1 Step 1 Step 2 Step 3 Step 4 B (p) 95% CI t B (p) 95% CI t B (p) 95% CI t B (p) 95% CI t Sex 2 − .025 (.073) − .051;.002 -1.799 − .021 (.147) − .050;.007 -1.456 − .015 (.302) − .044;.014 -1.034 − .004 (.741) − .029;.021 − .331 Age .007 (.300) − .006;.020 1.039 .004 (.504) − .009;.017 .669 .007 (.257) − .005;.020) 1.137 .006 (.293) − .005;.017 1.055 Profession .007 (.724) − .031;.045) .354 .004 (.844) − .034;.041 .197 − .004 (.832) − .040;.032 − .212 .011 (.489) − .021;.043 .693 TIPI-R Extraversion − .003 (.531) − .012;.006 − .627 − .002 (.679) − .011;.007 − .414 .000 (.997) − .008;.008 − .004 TIPI-R Agreeableness .015 (.020) .002;.027 2.342 .013 (.040) .001;.025 2.071 .015 (.007) .004;.025 2.720 TIPI-R Conscientiousness .000 (.997) − .011;.011 − .004 − .004 (.476) − .015;.007 − .715 − .009 (.093) − .019;.001 -1.690 TIPI-R Neuroticism − .001 (.888) − .011;.009 − .141 − .002 (.668) − .012;.008 − .430 − .004 (.321) − .013;.004 − .995 TIPI-R Openness to experience .002 (.801) − .011;.014 .252 − .002 (.728) − .014;.010 − .348 − .008 (.130) − .019;.002 -1.522 SD3 Machiavellianism .000 (.970) − .025;.026 .038 .001 (.964) − .024;.025 .045 − .003 (.805) − .024;.019 − .248 SD3 Narcissism .003 (.819) − .024;.030 .229 − .002 (.888) − .028;.024 − .141 − .016 (.179) − .039;.007 -1.349 SD3 Psychopathy .004 (.710) − .017;.025 .373 .001 (.950) − .020;.021 .062 .011 (.239) − .007;.028 1.181 CMQ − .024 (< .001) − .036;-.013 -4.112 − .022 (< .001) − .034;-.011 -3.858 − .013 (.010) − .024;-.003 -2.618 AILS Awareness − .007 (.528) − .030;.016 − .632 .006 (.529) − .014;.027 .631 AILS Usage .026 (.013) .005;.047 2.493 .013 (.173) − .006;.031 1.367 AILS Evaluation .020 (.079) − .002;.042 1.763 .010 (.316) − .010;.030 1.005 AILS Ethics .005 (.597) − .013;.022 .530 − .004 (.567) − .020;.011 − .573 TENS Autonomy .008 (< .001) .004;.012 3.985 TENS Competence .011 (< .001) .007;.015 5.221 TENS Relatedness .006 (< .001) .003;.010 3.345 R 2 (ΔR 2 ) .010 (.010) .085 (.075) .160 (.075) .365 (.205) F (p) 1.671 (.174) 2.612 (.003) 3.485 (< .001) 7.326 (< .001) ΔF (p) 1.671 (.174) 2.881 (.003) 5.402 (< .001) 21.800 (< .001) Notes. 1 Higher scores indicate lower negative attitude towards AI, 2 Sex coded with “1” = male, “2” = female. Table 5 presents the differences in the three ROBOTH subscales across sociodemographic groups. No statistically significant differences in ROBOTH scores emerged, except for Age, where the 18–25 group scored significantly lower on the Personal support scale compared to the 26–35 group. From a qualitative standpoint, participants tended to perceive AI as more capable than humans in managing system-level tasks, as reflected by higher average scores on the System management subscale compared to those on the Clinical care and Personal support subscales. Conversely, the lowest average scores were observed for the Personal support scale, suggesting a lower perceived competence of AI in this domain. Table 5 Means, standard deviations, and statistical tests for the ROBOTH across sociodemographic variables ROBOTH Clinical care Personal support System management M (SD) M (SD) M (SD) Biological sex t(231) = 1.347, p = .180, d = .21 t(231) = 1.940, p = .054, d = .30 t(231) = − .067, p = .947, d = .01 Male 3.42 (.74) 2.33 (.92) 3.89 (.75) Female 3.27 (.67) 2.08 (.80) 3.91 (.65) Age (years) F(5,227) = .331, p = .894, ƞ² = .009 F(5,227) = 2.585, p = .027, ƞ² = .063 F(5,227) = .335, p = .891, ƞ² = .009 18–25 3.28 (.69) 1.96 (.75) 3.84 (.70) 26–35 3.30 (.57) 2.40 (.88) 3.97 (.67) 36–45 3.37 (.74) 2.13 (.89) 3.95 (.54) 46–55 3.33 (.66) 2.42 (.81) 3.98 (.70) 56–65 3.52 (.96) 2.39 (1.00) 3.93 (.84) 66–75 3.27 (.83) 2.50 (.91) 4.00 (.72) Profession t(231) = .027, p = .979, d = .004 t(231) = 1.504, p = .134, d = .214 t(231) = 1.549, p = .123, d = .221 Healthcare professional 3.32 (.72) 2.25 (.85) 3.99 (.68) Healthcare student 3.32 (.67) 2.07 (.84) 3.84 (.68) Discussion The present study aimed to investigate a multi-layered psychological model of AI acceptance among healthcare students and professionals, assessing the interplay of sociodemographic, dispositional, cognitive, and motivational predictors. By adopting Self-Determination Theory (SDT) as a unifying framework applied to a novel context, our findings advance a theory-driven understanding of human-machine interaction in healthcare education and practice. This study contributes to theory by demonstrating that need satisfaction operates as a proximal mechanism through which individuals interpret and evaluate emerging technologies such as AI. This extends SDT into the domain of human–AI interaction, suggesting that technology acceptance is not only a function of perceived usefulness, but of the extent to which it supports fundamental psychological needs. Rather than conceptualizing attitudes toward AI as the outcome of isolated predictors, our hierarchical analyses strongly support a model in which distal traits and cognitive baseline variables are ultimately modulated or enriched by proximal everyday experiences of technology. Crucially, the results highlight the central role of basic psychological need satisfaction. Across analyses, the extent to which daily engagement with technology supports users’ autonomy, competence, and relatedness emerged as a robust predictor of both positive and negative attitudes toward AI, providing significant incremental validity even when controlling for personality, conspiracist beliefs, and AI literacy. These findings suggest that the acceptance of intelligent systems is fundamentally shaped by the quality of the psychological experience of technology, specifically, the user’s sense of agency, effectiveness, and social connectedness. Hypothesis 1: Sociodemographic predictors H1a was supported, as male participants reported significantly more positive attitudes toward AI than females. This finding aligns with prior research highlighting a gender gap in openness to technological innovation (Kovačević & Demić, 2024 ). Men often exhibit greater self-efficacy and confidence in digital domains, possibly influenced by sociocultural norms around competence and technology adoption (Tømte & Hatlevik, 2011 ). Moreover, women have been shown to express stronger ethical and privacy-related concerns regarding AI systems, reflecting broader gendered differences in technology perception and risk sensitivity (X. Wang et al., 2024 ). H1b received partial support. Although age did not significantly predict general attitudes toward AI, younger participants (18–25) reported lower perceived AI competence in the Personal support domain than those aged 26–35. This contrasts somewhat with prior studies showing that older adults often express heightened concerns over privacy, ethical issues, and job displacement in the context of AI and automation, and may perceive AI as less intuitive or useful (Chu et al., 2022 ; Grünloh et al., 2022 ). The present finding suggests that younger individuals may harbor greater skepticism about AI’s capacity to emulate human empathy or emotional intelligence. While younger cohorts are typically more willing to adopt AI for practical benefits like efficiency and multitasking, they may simultaneously question its affective reliability, especially in socially sensitive domains (Kyung & Kwon, 2025 ). H1c was not supported. Contrary to expectations, no significant differences were observed between students and professionals in attitudes toward AI or in their evaluations of its comparative competence. This result suggests that professional exposure or higher educational attainment alone may not be sufficient to shape more favorable attitudes, and that psychological factors may play a more critical role. Hypothesis 2: Personality traits, Dark Triad, and conspiracy beliefs H2a received partial support. Openness to experience was a significant positive predictor of favorable attitudes toward AI. This personality trait has consistently been associated with curiosity and adaptability, which facilitate acceptance of emerging innovations such as AI (Kaya et al., 2024 ). While agreeableness did not significantly predict positive attitudes toward AI, it was significantly associated with lower levels of negative attitudes, confirming previous results (Stein et al., 2024 ). This suggests that individuals high in agreeableness – typically characterized by interpersonal trust, kindness, and cooperativeness – may extend this disposition even toward artificial agents (Barnett et al., 2015 ; Schepman & Rodway, 2023 ), even in the absence of direct positive evaluations (Park & Woo, 2022 ). Contrary to the hypothesis, neuroticism – a personality trait characterized by anxiety and a tendency toward negative affect – did not emerge as a significant predictor. While some studies link emotional instability to lower technology acceptance and increased concerns (Svendsen et al., 2013 ), others find no clear association or even mitigating effects of emotional stability (Park & Woo, 2022 ; Schepman & Rodway, 2023 ). In our study, neuroticism’s impact may have been masked by stronger predictors. H2b yielded an unexpected but noteworthy result: Machiavellianism was positively associated with favorable attitudes toward AI. While traits from the Dark Triad are typically associated with distrust or skepticism toward others (Jones & Paulhus, 2014 ; Somma et al., 2020 ), individuals high in Machiavellianism may perceive AI as a strategic and utilitarian tool that can be leveraged to gain control, exert influence over others, or pursue antisocial goals (Laakasuo et al., 2021 ) within complex healthcare or organizational systems. This hypothesis was proposed but not confirmed in a previous study that employed a different measure of positive attitudes toward AI (Stein et al., 2024 ). H2c was fully supported. Higher levels of conspiracy thinking predicted more negative attitudes toward AI, consistent with studies linking conspiratorial ideation to distrust of systems perceived as opaque, data-driven, or potentially manipulative (Stein et al., 2024 ). This result reinforces the need to address epistemic trust when promoting AI integration in healthcare. Hypothesis 3: AI literacy H3 was only partially supported. In the model predicting GAAIS Positive, the Ethics subscale of the AILS had a negative effect, suggesting that individuals with higher ethical awareness may adopt a more cautious and discerning stance toward AI. This result is consistent with definitions of AI ethics as involving the recognition of risks, responsibilities, and broader societal implications associated with intelligent technologies (B. Wang et al., 2023 ), and is in line with systematic reviews highlighting the need to develop physician competencies in AI governance and ethical design to foster greater confidence in AI‑augmented decision‑making (Schuitmaker et al., 2025 ). This finding also aligns with the observed positive association between Machiavellianism and favorable AI attitudes in our study, suggesting that less ethically oriented individuals may view AI as a strategic tool. Rather than signaling outright rejection, this ethical tendency may reflect a conditionally supportive attitude toward AI shaped by a desire for safeguards and transparency. These findings suggest that ethical reflection can temper uncritical acceptance of AI and promote more cautious evaluations, particularly in healthcare settings, where issues of legality, privacy, and the secure management of clinically confidential information are especially salient. Hypothesis 4: SDT-based psychological needs H4 was strongly supported, representing the most robust and innovative finding of the present study. Across both hierarchical models, the inclusion of the three SDT-based psychological needs (Step 4) yielded a highly significant increase in the explained variance, fundamentally altering the predictive landscape. Specifically, the extent to which everyday technology satisfies users' needs