Personality Predictors of Attitudes and Misconduct Behaviors Related to Generative Artificial Intelligence: Evidence from the HEXACO and Dark Triad Models

Personality Predictors of Attitudes and Misconduct Behaviors Related to Generative Artificial Intelligence: Evidence from the HEXACO and Dark Triad Models | 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 Article Personality Predictors of Attitudes and Misconduct Behaviors Related to Generative Artificial Intelligence: Evidence from the HEXACO and Dark Triad Models Haiying Liang, Xu Mao, Michael J. Reiss This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7356639/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract This study investigates how personality traits, specifically those measured by the HEXACO Personality Inventory and the Dark Triad, predict university students’ attitudes toward generative artificial intelligence (GAI) and their engagement in GAI-related academic misconduct. The first objective was to develop and validate a Chinese-language scale to measure students’ attitudes toward GAI in academic contexts. The newly developed GAI attitudes scale was tested for psychometric properties, showing high internal consistency (Cronbach’s α = 0.92) and reliability. In the second part of the study, hierarchical linear regression analyses explored the relationship between personality traits and both GAI attitudes and misconduct behaviors. Findings indicated that Extraversion (β = 0.25, p < 0.001) and Openness to Experience (β = 0.36, p < 0.001) were significant positive predictors of favorable GAI attitudes. Regarding misconduct behaviors, Honesty-Humility (β = -0.36, p < 0.001), Agreeableness (β = -0.15, p < 0.001), and Conscientiousness (β = -0.25, p < 0.001) were significant negative predictors of GAI-related misconduct, while Narcissism (β = 0.24, p < 0.001) and Psychopathy (β = 0.25, p < 0.001) were significant positive predictors. Notably, GAI attitudes did not provide additional predictive value for misconduct beyond personality traits (ΔR² = .004, p = 0.55). These results contribute to the understanding of personality’s role in GAI adoption and unethical academic behaviors, with implications for the responsible integration and regulation of GAI in academic practices. Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Introduction The rapid proliferation of generative artificial intelligence (GAI) technologies is fundamentally transforming the way people engage with information. As a specialized subfield of artificial intelligence (AI), GAI is distinguished by its capacity to autonomously generate coherent and contextually relevant content 1 . As GAI becomes increasingly integrated into students’ academic practices—including literature reviews, manuscript drafting, code generation, and automated feedback 2 —it is vital to understand how they perceive and use these tools. Such inquiry is essential not only for educators and policymakers but also for guiding the ethical integration of GAI into scholarly practices. However, rigorous investigation into these attitudes depends on the availability of psychometrically robust, theoretically grounded instruments specifically tailored to GAI. While several instruments have been developed to assess general attitudes toward AI—such as the General Attitudes Toward Artificial Intelligence Scale 3 , the Attitudes Toward Artificial Intelligence Scale 4 , and the more recent Attitudes Towards AI scale-12 by Stein et al. 5 —these tools typically treat AI as a broad and undifferentiated construct, without differentiating the specific characteristics of GAI. Furthermore, few of these instruments were developed within academic contexts, and most do not capture the tripartite structure of attitude (cognitive, affective, behavioral) foundational in social psychology. Additionally, existing measures are almost exclusively developed in English, limiting their applicability across linguistic and cultural boundaries. To address these gaps, the first objective of this study was to develop and validate the GAI attitudes scale—a concise Chinese-language instrument that assesses students’ attitudes toward GAI specifically within academic contexts. The scale is designed to capture both positive and negative evaluations of GAI, grounded in the tripartite model of attitude, and to conceptualize GAI as a general set of technological affordances rather than as specific platforms (e.g., ChatGPT or Midjourney). This abstraction allows for broader applicability across disciplines and reduces the influence of transient technological branding on participants’ evaluations. Accordingly, the first research objective was: to construct and validate a psychometric instrument that captures students’ attitudes toward GAI within academic settings. While prior research on technology adoption has largely focused on demographic and