for autonomy, competence, and relatedness emerged as the strongest set of predictors for AI acceptance. Regarding positive attitudes (GAAIS Positive), Autonomy and Relatedness made unique positive contributions. This aligns with SDT’s proposition that individuals are more likely to enthusiastically engage with and value technological systems when they feel agentic and socially connected during their daily digital interactions (Bergdahl et al., 2023 ; Moradbakhti et al., 2022 ). The impact of psychological needs was even more striking in predicting negative attitudes and AI aversion (GAAIS Negative). All three basic needs – Autonomy, Competence, and Relatedness – significantly mitigated negative attitudes. This suggests a powerful “protective effect” of psychological need satisfaction: healthcare professionals and students who feel effective (competence), in control of their actions (autonomy), and meaningfully connected to others (relatedness) while using technology are significantly less likely to experience fear, suspicion, or alienation toward AI. This supports recent literature emphasizing that technology-induced anxiety is often rooted in the perceived threat to human agency and professional identity, which need-supportive environments can actively buffer (Peters et al., 2018 ; Sahin & Sahin, 2022 ). In the specific context of health professions education and clinical practice, these findings have profound implications. They suggest that to build acceptance for intelligent tools, institutions must move beyond simply training users on “how to click”. Instead, the design and implementation of AI systems must actively safeguard clinicians’ sense of professional identity. Fostering these psychological dimensions is key to integrating AI as a supportive partner that enhances, rather than erodes, medical autonomy and the deeply interpersonal nature of healthcare. Hypothesis 5: Perceived comparative competence of AI Regarding perceptions of AI’s comparative competence measured with the ROBOTH scale, participants tended to view AI as most capable in System management tasks, followed by Clinical care, and least competent in Personal support. This pattern reflects current limitations in affective computing and the persistent inability of AI systems to emulate empathy, relational nuance, or emotional sensitivity, domains where human capabilities remain unmatched (Guo et al., 2025 ). The only significant sociodemographic difference on ROBOTH subscales was for age, with younger participants perceiving AI as less competent in the personal/emotional domain. These perception patterns suggest that attitudes toward AI vary by functional domain, with greater skepticism typically emerging when tasks involve empathy or interpersonal engagement. As Aly et al. ( 2024 ) reported, users are more reluctant to trust AI in emotionally laden or educational roles, underscoring the need to distinguish between cognitive and affective competence in AI design and evaluation. Strengths and limitations Despite these promising findings, the present study has certain limitations that should be acknowledged. First, the cross-sectional design precludes the establishment of causal relationships among dispositional traits, AI literacy, psychological need satisfaction, and attitudes toward AI. Future longitudinal or experimental research is required to ascertain whether enhancing technology-related need satisfaction actively drives shifts in AI acceptance over time. Second, the reliance on self-report instruments introduces the potential for common method variance and social desirability bias, although the strict anonymity of the survey protocol helps mitigate these risks. Third, while our sample included both students and practitioners, it was predominantly skewed toward the healthcare student population. Although our regression models (Step 1) did not identify professional status as a significant predictor of attitudes, further validation in larger, more balanced cohorts of experienced clinicians is warranted to fully establish the broader generalizability of these findings. Despite these limitations, this study possesses notable theoretical and methodological strengths. To our knowledge, it is one of the first investigations to apply a unifying theoretical framework (SDT) to disentangle the multi-layered predictors of AI acceptance in health professions education. The rigorous hierarchical analytical approach allowed for the precise isolation of specific dispositional, cognitive, and motivational drivers, advancing the field beyond mere descriptive or sociodemographic analyses of technology adoption. Furthermore, the integration of multidimensional instruments—such as the ROBOTH scale to assess comparative competence and the TENS-Life to capture daily psychological experiences—yielded a highly granular understanding of human-machine interaction. Collectively, these insights offer a robust, evidence-based foundation for designing future educational interventions to optimize the integration of intelligent systems into clinical training and practice. Conclusions and educational implications In conclusion, the present study demonstrates that acceptance of artificial intelligence among healthcare students and professionals is a multifaceted process, driven not merely by cognitive or sociodemographic variables but primarily by the fulfillment of basic psychological needs in everyday interactions with technology. While dispositional traits and epistemic beliefs set a baseline orientation toward innovation, the proximal experience of autonomy, competence, and relatedness serves as the most robust safeguard against AI aversion and the strongest driver of positive attitudes. Furthermore, our findings highlight a crucial nuance regarding AI literacy: while technical proficiency does not automatically translate into enthusiasm, ethical awareness fosters a necessary, responsible caution. Ultimately, the successful integration of AI into healthcare education and clinical practice will depend less on the sheer volume of technological exposure, and significantly more on designing operational environments that empower practitioners to preserve their professional agency and relational identity in the face of increasingly automated systems. AI curricula should incorporate participatory learning approaches that enable students to critically evaluate and co-design AI use (supporting autonomy), simulation-based training to build applied competence, and reflective exercises that address the impact of AI on patient relationships (supporting relatedness). Declarations AUTHOR CONTRIBUTIONS: M.B., S.R. and G.M.L.S. developed the research aims, conceptualizazion and methology. M.B. and S.A. wrote, reviewed and edited the manuscript. M.B., N.C., S.R., G.M.L.S. contributed to data collection. S.A. contributed to data analysis and wrote the results section of the manuscript. S.R., F.Z., M.G.S. and G.M.L.S. contributed to study design and manuscript review and editing. All authors read and approved the final manuscript. CONFLICT OF INTEREST STATEMENT: None. DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS: During the preparation of this work, the authors used Grammarly in order to improve readability. After using this tool, the authors reviewed and edited the content as necessary and took full responsibility for the publication's content. DATA AVAILABILITY STATEMENT: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. ETHICS STATEMENT: The study was approved by the Research Evaluation Committee, Department of Psychology, University of Milano-Bicocca (Protocol No. RM-2024-773). All participants in this study gave informed consent to participate. References Aly, H., Byrne, K. A., & Knijnenburg, B. (2024). 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Nursing Students’ Personality Traits and Their Attitude toward Artificial Intelligence: A Multicenter Cross-Sectional Study. Journal of Nursing Management , 2024 (1). https://doi.org/10.1155/2024/6992824 Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports , 1 , 100014. https://doi.org/10.1016/j.chbr.2020.100014 Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust. International Journal of Human–Computer Interaction , 39 (13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400 Schuitmaker, L., Drogt, J., Benders, M., & Jongsma, K. (2025). Physicians’ required competencies in AI-assisted clinical settings: A systematic review. British Medical Bulletin , 153 (1). https://doi.org/10.1093/bmb/ldae025 Somma, A., Paulhus, D. L., Borroni, S., & Fossati, A. (2020). Evaluating the Psychometric Properties of the Short Dark Triad (SD3) in Italian Adults and Adolescents. European Journal of Psychological Assessment , 36 (1), 185–195. https://doi.org/10.1027/1015-5759/a000499 Stein, J. P., Messingschlager, T., Gnambs, T., Hutmacher, F., & Appel, M. (2024). Attitudes towards AI: Measurement and associations with personality. Scientific Reports , 14 (1). https://doi.org/10.1038/s41598-024-53335-2 Svendsen, G. B., Johnsen, J. A. K., Almås-Sørensen, L., & Vittersø, J. (2013). Personality and technology acceptance: The influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour & Information Technology , 32 (4), 323–334. https://doi.org/10.1080/0144929x.2011.553740 Tømte, C., & Hatlevik, O. E. (2011). Gender-differences in Self-efficacy ICT related to various ICT-user profiles in Finland and Norway. How do self-efficacy, gender and ICT-user profiles relate to findings from PISA 2006. Computers & Education , 57 (1), 1416–1424. https://doi.org/10.1016/j.compedu.2010.12.011 Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology , 42 (9), 1324–1337. https://doi.org/10.1080/0144929x.2022.2072768 Wang, X., Fei, F., Wei, J., Huang, M., Xiang, F., Tu, J., Wang, Y., & Gan, J. (2024). Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: A cross-sectional study. Frontiers in Public Health , 12 . https://doi.org/10.3389/fpubh.2024.1433252 Wissing, B. G., & Reinhard, M. A. (2018). Individual Differences in Risk Perception of Artificial Intelligence. Swiss Journal of Psychology , 77 (4), 149–157. https://doi.org/10.1024/1421-0185/a000214 Younis, H. A., Eisa, T. A. E., Nasser, M., Sahib, T. M., Noor, A. A., Alyasiri, O. M., Salisu, S., Hayder, I. M., & Younis, H. A. (2024). A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges. Diagnostics , 14 (1), 109. https://doi.org/10.3390/diagnostics14010109 Zabel, S., Pensini, P., & Otto, S. (2025). Unveiling the role of honesty-humility in shaping attitudes towards artificial intelligence. Personality and Individual Differences , 238 , 113072. https://doi.org/10.1016/j.paid.2025.113072 Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 28 Mar, 2026 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-9254457","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624163970,"identity":"f666252e-0a4c-49c9-89e4-b4c1a59c169e","order_by":0,"name":"Stefano Ardenghi","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Ardenghi","suffix":""},{"id":624163971,"identity":"1cb098b6-6717-4941-b824-cace250de08c","order_by":1,"name":"Marco Bani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYPCCAwwMzIyNDxgYJBjYJMBcorQwNxvAtRChB6SEvU0CzJZgwG+NbvvZhx8+MNyRN2dnbKvm3WERzSfdwHj4Ax4tZmfSjSVnMDwz3NnM2Hab94xEbpvMAfwOMzuQxsbMw3CYccNhkJY2oBaJBAJazj8Da7EHaSkmTssNiC2JIC3MRGp5xiw5w+BZMlBLs+RckBaZgw0HzuB1WBrjhw8Vd2w3nD/+8MPbtrrc+bObD3+owKMFAgxQeIwNBDWMglEwCkbBKMAPAD+EUhS335zOAAAAAElFTkSuQmCC","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":true,"prefix":"","firstName":"Marco","middleName":"","lastName":"Bani","suffix":""},{"id":624163972,"identity":"1de58d65-db25-4dd1-9e88-ebe42cc002f5","order_by":2,"name":"Selena Russo","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Selena","middleName":"","lastName":"Russo","suffix":""},{"id":624163973,"identity":"6a9beed6-f2c6-4261-9a55-f658cf771753","order_by":3,"name":"Niccolò Cremona","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Niccolò","middleName":"","lastName":"Cremona","suffix":""},{"id":624163974,"identity":"d98d1339-7f14-49f4-b733-d62529ced57c","order_by":4,"name":"Federico Zorzi","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Zorzi","suffix":""},{"id":624163975,"identity":"34357020-cb87-467a-9df6-9d29fed26da4","order_by":5,"name":"Giuseppe Maria Luigi Sarnè","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"Maria Luigi","lastName":"Sarnè","suffix":""},{"id":624163976,"identity":"09e5a846-216a-4db4-8ff6-5193dd4b69b3","order_by":6,"name":"Maria Grazia Strepparava","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Grazia","lastName":"Strepparava","suffix":""}],"badges":[],"createdAt":"2026-03-28 17:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9254457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9254457/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705429,"identity":"367c3ca4-140e-4b1b-950a-8877d16c0e19","added_by":"auto","created_at":"2026-04-24 09:12:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":843973,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9254457/v1/657f5194-e569-416f-bac6-63209f3e1712.pdf"},{"id":107565409,"identity":"1e7c65c7-0820-4e18-9a85-51e8d4c951f9","added_by":"auto","created_at":"2026-04-22 16:46:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19297,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9254457/v1/b0c2f68813d7b7eb1c0309a0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFrom Everyday Technology Experience to AI Acceptance in Healthcare Education: A Self-determination Theory–based Model\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is transforming the healthcare sector by providing advanced solutions for early diagnosis (K. Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), clinical data management (Gupta \u0026amp; Kumar, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and therapeutic decision support (Karuppan Perumal et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). AI-powered analytics are crucial to advancing precision medicine by enabling personalized treatments based on individual patient profiles (Rishi Reddy Kothinti, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These developments have the potential to significantly improve both the effectiveness and overall quality of healthcare services. The integration of AI into healthcare settings presents significant challenges related to the education and training of professionals (Love et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Masoumian Hosseini et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Younis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Today\u0026rsquo;s healthcare students and professionals are not only required to operate advanced technological tools but also cultivate a critical understanding of the capabilities, limitations, and ethical considerations associated with intelligent technologies (Goankar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, health professions education is facing the challenge of preparing both students and practitioners to engage with AI in informed and responsible ways. However, the successful integration of AI into clinical practice relies not only on the availability of technology but also on the subjective perceptions healthcare professionals develop towards these tools, particularly in relation to how they assess AI\u0026rsquo;s capabilities in comparison to human clinicians (Schepman \u0026amp; Rodway, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical studies show that AI tends to be perceived as more competent than humans in data-intensive, structured, and analytical tasks \u0026ndash; such as diagnostics, risk stratification, or resource management \u0026ndash; where it can outperform humans in accuracy and speed (Asan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kaplan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, AI is often seen as less effective in domains that require emotional intelligence, ethical sensitivity, and relational competence, such as psychological support or shared decision-making (Longoni et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Montemayor et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These evaluations are shaped by a range of individual-level variables that play a key role in influencing the acceptance or rejection of digital innovations. Among sociodemographic variables, several predictors have consistently emerged in shaping a more positive attitude toward AI, such as being male, younger, and having higher academic qualifications (Kozak \u0026amp; Fel, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing interest in attitudes toward AI in healthcare, existing research has been largely descriptive, focusing on sociodemographic differences or isolated psychological predictors of acceptance. While these studies provide useful insights, they often lack a coherent theoretical framework to explain why individuals develop favorable or unfavorable attitudes toward AI. Furthermore, most studies fail to distinguish between the effects of stable, distal individual traits (such as personality) and more proximal, experiential factors. As a result, current evidence remains fragmented, limiting its contribution to the design of educational interventions to foster meaningful and critical engagement with AI technologies (Habib et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor instance, dispositional traits, such as personality dimensions (Ozbey \u0026amp; Yasa, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Salem et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zabel et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and socially aversive characteristics (e.g., Dark Triad traits; Wissing \u0026amp; Reinhard, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), may influence openness to innovation, interpersonal trust, and strategic orientations toward technology. Similarly, conspiracy beliefs (Stein et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) may reflect broader tendencies toward epistemic distrust, which can negatively affect perceptions of complex and opaque systems such as AI. Finally, AI literacy represents a cognitive dimension that may enable individuals to better understand and critically appraise AI applications, while also increasing awareness of their ethical implications (Asio \u0026amp; Gadia, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Laupichler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, rather than acting as independent and isolated predictors, these factors can be conceptualized as contributing to a multi-layered psychological model in which proximal motivational processes remain central.\u003c/p\u003e \u003cp\u003eTo address this limitation, the present study draws on Self-Determination Theory (SDT; Deci \u0026amp; Ryan, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) to propose a multi-layered psychological model of AI acceptance in healthcare. According to SDT, individuals thrive when three fundamental psychological needs are met: autonomy (the sense of volition and agency), competence (the feeling of effectiveness and capability), and relatedness (the experience of meaningful connection). When these core needs are fulfilled, people are more inclined to participate in activities that they find personally valuable and intrinsically rewarding. Rather than assessing these needs in the abstract, we argue that the everyday technological ecosystem acts as a crucial bridge to AI acceptance. Recent research underscores the relevance of SDT in shaping attitudes toward AI and technology (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Individuals who perceive greater autonomy, competence, and relatedness in their technological interactions tend to report more favorable attitudes, higher perceived usefulness, and greater engagement with intelligent systems (Bergdahl et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we adopt a hierarchical approach to conceptualize how motivational, dispositional, and cognitive processes interplay. We posit that dispositional traits (such as personality dimensions) and epistemic attitudes (like conspiracy beliefs) act as distal modulators, setting a baseline orientation toward innovation and trust. Similarly, AI literacy represents a cognitive baseline that enables critical appraisal. However, we hypothesize that the proximal experience of satisfaction of the basic psychological needs for autonomy, competence, and relatedness in everyday interactions with technology will provide unique, incremental predictive value over and above these traits and cognitive characteristics. By integrating SDT with individual difference variables, this study seeks to contribute to a more conceptually coherent understanding of AI acceptance in health professions education.\u003c/p\u003e \u003cp\u003eWithin this framework, we aim to (1) test a theoretically grounded, hierarchical model of general attitudes toward AI among healthcare students and professionals, isolating the specific contribution of SDT variables, and (2) explore how AI is perceived in terms of its comparative competence across clinically relevant domains (system-level, clinical, and relational tasks). Based on prior research and relevant theoretical frameworks, we hypothesize that:\u003c/p\u003e \u003cp\u003eH1: Sociodemographic factors will be significantly associated with attitudes toward AI.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eH1a: Males will report more positive attitudes toward AI compared to female participants.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH1b: Younger individuals will exhibit more favorable attitudes toward AI.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH1c: Healthcare professionals (as a proxy for higher educational attainment) will report more positive attitudes toward AI compared to students.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eH2: Personality dimensions are expected to influence AI-related attitudes.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eH2a: Higher levels of agreeableness and openness to experience, and lower levels of neuroticism, will be associated with more positive attitudes toward AI.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH2b: Elevated scores on Dark Triad traits will correlate with more negative attitudes toward AI.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH2c: Higher endorsement of conspiracy beliefs will predict greater aversion and distrust toward AI systems.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eH3: Higher levels of AI literacy will be positively associated with acceptance of AI.\u003c/p\u003e \u003cp\u003eH4: The extent to which daily technology use supports users' basic psychological needs (autonomy, competence, and relatedness) will positively predict optimistic attitudes toward AI and significantly mitigate negative concerns and mistrust.\u003c/p\u003e \u003cp\u003eH5: AI will be perceived as more competent than human healthcare professionals in system-level tasks, but less skilled in domains requiring psychological support.