sociocultural factors—such as age, gender, education level, and media exposure 6,7,8,9 —the role of stable psychological dispositions, particularly personality traits, remains largely understudied. Existing evidence suggests that personality may meaningfully shape attitudes toward technology, including both acceptance and ethical decision-making 10,11 . However, most prior studies have examined general AI or physically embodied AI (e.g., robots) 12,13 , with relatively few investigating GAI in academic settings. Previous research has primarily relied on the Big Five personality model to examine technology acceptance 5,14,15,16 . While the Big Five model offers valuable insights into broad dispositional tendencies, it may fall short in capturing morally relevant personality dimensions when measuring attitudes toward AI. As Stein et al. 5 suggest, the HEXACO Personality Inventory may provide more nuanced explanatory power, particularly through its inclusion of the Honesty-Humility dimension—a trait strongly linked to ethical decision-making and adherence to rules. While the Dark Triad traits ( Machiavellianism , Psychopathy and Narcissism ) have been applied in studies of technology abuse 17,18,19 , their use in the context of GAI-related misconduct remains limited. Accordingly, the second objective of this study was: to examine the predictive roles of personality—assessed via the HEXACO Personality Inventory and the Dark Triad traits—in shaping students’ attitudes toward GAI and their engagement in GAI-related misconduct. This research seeks to identify which psychological profiles are more likely to embrace or misuse generative AI tools. Understanding these associations can contribute to the development of targeted interventions, ethical training, and evidence-based policy recommendations, improving the responsible use of GAI in education and scholarly communication. Overview of studies and theoretical predictions To address the two overarching research objectives, we conducted two empirical studies. Study 1 focuses on the development and validation of a novel instrument designed to measure students’ attitudes toward GAI in academic settings. Building upon and adapting the ATTARI-12 framework 5 , this study evaluated the psychometric properties of the new scale, including its internal consistency, test–retest reliability, and convergent validity. Additionally, we examined the potential influence of social desirability bias on self-reported GAI attitudes. These initial validation studies were critical for ensuring that the instrument accurately captures both the positive and negative dimensions of GAI evaluation within a research context. Study 2 extended this work by investigating how university students’ attitudes toward GAI and GAI misconduct behaviors relate to individual differences in personality measured through HEXACO and the Personality Inventory and the Dark Triad traits. Accordingly, we formulated a series of hypotheses regarding how these personality dimensions may predict both attitudes towards GAI and engagement in GAI misconduct, as follows. The HEXACO Personality Inventory The HEXACO Personality Inventory is a widely used model in personality psychology, measuring six major traits: Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience 20,21 . This model has shown strong reliability and validity across various cultural contexts and is useful for understanding human behaviors 22,23 . Honesty–Humility , a core dimension of the HEXACO personality model, encompasses traits such as sincerity, fairness, and moral restraint. Individuals scoring high in this dimension are characterized by their reluctance to manipulate others for personal gain and their commitment to ethical behavior 21 . This trait has been shown to significantly influence ethical decision-making and pro-social behaviors 24 . In the context of GAI, researchers with high Honesty–Humility are expected to adopt more cautious attitudes toward GAI use. This expectation is grounded in evidence suggesting that individuals high in Honesty–Humility are less likely to engage in unethical behaviors 25 . Furthermore, these individuals are anticipated to hold more negative attitudes toward GAI misconduct behaviors. Therefore, it is hypothesized that: H1 Students higher in Honesty–Humility will report more negative attitudes toward GAI. H2 Students higher in Honesty–Humility will report lower likelihood of engaging in GAI misconduct behaviors. Emotionality encompasses traits such as anxiety, fearfulness, dependence, and sentimentality 20,21 . Individuals with high Emotionality tend to experience heightened sensitivity to stress and a strong need for emotional support from others 26 . This heightened sensitivity may lead them to perceive greater risks and potential negative outcomes associated with GAI technologies. Consequently, they may exhibit more cautious