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional survey design. Participants were recruited voluntarily from university programs, professional associations, and social media groups for healthcare professionals/students in Italy between December 2024 and May 2025. The target population included healthcare professionals (physicians, nurses, other allied health professionals) and students enrolled in medicine, nursing, and related health science programs in Italy. Data were collected using an anonymous online questionnaire administered via Qualtrics. An introductory page explained the study\u0026rsquo;s purpose, assured confidentiality and anonymity, outlined the voluntary nature of participation, and contained the informed consent form. Participants indicated their consent before proceeding to the questionnaire, which took approximately 20 minutes to complete. The study received approval from the Research Evaluation Committee of the Department of Psychology of the University of Milano-Bicocca (protocol number: RM-2024-905) and was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMaterials\u003c/h3\u003e\n\u003cp\u003eTo assess participants\u0026rsquo; general attitudes toward AI, the Italian translation of the General Attitudes Towards AI Scale (GAAIS; Schepman \u0026amp; Rodway, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sacco et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) was employed. The scale consists of 20 items and is composed of 2 subscales: the Positive subscale includes 12 items that capture perceived benefits, interest, and optimism regarding AI (e.g., \u0026ldquo;I am interested in using artificially intelligent systems in my daily life\u0026rdquo;), while the Negative subscale includes 8 items, assessing concerns, discomfort, and mistrust toward AI (e.g., \u0026ldquo;I think Artificial Intelligence is dangerous\u0026rdquo;). Responses are rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating more positive (or less negative, depending on the subscale) attitudes toward AI.\u003c/p\u003e \u003cp\u003eTo assess personality traits, the Italian validation of the revised Ten Item Personality Inventory (TIPI-R; Chiorri et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gosling et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), a brief measure of the Big Five personality dimensions, was used. The TIPI-R evaluates 5 core personality traits: Extraversion (the tendency to experience positive emotions, be energetic, decisive, sociable, and talkative), Agreeableness (the tendency to be compassionate, cooperative, and well-disposed toward others), Conscientiousness (the tendency to be organized, reliable, self-disciplined, and task-focused), Neuroticism (the tendency to experience negative emotional states such as anger, anxiety, or sadness), and Openness to Experience (the tendency to be curious, imaginative, and appreciative of art, adventure, and novelty). The scale consists of 10 items, with 2 items for each personality trait. Responses are given on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with higher mean scores indicating a stronger presence of the corresponding personality trait.\u003c/p\u003e \u003cp\u003eTo assess socially aversive personality traits, we administered the Italian validated version of the Short Dark Triad (SD3; Jones \u0026amp; Paulhus, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Somma et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The SD3 is a 27-item self-report questionnaire specifically developed to measure the 3 core components of the so-called \u0026ldquo;Dark Triad\u0026rdquo;: Narcissism, Machiavellianism, and Psychopathy. The Machiavellianism subscale (e.g., \u0026ldquo;Make sure your plans benefit yourself, not others\u0026rdquo;) assesses manipulative interpersonal strategies, strategic planning, and a cynical view of human nature. The Narcissism subscale (e.g., \u0026ldquo;Many group activities tend to be dull without me\u0026rdquo;) captures traits such as grandiosity, entitlement, and a need for admiration. The Psychopathy subscale (e.g., \u0026ldquo;People who mess with me always regret it\u0026rdquo;) reflects impulsivity, callousness, and a lack of empathy or remorse. Each subscale consists of nine items, and responses are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating a stronger presence of the corresponding personality trait.\u003c/p\u003e \u003cp\u003eTo assess general conspiratorial thinking, the Italian validated version of the Conspiracy Mentality Questionnaire (CMQ; Bruder et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Candini et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was used. The CMQ is a brief, unidimensional measure consisting of 5 items (e.g., \u0026ldquo;I think that government agencies closely monitor all citizens\u0026rdquo;) designed to capture generic beliefs in conspiracy theories, independent of specific events or contexts. Participants respond using a 7-point Likert scale, ranging from 1 (certainly not true) to 7 (certainly true), with higher mean scores indicating a stronger conspiracy mentality.\u003c/p\u003e \u003cp\u003eTo assess participants\u0026rsquo; level of AI literacy, the Italian translation of the Artificial Intelligence Literacy Scale (AILS; B. Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was administered. It evaluates 4 core dimensions of AI literacy, each consisting of 3 items: Awareness (e.g., \u0026ldquo;I can distinguish between smart devices and non-smart devices\u0026rdquo;), Usage (e.g., \u0026ldquo;I can skillfully use AI applications or products to help me with my daily work\u0026rdquo;), Evaluation (e.g., \u0026ldquo;I can evaluate the capabilities and limitations of an AI application or product after using it for a while\u0026rdquo;), and Ethics (e.g., \u0026ldquo;I am never alert to privacy and information security issues when using AI applications or products\u0026rdquo; \u0026ndash; reverse scored). The AILS consists of 12 self-report items presented as statements describing behaviors, abilities, or attitudes toward AI-related tasks. Respondents indicate their level of agreement on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating higher levels of AI literacy across the measured dimensions.\u003c/p\u003e \u003cp\u003eTo assess the perceived impact of technology on the satisfaction of basic psychological needs in daily life, the Italian translation of the TENS-Life scale was adopted. It was originally introduced by Peters et al. (Peters et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and conceptually grounded in the Basic Psychological Need Satisfaction and Frustration Scale (B. Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The scale measures the extent to which the use of technology supports or undermines the satisfaction of the 3 basic psychological needs defined by Self-Determination Theory (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1985\u003c/span\u003e): Autonomy (e.g., \u0026ldquo;The new technologies intrude in my life\u0026rdquo; \u0026ndash; reverse scored), Competence (e.g., \u0026ldquo;Using the new technologies has made me feel insecure about my abilities\u0026rdquo; \u0026ndash; reverse scored), and Relatedness (e.g., \u0026ldquo;Because of these new technologies, I feel closer to some others\u0026rdquo;). It consists of 9 items, with 3 items for each subscale. Responses are collected using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher mean scores indicating greater satisfaction of basic psychological needs in relation to technology use in everyday life.\u003c/p\u003e \u003cp\u003eTo investigate the perceived capability of AI to perform specific healthcare tasks compared to humans, ChatGPT was used to generate a preliminary list of key AI applications in the sector. Leveraging its capacity to synthesize large volumes of information, this tool offered a valuable foundation for identifying and organizing intervention domains. The list was then validated through expert consultation to ensure relevance and accuracy. The resulting set of 14 items extends the work of Schepman and Rodway (Schepman \u0026amp; Rodway, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), focusing specifically on healthcare-related domains. Each item is rated on a 5-point Likert scale ranging from 1 (AI much less capable than humans) to 5 (AI much more capable than humans), with higher mean scores indicating that respondents perceive AI as more capable than humans in performing specific healthcare tasks. An exploratory factor analysis (EFA) was conducted to identify the underlying latent structure of the scale. A Kaiser-Meyer-Olkin value of .848 and a significant Bartlett\u0026rsquo;s test of sphericity (p \u0026lt; .001) confirmed the suitability of the data for factor analysis. The EFA was performed using Principal Component Analysis as the extraction method with Direct Oblimin rotation to allow for factor correlation. The scree plot and the number of eigenvalues greater than 1.00 both suggested retaining a three-factor solution, which accounted for 53.71% of the total cumulative variance. The 3 factors were subsequently interpreted and labeled as follows: Clinical care (8 items; e.g. \u0026ldquo;AI algorithms can analyze individual patient characteristics \u0026ndash; genetic, clinical, and environmental \u0026ndash; and suggest optimal treatment plans for each specific case\u0026rdquo;), Personal support (2 items; e.g., \u0026ldquo;AI-based tools can assist patients with psychological disorders through automated emotional support, online cognitive-behavioral therapy, or monitoring changes in behavior\u0026rdquo;), and System management (4 items; e.g., \u0026ldquo;AI can streamline and automate administrative tasks such as managing electronic health records, scheduling appointments, and processing invoices\u0026rdquo;). In light of the three identified factors and the content of the items, the questionnaire was named ROBOTH (Representations Of Benefits Of Technology in Healthcare), reflecting its focus on perceived advantages of AI across different domains of healthcare practice (Table S1).\u003c/p\u003e \n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics (Version 29.0.2.0). Initially, descriptive analyses were performed to explore the distribution of all variables. These included the calculation of frequencies, percentages, means, standard deviations, minimum, maximum, range, as well as indices of skewness and kurtosis to assess the assumption of normality. Given the approximate normal distribution of the data, we proceeded with parametric inferential analyses. To investigate the predictors of positive and negative attitudes toward AI (GAAIS) and test Hypotheses 1 to 4, we conducted two hierarchical linear regression analyses. Predictors were entered in four sequential steps reflecting both methodological and theoretical considerations. At Step 1, sociodemographic variables were entered (biological sex, age, and profession) as control variables; at Step 2, personality-related variables (specifically the dimensions from the TIPI-R, the SD3, and the CMQ) were entered to account for relatively stable individual differences; at Step 3, cognitive variables (AI literacy), were added to capture knowledge- and belief-based influences on attitudes toward AI. In Step 4, SDT variables (TENS-Life) were entered to assess their incremental contribution beyond sociodemographic, dispositional, and cognitive factors. For each step, we reported unstandardized regression coefficients (B), p-values (p), R-squared (R\u0026sup2;), change in adjusted R-squared (ΔR\u0026sup2;), and F-change statistics to assess model improvement across steps. In addition, independent-samples t-tests and one-way analyses of variance (with Tukey \u003cem\u003epost hoc\u003c/em\u003e correction) were used to explore group differences on the ROBOTH scale across levels of sociodemographic variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e336 participants accessed the survey; however, 9 subjects did not give informed consent and could not proceed with the survey. 20 subjects were excluded due to failure to answer one of the control items. Control items were embedded in some questionnaires to detect participants responding randomly (e.g., \u0026ldquo;This is a control item; answer 'never' to confirm that you have read it\u0026rdquo;). Finally, 74 subjects were excluded due to mostly incomplete answers. Therefore, the final sample consisted of 233 participants, all of whom were Italian. The majority of participants were female, between 18 and 25 years old, and enrolled in healthcare-related degree programs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics (N\u0026thinsp;=\u0026thinsp;233)\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiological sex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\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\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.5\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics for all study measures. All variables met the criteria for normality, with skewness and kurtosis values falling within the acceptable range.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for study measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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=\"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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAAIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emin-Max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.36 (3.312;3.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20\u0026ndash;4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.04 (2.976;3.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\u0026ndash;4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.07 (2.866;3.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026ndash;6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.15 (5.005;5.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.00\u0026ndash;7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.23 (5.066;5.391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026ndash;7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.59 (3.411;3.780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026ndash;7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness to experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.69 (4.552;4.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50-7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMachiavellianism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.79 (2.709;2.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44\u0026ndash;4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarcissism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.73 (2.653;2.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44\u0026ndash;4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.09 (2.986;3.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50\u0026ndash;4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36 (4.214;4.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20\u0026ndash;6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.33 (3.238;3.419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.39 (3.279;3.496)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.34 (3.247;3.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.51 (3.403;3.607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENS-Life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.70 (3.567;3.833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompetence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86 (3.733;3.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.57 (2.430;2.703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00-4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROBOTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.32 (3.219;3.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.15 (2.036;2.271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.90 (3.808;3.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.00\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.111\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 first step of the hierarchical multiple regression model to examine the predictors of GAAIS Positive (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) explained 7.8% of the variance. Among these sociodemographic control variables, Sex emerged as a significant predictor, indicating that males reported higher levels of positive attitudes towards AI compared to females. Step 2 resulted in a significant model improvement, accounting for an additional 4.5% of the variance and increasing the total explained variance to 12.3%. In this step, Sex remained a significant predictor, while TIPI-R Openness to experience and SD3 Machiavellianism emerged as unique positive predictors of GAAIS Positive. Step 3, which introduced the cognitive variables related to AI literacy, led to a further significant increase of 12.0% in explained variance. Sex, TIPI-R Openness to experience, and SD3 Machiavellianism maintained their significance. Furthermore, AILS Usage emerged as a significant positive predictor, whereas AILS Ethics made a significant negative contribution to the model. The final step (Step 4) evaluated the incremental contribution of the SDT variables and explained an additional 8.4% of the variance. The complete model accounted for 32.7% of the total variance in GAAIS Positive. In this final model, Sex, TIPI-R Openness to experience, and SD3 Machiavellianism remained significant. Notably, the effect of AILS Usage became non-significant at this stage, while AILS Ethics continued to contribute negatively to the model Among the newly added variables, TENS-Life Autonomy and TENS-Life Relatedness demonstrated unique and statistically significant positive contributions to the prediction of GAAIS Positive.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHierarchical regression predicting participants\u0026rsquo; positive attitude towards AI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\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=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003eGAAIS Positive\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eStep 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eStep 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eStep 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eStep 4\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\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.031 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.045;-.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.029 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.044;-.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.841\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e\u0026minus;\u0026thinsp;.002;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000 (.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.020;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.021;.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006 (.524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019;.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Extraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.007;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Agreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003 (.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.003 (.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.002 (.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Conscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003 (.276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005 (.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.010;.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Neuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001 (.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.001 (.804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001 (.634)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Openness to experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010 (.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.004;.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.008 (.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.002;.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.006 (.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.001;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD3 Machiavellianism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.014 (.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001;.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.015 (.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.003;.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.014 (.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.002;.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD3 Narcissism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.005 (.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001 (.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.014;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.016;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD3 Psychopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005 (.301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015;.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003 (.481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000 (.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000 (.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.002 (.534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.004 (.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.007;.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.007 (.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004;.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.014 (.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.003;.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.009 (.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.010 (.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001;.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.007 (.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003;.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.012 (.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.021;-.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013 (.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.021;-.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENS Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.003 (.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.001;.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENS Competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.001 (.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENS Relatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.005 (\u0026lt;. 001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.003;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e (ΔR\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e.078 (.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.123 (.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e.243 (.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e.327 (.084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e6.866 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3.453 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e5.190 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e6.349 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔF (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e6.866 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.196 (.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e8.768 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e9.059 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNotes\u003c/em\u003e. \u003csup\u003e1\u003c/sup\u003e Sex coded with \u0026ldquo;1\u0026rdquo; = male, \u0026ldquo;2\u0026rdquo; = female.