attitudes toward GAI use, driven by concerns about potential misuse or unintended consequences. Research has shown that individuals high in Emotionality are more likely to avoid risky behaviors and seek reassurance in uncertain situations 27 . This tendency suggests that such individuals may be less inclined to engage in unethical practices involving GAI due to fears of detection and feelings of guilt. Therefore, it is hypothesized that: H3 Students higher in Emotionality will report more negative attitudes toward GAI. H4 Students higher in Emotionality will report lower likelihood of engaging in GAI misconduct behaviors. Extraversion , characterized by sociability, enthusiasm, and assertiveness, is often associated with openness to innovation and experimentation 20,21 . Individuals high in extraversion are generally more willing to engage with new technologies, including GAI, due to their greater comfort in social interactions and openness to new experiences5 ,28 . This inclination may lead them to adopt GAI more readily, especially in environments where such technologies are perceived as enhancing performance or are socially accepted. However, this same openness can also increase the likelihood of engaging in misconduct if the use of GAI is seen as socially tolerated or advantageous. Therefore, it is hypothesized that: H5 Students higher in Extraversion will report more positive attitudes toward GAI. H6 Students higher in Extraversion will report higher likelihood of engaging in GAI misconduct behaviors. Agreeableness is characterized by traits such as empathy, cooperativeness, and trustworthiness 20,21 . Individuals high in agreeableness are more likely to engage in behaviors that promote social harmony and are less inclined to act unethically 15,29 . This tendency extends to their interactions with technology, where agreeable individuals may favor technologies that align with ethical standards and societal well-being. Research indicates that agreeableness is positively associated with prosocial behaviors and moral decision-making, suggesting that agreeable individuals are more likely to adopt technologies like GAI in ways that are ethically sound and socially responsible 24 . Furthermore, agreeableness has been linked to lower tendencies toward unethical behavior. Studies have shown that individuals high in agreeableness are less likely to engage in deviant behaviors, including those involving technology misuse 15,30 . This suggests that researchers with higher levels of agreeableness may be less inclined to misuse GAI and more likely to uphold ethical standards in their use of such technologies. Therefore, it is hypothesized that: H7 Students higher in Agreeableness will report more positive attitudes toward GAI. H8 Students higher in Agreeableness will report lower likelihood of engaging in GAI misconduct behaviors. Conscientiousness is characterized by traits such as diligence, self-discipline, and ethical responsibility 20,21 . Highly conscientious individuals tend to be cautious and deliberate in their decision-making processes, often exhibiting skepticism toward technologies 15,31 . This skepticism arises from their preference for structured environments and adherence to established norms, leading them to critically assess the potential risks and implications of adopting new technologies 24 . Moreover, conscientious individuals are less likely to engage in misconduct related to GAI because their strong sense of duty and moral responsibility fosters adherence to ethical standards, reducing the likelihood of participating in activities such as academic dishonesty or misuse of AI-generated content 25 . Based on these considerations, the following hypotheses are proposed: H9 Students higher in Conscientiousness will report more negative attitudes toward GAI. H10 Students higher in Conscientiousness will report lower likelihood of engaging in GAI misconduct behaviors. Openness to Experience is characterized by traits such as intellectual curiosity, creativity, and a preference for novelty 20,21 . Individuals high in Openness to Experience are typically more willing to explore and adopt innovative technologies 32 . This openness may lead to more positive attitudes toward GAI use, as these individuals are generally more accepting of technological advancements. However, Openness to Experience is not directly associated with unethical intent 33 . Therefore, it is hypothesized that: H11 Students higher in Openness to Experience will report more positive attitudes toward GAI. H12 Students higher in Openness to Experience will not predict GAI misconduct behaviors. The Dark Triad The Dark Triad refers to a cluster of three interrelated but distinct personality traits— Machiavellianism , Psychopathy, and Narcissism —that are characterized by self-serving, manipulative, and often callous behaviors 34 . Coined by