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first step of the hierarchical multiple regression model to examine the predictors of the GAAIS Negative score (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) accounted for 1.0% of the variance. Unlike the positive attitude model, this initial step was not statistically significant, and none of the sociodemographic variables (Sex, Age, or Profession) emerged as significant predictors. Step 2 resulted in a significant model improvement, accounting for an additional 7.5% of the variance and increasing the total explained variance to 8.5%. At this step, TIPI-R Agreeableness made a significant positive contribution to the score, whereas conspiracy beliefs (CMQ) emerged as a significant negative predictor. Step 3, which introduced the cognitive variables related to AI literacy, led to a further significant increase of 7.5% in explained variance, bringing the cumulative total to 16.0%. TIPI-R Agreeableness and CMQ maintained their significant predictive value. Additionally, AILS Usage emerged as a unique positive predictor of the GAAIS Negative score at this stage. The final step (Step 4) evaluated the incremental contribution of the SDT variables and explained a substantial additional 20.5% of the variance. The complete model accounted for 36.5% of the total variance in the GAAIS Negative score. In this final model, TIPI-R Agreeableness remained a significant positive predictor, and CMQ continued to contribute negatively. Crucially, all three dimensions of daily technology experience \u0026ndash; TENS-Life Autonomy, Competence, and Relatedness \u0026ndash; demonstrated unique and highly significant positive contributions to the prediction of the score. Notably, the previously significant effect of AILS Usage was fully attenuated and became non-significant after the inclusion of the psychological needs variables.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHierarchical regression predicting participants\u0026rsquo; negative attitude towards AI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\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=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003eGAAIS Negative\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eStep 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eStep 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eStep 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eStep 4\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\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eB (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.025 (.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.051;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.021 (.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.050;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015 (.302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.044;.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.029;.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.007 (.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006;.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.004 (.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009;.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.007 (.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005;.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.006 (.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.005;.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.007 (.724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.031;.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.004 (.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.034;.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.040;.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.011 (.489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.021;.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Extraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003 (.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.012;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.000 (.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.008;.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Agreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.015 (.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.002;.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.013 (.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.001;.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.015 (.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.004;.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Conscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000 (.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011;.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009 (.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Neuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001 (.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.012;.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPI-R Openness to experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002 (.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011;.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.014;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.008 (.130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD3 Machiavellianism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000 (.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.025;.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.001 (.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024;.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003 (.805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD3 Narcissism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003 (.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024;.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002 (.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.028;.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.016 (.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.039;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD3 Psychopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.004 (.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.017;.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.001 (.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.020;.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.011 (.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.007;.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.036;-.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.022 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.034;-.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013 (.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024;-.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-2.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.007 (.528)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.030;.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.006 (.529)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.014;.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.026 (.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.005;.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.013 (.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006;.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.020 (.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002;.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.010 (.316)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.010;.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAILS Ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.005 (.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013;.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004 (.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.020;.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENS Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.008 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.004;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENS Competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.011 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.007;.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTENS Relatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.006 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.003;.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e (ΔR\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e.010 (.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.085 (.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e.160 (.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e.365 (.205)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.671 (.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.612 (.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e3.485 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e7.326 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔF (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.671 (.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.881 (.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e5.402 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e21.800 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNotes. \u003csup\u003e1\u003c/sup\u003eHigher scores indicate lower negative attitude towards AI, \u003csup\u003e2\u003c/sup\u003e Sex coded with \u0026ldquo;1\u0026rdquo; = male, \u0026ldquo;2\u0026rdquo; = female.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the differences in the three ROBOTH subscales across sociodemographic groups. No statistically significant differences in ROBOTH scores emerged, except for Age, where the 18\u0026ndash;25 group scored significantly lower on the Personal support scale compared to the 26\u0026ndash;35 group. From a qualitative standpoint, participants tended to perceive AI as more capable than humans in managing system-level tasks, as reflected by higher average scores on the System management subscale compared to those on the Clinical care and Personal support subscales. Conversely, the lowest average scores were observed for the Personal support scale, suggesting a lower perceived competence of AI in this domain.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans, standard deviations, and statistical tests for the ROBOTH across sociodemographic variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eROBOTH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical care\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePersonal support\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSystem management\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eM (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003et(231)\u0026thinsp;=\u0026thinsp;1.347, p = .180, d = .21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et(231)\u0026thinsp;=\u0026thinsp;1.940, p = .054, d = .30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003et(231)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.067, p = .947, d = .01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42 (.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.89 (.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.27 (.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08 (.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.91 (.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF(5,227) = .331, p = .894, ƞ\u0026sup2; = .009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(5,227)\u0026thinsp;=\u0026thinsp;2.585, p = .027, ƞ\u0026sup2; = .063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF(5,227) = .335, p = .891, ƞ\u0026sup2; = .009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.28 (.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96 (.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.84 (.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.30 (.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40 (.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.97 (.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37 (.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.13 (.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.95 (.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 (.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.42 (.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.98 (.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52 (.