Paulhus and Williams 32 , the Dark Triad has become a central construct in personality psychology, particularly in understanding antisocial and socially aversive behaviors 32,35,36 . Machiavellianism is characterized by manipulativeness, strategic self-interest, and a lack of morality 32 . Individuals exhibiting high levels of Machiavellianism tend to view interpersonal relationships as opportunities for exploitation, often employing deceitful tactics to achieve personal goals 37,38,39 . In the context of academic research, individuals high in Machiavellianism may be more inclined to exploit GAI technologies for competitive advantage, regardless of ethical considerations. Therefore, it is hypothesized that: H13 Students higher in Machiavellianism will report more positive attitudes toward GAI. H14 Students higher in Machiavellianism will report a higher likelihood of engaging in GAI misconduct behaviors. Psychopathy is characterized by impulsivity, low empathy, and a propensity for unethical behavior 32 . Individuals exhibiting high levels of psychopathy often display a lack of remorse, shallow affect, and a disregard for the impact of their actions on others 32,40 . These traits may contribute to ethical indifference and a greater comfort with rule-breaking, particularly in contexts where personal gain is perceived. In the realm of academic research, such individuals may be more inclined to exploit GAI technologies for competitive advantage, irrespective of ethical considerations. Their impulsive nature and focus on self-interest can lead to a higher likelihood of engaging in misconduct behaviors involving GAI. Therefore, it is hypothesized that: H15 Students higher in psychopathy will report more positive attitudes toward GAI. H16 Students higher in psychopathy will report a higher likelihood of engaging in GAI misconduct behaviors. Narcissism is characterized by grandiosity, a need for admiration, and a lack of empathy 32 . Individuals exhibiting high levels of narcissism often engage in self-enhancing behaviors and seek recognition, sometimes at the expense of ethical considerations. In academic contexts, such traits may drive researchers to utilize GAI technologies to polish their outputs or gain recognition, even through misconduct 41 . This inclination is supported by studies indicating that narcissism is positively correlated with academic dishonesty and unethical behavior 42 . Therefore, it is hypothesized that: H17 Students higher in narcissism will report more positive attitudes toward GAI. H18 Students higher in narcissism will report a higher likelihood of engaging in GAI misconduct behaviors. Study 1 The primary aim of Study 1 was to develop a psychometrically robust scale specifically designed to measure students’ attitudes toward the use of GAI in academic contexts. The scale development process was guided by three core principles: (a) the scale should be unidimensional to enable clear interpretation of overall attitude scores; (b) it should incorporate items representing the three classic components of attitudes in psychology—cognitive, affective, and behavioral; and (c) it should contain both positively and negatively worded items to capture the full evaluative spectrum, while mitigating agreement bias. Grounded in social psychological theories of attitude structure 43 and informed by existing general AI attitude measures 3,5 , we initially generated 24 items. Item formulation aimed at achieving balance in both evaluative direction (12 positive, 12 negative) and attitudinal facet representation (8 items per facet, across valences). Given the context-specific nature of our research, all items were preceded by a standardized introductory instruction that briefly defined GAI and situated its use in academic research (e.g., literature review, writing, analysis), thereby minimizing semantic ambiguity and ensuring that participants interpreted the attitude object consistently. This instruction was considered an integral component of the measurement tool. In the second stage, a panel of three researchers—whose expertise covered educational assessment, psychology, and educational technology—reviewed the initial item pool. Items were revised or eliminated if they were semantically redundant, too contextually narrow, or ambiguous in focus. This refinement process resulted in a 12-item scale with each attitudinal component (cognitive, affective, behavioral) represented by four items: two positively and two negatively worded. Although the items span distinct psychological dimensions, they were theorized to load onto a single latent factor reflecting an individual’s general attitude toward the use of GAI in academic work. To assess construct validity, Study 1 also included measures of participants’ intention to use GAI and actual use of GAI in academic contexts. We expected that general attitudes measured by the GAI attitudes scale would correlate positively with their intention and actual use of GAI, based on Theory of Planned Behavior 44 . Additionally, to evaluate potential susceptibility to social desirability bias, we included a short-form social desirability scale 45 . Given the careful phrasing and balanced item valence, we hypothesized that GAI attitudes scores would not be significantly associated with socially desirable responding. Methods Ethics Statement This research received ethical approval from Peking University Institutional Review Board. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all participants prior to their participation in the study, and they were assured of their anonymity and the voluntary nature of their involvement. Participants and Procedure To ensure sufficient statistical power for scale validation and subsequent correlational analyses, a priori power analysis conducted using semPower (Version 2.0.1) indicated that a minimum sample size of 500 participants was necessary. A total of 625 participants, students from five universities in China, were recruited via Wenjuanxing website, which is a popular website for collecting survey responses in China. Incentives of 5 RMB were provided for each completed questionnaire. The average completion time was approximately 4 minutes. Exclusion criteria were preregistered and applied rigorously. These included completion time under 120 seconds, failure to pass at least one of two attention checks. Based on these criteria, 78 participants were excluded (31 for completion time, 47 for failing the description task), yielding a final sample of 547 participants (279 female, 268 male). Participants ranged in age from 20 to 35 years. After providing informed consent, participants were first asked to create a unique anonymous identifier by combining two elements only they would know—for example, the name of an elementary school teacher and the month of their birth. This identifier could not be traced back to participants’ identities by the researchers but allowed for accurate matching in the follow-up test–retest reliability assessment. Participants first answered demographic questions, followed by an attention check question. Next, they completed the GAI Attitudes Scale, which assessed their attitudes toward the use of GAI in academic contexts. They then responded to measures evaluating their intention to use GAI, actual GAI usage, a social desirability scale, and a final attention check in the form of a summary question. Measures GAI attitudes scale We administered the newly developed GAI attitudes scale to assess participants’ attitudes toward GAI in academic settings. The scale includes 12 items representing the cognitive, affective, and behavioral components of attitudes, each balanced with positive and negative wording. Responses were recorded using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). Psychometric properties and descriptive statistics are reported in the Results section. Behavioral intention to use GAI in academic contexts Participants’ intention to use generative AI in academic settings was measured with a single-item indicator adapted for this study. The item asked: “To what extent would you like to use generative AI (e.g., ChatGPT, Claude, Gemini) in your academic work (e.g., research, writing, teaching)?” Responses were recorded on a 5-point Likert scale ranging from 1 (Not at all) to 5 (Very strongly intend to). Actual use of GAI in academic settings To assess participants’ current engagement with generative AI tools in academic domains, a single item was used: “How frequently do you currently use generative AI tools (e.g., ChatGPT, Claude, Gemini) in your academic work (e.g., writing papers, preparing lectures, analyzing data)?” Responses were measured on a 5-point scale ranging from 1 (Never) to 5 (Very frequently). Social desirability To assess potential response bias, we included a 17-item Social Desirability Scale 44 . Participants responded to whether a set of socially desirable or undesirable behaviors described them (true/false format). Scores ranged from 0 to 17, with higher scores indicating greater tendency toward socially desirable responding. Results We assessed the factorial validity of the scale based on the assumption that all items continue to reflect a single underlying construct—students’ attitudes toward GAI. To evaluate this, we conducted a confirmatory factor analysis comparing a series of models with progressively fewer constraints. As shown in Table 1, among the tested models, the bifactor S-1 model with content facets (Model b) demonstrated the best overall fit. It significantly outperformed the single-factor model (Model a), as indicated by the chi-square difference test (Δχ²(8) = 20.02, p = .010), and showed improved CFI, RMSEA, SRMR, and lower AIC/BIC values. While the full model including both content