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.93 (.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.27 (.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50 (.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00 (.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003et(231) = .027, p = .979, d = .004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et(231)\u0026thinsp;=\u0026thinsp;1.504, p = .134, d = .214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003et(231)\u0026thinsp;=\u0026thinsp;1.549, p = .123, d = .221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.32 (.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25 (.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.99 (.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.32 (.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07 (.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.84 (.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to investigate a multi-layered psychological model of AI acceptance among healthcare students and professionals, assessing the interplay of sociodemographic, dispositional, cognitive, and motivational predictors.\u003c/p\u003e \u003cp\u003eBy adopting Self-Determination Theory (SDT) as a unifying framework applied to a novel context, our findings advance a theory-driven understanding of human-machine interaction in healthcare education and practice. This study contributes to theory by demonstrating that need satisfaction operates as a proximal mechanism through which individuals interpret and evaluate emerging technologies such as AI. This extends SDT into the domain of human\u0026ndash;AI interaction, suggesting that technology acceptance is not only a function of perceived usefulness, but of the extent to which it supports fundamental psychological needs.\u003c/p\u003e \u003cp\u003eRather than conceptualizing attitudes toward AI as the outcome of isolated predictors, our hierarchical analyses strongly support a model in which distal traits and cognitive baseline variables are ultimately modulated or enriched by proximal everyday experiences of technology. Crucially, the results highlight the central role of basic psychological need satisfaction. Across analyses, the extent to which daily engagement with technology supports users\u0026rsquo; autonomy, competence, and relatedness emerged as a robust predictor of both positive and negative attitudes toward AI, providing significant incremental validity even when controlling for personality, conspiracist beliefs, and AI literacy. These findings suggest that the acceptance of intelligent systems is fundamentally shaped by the quality of the psychological experience of technology, specifically, the user\u0026rsquo;s sense of agency, effectiveness, and social connectedness.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis 1: Sociodemographic predictors\u003c/h2\u003e \u003cp\u003eH1a was supported, as male participants reported significantly more positive attitudes toward AI than females. This finding aligns with prior research highlighting a gender gap in openness to technological innovation (Kovačević \u0026amp; Demić, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Men often exhibit greater self-efficacy and confidence in digital domains, possibly influenced by sociocultural norms around competence and technology adoption (T\u0026oslash;mte \u0026amp; Hatlevik, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, women have been shown to express stronger ethical and privacy-related concerns regarding AI systems, reflecting broader gendered differences in technology perception and risk sensitivity (X. Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). H1b received partial support. Although age did not significantly predict general attitudes toward AI, younger participants (18\u0026ndash;25) reported lower perceived AI competence in the Personal support domain than those aged 26\u0026ndash;35. This contrasts somewhat with prior studies showing that older adults often express heightened concerns over privacy, ethical issues, and job displacement in the context of AI and automation, and may perceive AI as less intuitive or useful (Chu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gr\u0026uuml;nloh et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The present finding suggests that younger individuals may harbor greater skepticism about AI\u0026rsquo;s capacity to emulate human empathy or emotional intelligence. While younger cohorts are typically more willing to adopt AI for practical benefits like efficiency and multitasking, they may simultaneously question its affective reliability, especially in socially sensitive domains (Kyung \u0026amp; Kwon, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). H1c was not supported. Contrary to expectations, no significant differences were observed between students and professionals in attitudes toward AI or in their evaluations of its comparative competence. This result suggests that professional exposure or higher educational attainment alone may not be sufficient to shape more favorable attitudes, and that psychological factors may play a more critical role.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHypothesis 2: Personality traits, Dark Triad, and conspiracy beliefs\u003c/h3\u003e\n\u003cp\u003eH2a received partial support. Openness to experience was a significant positive predictor of favorable attitudes toward AI. This personality trait has consistently been associated with curiosity and adaptability, which facilitate acceptance of emerging innovations such as AI (Kaya et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While agreeableness did not significantly predict positive attitudes toward AI, it was significantly associated with lower levels of negative attitudes, confirming previous results (Stein et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This suggests that individuals high in agreeableness \u0026ndash; typically characterized by interpersonal trust, kindness, and cooperativeness \u0026ndash; may extend this disposition even toward artificial agents (Barnett et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schepman \u0026amp; Rodway, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), even in the absence of direct positive evaluations (Park \u0026amp; Woo, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Contrary to the hypothesis, neuroticism \u0026ndash; a personality trait characterized by anxiety and a tendency toward negative affect \u0026ndash; did not emerge as a significant predictor. While some studies link emotional instability to lower technology acceptance and increased concerns (Svendsen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), others find no clear association or even mitigating effects of emotional stability (Park \u0026amp; Woo, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schepman \u0026amp; Rodway, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, neuroticism\u0026rsquo;s impact may have been masked by stronger predictors. H2b yielded an unexpected but noteworthy result: Machiavellianism was positively associated with favorable attitudes toward AI. While traits from the Dark Triad are typically associated with distrust or skepticism toward others (Jones \u0026amp; Paulhus, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Somma et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), individuals high in Machiavellianism may perceive AI as a strategic and utilitarian tool that can be leveraged to gain control, exert influence over others, or pursue antisocial goals (Laakasuo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) within complex healthcare or organizational systems. This hypothesis was proposed but not confirmed in a previous study that employed a different measure of positive attitudes toward AI (Stein et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). H2c was fully supported. Higher levels of conspiracy thinking predicted more negative attitudes toward AI, consistent with studies linking conspiratorial ideation to distrust of systems perceived as opaque, data-driven, or potentially manipulative (Stein et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This result reinforces the need to address epistemic trust when promoting AI integration in healthcare.\u003c/p\u003e\n\u003ch3\u003eHypothesis 3: AI literacy\u003c/h3\u003e\n\u003cp\u003eH3 was only partially supported. In the model predicting GAAIS Positive, the Ethics subscale of the AILS had a negative effect, suggesting that individuals with higher ethical awareness may adopt a more cautious and discerning stance toward AI. This result is consistent with definitions of AI ethics as involving the recognition of risks, responsibilities, and broader societal implications associated with intelligent technologies (B. Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and is in line with systematic reviews highlighting the need to develop physician competencies in AI governance and ethical design to foster greater confidence in AI‑augmented decision‑making (Schuitmaker et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This finding also aligns with the observed positive association between Machiavellianism and favorable AI attitudes in our study, suggesting that less ethically oriented individuals may view AI as a strategic tool. Rather than signaling outright rejection, this ethical tendency may reflect a conditionally supportive attitude toward AI shaped by a desire for safeguards and transparency. These findings suggest that ethical reflection can temper uncritical acceptance of AI and promote more cautious evaluations, particularly in healthcare settings, where issues of legality, privacy, and the secure management of clinically confidential information are especially salient.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis 4: SDT-based psychological needs\u003c/h2\u003e \u003cp\u003eH4 was strongly supported, representing the most robust and innovative finding of the present study. Across both hierarchical models, the inclusion of the three SDT-based psychological needs (Step 4) yielded a highly significant increase in the explained variance, fundamentally altering the predictive landscape. Specifically, the extent to which everyday technology satisfies users' needs for autonomy, competence, and relatedness emerged as the strongest set of predictors for AI acceptance. Regarding positive attitudes (GAAIS Positive), Autonomy and Relatedness made unique positive contributions. This aligns with SDT\u0026rsquo;s proposition that individuals are more likely to enthusiastically engage with and value technological systems when they feel agentic and socially connected during their daily digital interactions (Bergdahl et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Moradbakhti et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The impact of psychological needs was even more striking in predicting negative attitudes and AI aversion (GAAIS Negative). All three basic needs \u0026ndash; Autonomy, Competence, and Relatedness \u0026ndash; significantly mitigated negative attitudes. This suggests a powerful \u0026ldquo;protective effect\u0026rdquo; of psychological need satisfaction: healthcare professionals and students who feel effective (competence), in control of their actions (autonomy), and meaningfully connected to others (relatedness) while using technology are significantly less likely to experience fear, suspicion, or alienation toward AI. This supports recent literature emphasizing that technology-induced anxiety is often rooted in the perceived threat to human agency and professional identity, which need-supportive environments can actively buffer (Peters et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sahin \u0026amp; Sahin, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the specific context of health professions education and clinical practice, these findings have profound implications. They suggest that to build acceptance for intelligent tools, institutions must move beyond simply training users on \u0026ldquo;how to click\u0026rdquo;. Instead, the design and implementation of AI systems must actively safeguard clinicians\u0026rsquo; sense of professional identity. Fostering these psychological dimensions is key to integrating AI as a supportive partner that enhances, rather than erodes, medical autonomy and the deeply interpersonal nature of healthcare.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis 5: Perceived comparative competence of AI\u003c/h2\u003e \u003cp\u003eRegarding perceptions of AI\u0026rsquo;s comparative competence measured with the ROBOTH scale, participants tended to view AI as most capable in System management tasks, followed by Clinical care, and least competent in Personal support. This pattern reflects current limitations in affective computing and the persistent inability of AI systems to emulate empathy, relational nuance, or emotional sensitivity, domains where human capabilities remain unmatched (Guo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The only significant sociodemographic difference on ROBOTH subscales was for age, with younger participants perceiving AI as less competent in the personal/emotional domain. These perception patterns suggest that attitudes toward AI vary by functional domain, with greater skepticism typically emerging when tasks involve empathy or interpersonal engagement. As Aly et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported, users are more reluctant to trust AI in emotionally laden or educational roles, underscoring the need to distinguish between cognitive and affective competence in AI design and evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eDespite these promising findings, the present study has certain limitations that should be acknowledged. First, the cross-sectional design precludes the establishment of causal relationships among dispositional traits, AI literacy, psychological need satisfaction, and attitudes toward AI. Future longitudinal or experimental research is required to ascertain whether enhancing technology-related need satisfaction actively drives shifts in AI acceptance over time. Second, the reliance on self-report instruments introduces the potential for common method variance and social desirability bias, although the strict anonymity of the survey protocol helps mitigate these risks. Third, while our sample included both students and practitioners, it was predominantly skewed toward the healthcare student population. Although our regression models (Step 1) did not identify professional status as a significant predictor of attitudes, further validation in larger, more balanced cohorts of experienced clinicians is warranted to fully establish the broader generalizability of these findings.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study possesses notable theoretical and methodological strengths. To our knowledge, it is one of the first investigations to apply a unifying theoretical framework (SDT) to disentangle the multi-layered predictors of AI acceptance in health professions education. The rigorous hierarchical analytical approach allowed for the precise isolation of specific dispositional, cognitive, and motivational drivers, advancing the field beyond mere descriptive or sociodemographic analyses of technology adoption. Furthermore, the integration of multidimensional instruments\u0026mdash;such as the ROBOTH scale to assess comparative competence and the TENS-Life to capture daily psychological experiences\u0026mdash;yielded a highly granular understanding of human-machine interaction. Collectively, these insights offer a robust, evidence-based foundation for designing future educational interventions to optimize the integration of intelligent systems into clinical training and practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions and educational implications","content":"\u003cp\u003eIn conclusion, the present study demonstrates that acceptance of artificial intelligence among healthcare students and professionals is a multifaceted process, driven not merely by cognitive or sociodemographic variables but primarily by the fulfillment of basic psychological needs in everyday interactions with technology. While dispositional traits and epistemic beliefs set a baseline orientation toward innovation, the proximal experience of autonomy, competence, and relatedness serves as the most robust safeguard against AI aversion and the strongest driver of positive attitudes. Furthermore, our findings highlight a crucial nuance regarding AI literacy: while technical proficiency does not automatically translate into enthusiasm, ethical awareness fosters a necessary, responsible caution. Ultimately, the successful integration of AI into healthcare education and clinical practice will depend less on the sheer volume of technological exposure, and significantly more on designing operational environments that empower practitioners to preserve their professional agency and relational identity in the face of increasingly automated systems. AI curricula should incorporate participatory learning approaches that enable students to critically evaluate and co-design AI use (supporting autonomy), simulation-based training to build applied competence, and reflective exercises that address the impact of AI on patient relationships (supporting relatedness).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAUTHOR CONTRIBUTIONS: M.B., S.R. and G.M.L.S. developed the research aims, conceptualizazion and methology. M.B. and S.A. wrote, reviewed and edited the manuscript. M.B., N.C., S.R., G.M.L.S. contributed to data collection. S.A. contributed to data analysis and wrote the results section of the manuscript. S.R., F.Z., M.G.S. and G.M.L.S. contributed to study design and manuscript review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTEREST STATEMENT: None.\u003c/p\u003e\n\u003cp\u003eDECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS: During the preparation of this work, the authors used Grammarly in order to improve readability. After using this tool, the authors reviewed and edited the content as necessary and took full responsibility for the publication's content.\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY STATEMENT: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eETHICS STATEMENT: The study was approved by the Research Evaluation Committee, Department of Psychology, University of Milano-Bicocca (Protocol No. RM-2024-773). 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Unveiling the role of honesty-humility in shaping attitudes towards artificial intelligence. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e238\u003c/em\u003e, 113072. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.paid.2025.113072\u003c/span\u003e\u003cspan address=\"10.1016/j.paid.2025.113072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"advances-in-health-sciences-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ahse","sideBox":"Learn more about [Advances in Health Sciences Education](http://link.springer.com/journal/10459)","snPcode":"10459","submissionUrl":"https://submission.nature.com/new-submission/10459/3","title":"Advances in Health Sciences Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial intelligence, Healthcare, Self-Determination Theory, Personality traits, AI literacy","lastPublishedDoi":"10.21203/rs.3.rs-9254457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9254457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of artificial intelligence (AI) into healthcare requires preparing professionals to engage critically with intelligent systems. Moving beyond isolated predictors, this study investigates a multi-layered psychological model of AI acceptance, drawing on Self-Determination Theory (SDT), and proposes a psychologically grounded model in which the satisfaction of basic psychological needs serves as a proximal mechanism shaping attitudes toward AI.\u003c/p\u003e\n\u003cp\u003eA cross-sectional survey was conducted with 233 Italian healthcare students and professionals. Participants completed validated measures of attitudes toward AI, psychological need satisfaction in relation to technology (autonomy, competence, relatedness), personality traits (Big Five and Dark Triad), conspiracy beliefs, and AI literacy. Hierarchical regression analyses were used to test the incremental contribution of motivational, dispositional, and cognitive factors. Additionally, comparative evaluations were conducted to assess the perceived competence of AI versus human professionals across different clinical domains.\u003c/p\u003e\n\u003cp\u003eThe final models explained a substantial proportion of the variance in both positive and negative attitudes. SDT variables provided strong incremental validity, emerging as the primary factors in promoting AI acceptance and mitigating AI aversion. Regarding comparative competence, AI was perceived as outperforming humans in system-level and analytical tasks, but significantly less skilled in domains requiring psychological and relational support.\u003c/p\u003e\n\u003cp\u003eFindings support a theory-driven account of AI acceptance in healthcare education, highlighting psychological need satisfaction as a key mechanism by which individuals evaluate emerging technologies. This extends SDT to the domain of human–AI interaction, suggesting that acceptance depends not only on knowledge or exposure, but on whether engagement with AI supports autonomy, competence, and relatedness.\u003c/p\u003e\n\u003cp\u003eAI training fostering a meaningful and responsible integration of AI in healthcare should move beyond technical instruction to include participatory, competence-building, and reflective approaches that address learners’ motivational needs.\u003c/p\u003e","manuscriptTitle":"From Everyday Technology Experience to AI Acceptance in Healthcare Education: A Self-determination Theory–based Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 16:46:09","doi":"10.21203/rs.3.rs-9254457/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-09T12:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233595181253236479966273565321028483087","date":"2026-05-05T11:21:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253902655243058355900581787825091953176","date":"2026-05-05T09:11:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T21:21:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T10:57:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T10:56:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Advances in Health Sciences Education","date":"2026-03-28T17:24:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"advances-in-health-sciences-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ahse","sideBox":"Learn more about [Advances in Health Sciences Education](http://link.springer.com/journal/10459)","snPcode":"10459","submissionUrl":"https://submission.nature.com/new-submission/10459/3","title":"Advances in Health Sciences Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cd2bc8f6-e5dc-4385-b21c-732337d7b987","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-09T12:24:36+00:00","index":57,"fulltext":""},{"type":"reviewerAgreed","content":"233595181253236479966273565321028483087","date":"2026-05-05T11:21:35+00:00","index":54,"fulltext":""},{"type":"reviewerAgreed","content":"253902655243058355900581787825091953176","date":"2026-05-05T09:11:19+00:00","index":53,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T16:46:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 16:46:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9254457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9254457","identity":"rs-9254457","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}