and wording factors (Model d) had slightly better absolute fit indices (e.g., lowest RMSEA and SRMR), the improvement over Model (b) was not statistically significant (p = .236) and came at the cost of model complexity. Table 1. Goodness of fit for competing confirmatory factor models for the GAI attitudes scale. χ² df CFI RMSEA SRMR AIC BIC Comp Δχ² Δdf p (a) Single factor model 66.89 54 0.96 0.020 0.021 15574.25 15642.12 – – – – Bifactor S-1 models with one global factor and orthogonal specific factors for … (b) Content facets 46.87 46 0.97 0.006 0.0175 15568.89 15664.14 a 20.02 8 .010* (c) Item wording 60.33 48 0.96 0.021 0.0200 15571.88 15659.02 a 6.56 6 .365 (d) Content facets and item wording 38.84 40 0.95 0.005 0.0157 15567.73 15679.33 b 8.03 6 .236 c 21.49 8 0.129 As shown in Table 2, all 12 items of the GAI Attitudes Scale demonstrated moderate to strong standardized loadings on the general factor (range = .58 to .72), supporting the presence of a common underlying construct—attitudes toward GAI in academic contexts. Items from the cognitive (Items 1–4) and affective (Items 5–8) subdomains also showed meaningful loadings on their respective specific factors (range = .27 to .38), indicating content-specific variance beyond the general factor. Residual variances ranged from .30 to .49, and bifactor indices were computed to further evaluate the influence of multidimensionality. The general factor accounted for 86% of the common variance (ECV = 0.86), while the specific factors contributed only marginally. The omega hierarchical coefficient for the general factor was 0.92, indicating that the majority of the reliable variance in total scores can be attributed to the general construct—students’ overall attitudes toward GAI. Taken together, these results support the interpretation of the scale as an essentially unidimensional measure, justifying the use of a total score. Table 2. Factor loading pattern for the GAI attitudes scale. Item Standardized factor loadings (on general factor) Standardized factor loadings (on specific factor) Residual variance 0.62 0.31 (Cognitive) 0.42 0.58 0.29 (Cognitive) 0.49 0.65 0.33 (Cognitive) 0.38 0.59 0.36 (Cognitive) 0.41 0.68 0.27 (Affective) 0.35 0.61 0.34 (Affective) 0.42 0.70 0.29 (Affective) 0.31 0.63 0.38 (Affective) 0.37 0.72 – 0.30 0.66 – 0.37 0.69 – 0.34 0.60 – 0.44 Descriptive statistics and reliability estimates for the study variables are presented in Table 3. The internal consistency of the GAI attitudes scale was excellent (Cronbach’s α = .92). The distribution approximated a near-normal distribution (Skew = –0.08, Kurtosis = 0.02). The social desirability scale demonstrated acceptable reliability (α = .79). Participants reported high behavioral intention (M = 4.18, SD = 0.51) and actual (M = 4.18, SD = 0.50) use of GAI tools, with the two items showing high internal consistency when combined (α = .93). Pearson correlation analyses revealed strong positive associations between attitudes toward GAI and both behavioral intention (r = .86, p < .001) and actual use (r = .85, p < .001). Intention and use were also highly correlated (r = .86, p < .001), supporting the convergent validity of the measures. Social desirability showed no significant correlation with GAI attitudes (r = –.03, p = .468) or use (r = –.05, p = .246), suggesting minimal response bias. Gender was weakly but significantly associated with attitudes (r = .12, p = .004), intention (r = .12, p = .005), and use (r = .10, p = .011), with males scoring slightly higher. Age and degree level were not significantly related to any GAI-related variables. Table 3. Descriptive statistics and correlation analysis. 1 2 3 4 5 6 Cronbach’s α M SD Skew Kurt t(p) 1 GAI attitudes scale 0.916 50.22 4.9 -0.08 0.02 2 Social Desirability 0.794 13.63 3.27 -1.17 0.82 -0.03 (0.468) 3 GAI Intention 4.18 0.51 0.25 0.21 0.86 (0.0) -0.053 (0.199) 4 GAI use behavior 4.18 0.5 0.31 0.28 0.853 (0.0) -0.047 (0.246) 0.863 (0.0) 5 Age 37.66 10.61 -0.02 -1.25 0.04 (0.325) 0.026 (0.518) 0.018 (0.657) 0.06 (0.142) 6 Gender 0.117 (0.004) -0.016 (0.697) 0.115 (0.005) 0.103 (0.011) -0.006 (0.879) 7 Degree 0.007 (0.868) 0.008 (0.854) -0.032 (0.436) -0.033 (0.427) -0.036 (0.382) -0.014 (0.724) Test–retest reliability In order to further evaluate the reliability of the GAI attitudes scale by assessing its test–retest reliability, the scale was administered to the same participants—postgraduate students from five universities in China—a second time. The instructors at these universities were re-contacted to help administer the second round of the survey to the same groups of students. A total of 383 participants completed the GAI attitudes scale for a second time, allowing for the assessment of test–retest reliability. The survey was conducted via the Wenjuanxing platform, and responses were matched using the unique anonymous identifiers provided by participants. Of the 383 responses, 381 could be matched to the initial survey. Among these, 3 responses were excluded due to incompleteness, resulting in 378 valid cases for the test–retest reliability analysis. The internal consistency reliability of the GAI attitudes scale was also high this time (Cronbach’s α = 0.912), indicating strong scale coherence over time. Descriptive statistics confirmed that the distribution of scores approximated normality, with acceptable levels of skewness (–0.319) and kurtosis (0.24). GAI attitudes scale scores followed a reasonably symmetrical and mesokurtic distribution at both measurement points, consistent with expectations for a psychometrically sound scale. Most importantly, the test–retest reliability was strong, with a Pearson correlation of r(378) = 0.856, p < 0.001, demonstrating that participants’ attitudes toward GAI in academic contexts were highly stable over time. Study 2 Building on the initial validation of the GAI attitudes scale in Study 1, Study 2 addressed the second core research objective: examining how individual differences in personality traits predict students’ attitudes toward GAI in academic contexts, as well as their GAI-related misconduct behaviors. Specifically, this study focused on two complementary personality frameworks—the HEXACO Personality Inventory and the Dark Triad traits. To promote transparency, we preregistered Study 2 prior to data collection, outlining all hypotheses and the intended analyses. (https://aspredicted.org/93ny-7qhg.pdf). Methods Participants An a priori power analysis conducted using G*Power (assuming a small to moderate effect size of f² = 0.08, with 80% power, α = 0.05, and 12 predictors in a hierarchical linear regression) indicated that a minimum sample size of 234 participants was required. To ensure adequate power and allow for potential exclusions during data screening, we recruited a total of 1007 participants via the Wenjuanxing platform. Several quality control procedures were implemented during data screening to ensure data integrity and minimize the impact of careless or inattentive responding. First, the questionnaire consisted of 107 items, with most being 5-point Likert scale questions. The expected completion time was between 4 and 8 minutes, but the data shows that many participants completed the survey between 3-4 minutes. As a result, we set the cutoff time at 3 minutes, removing 99 responses. Additionally, two attention check questions (Q5 and Q107) were included to assess participant attentiveness. For Q5, which asked, “Which of the following is a fruit?”, responses that incorrectly answered option 1 (n = 4) and option 3 (n = 2) were excluded, resulting in 6 removals. For Q107, which asked, “What is the main theme of this questionnaire?”, incorrect responses (option 1, n = 30 and option 3, n = 8) led to the removal of 38 responses. In total, 143 responses were excluded, leaving a final sample of 864 valid responses for analysis. The final sample had a mean age of 23.1 years (SD = 2.92), and was composed of 558 females, 306 males. The distribution of participants’ level of study is as follows: 50.3% (n = 434) were undergraduates, 38.5% (n = 333) were master’s students, and 11.2% (n = 97) were doctoral students. Among the five disciplinary categories, the highest number of participants were from the “Humanities and Social Sciences” category, with 417 participants, accounting for 48.3% of the sample. The second largest group was from the “Medical and Health Sciences” category, with 140 participants, making up 16.2% of the sample, while the “Business and Economics” category had the fewest participants, with 77, equating to 8.9%. Measures All measures were administered using 5-point Likert-type response scales (1 = strongly disagree, 5 = strongly agree), unless otherwise specified. HEXACO Personality Inventory Participants’ broad personality traits were measured using the 60-item HEXACO Personality Inventory – Revised (HEXACO-60) 20 . This instrument assesses six core dimensions: Honesty–Humility (e.g., “I wouldn’t use flattery to get a raise or promotion at work”), Emotionality (e.g., “I sometimes can’t help worrying about little things”), Extraversion (e.g., “I feel reasonably satisfied with myself overall”), Agreeableness (e.g., “People sometimes tell me that I am too critical of others”), Conscientiousness (e.g., “I plan ahead and organize things to avoid scrambling at the last minute”), and Openness to Experience (e.g., “I enjoy looking at maps of different places”). Participants responded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Internal consistency was acceptable to excellent across the six dimensions (Honesty–Humility: α = 0.92; Emotionality: α = 0.91; Extraversion: α = 0.84; Agreeableness: α = 0.90; Conscientiousness: α = 0.91; Openness: α = 0.83). Dark Triad We assessed participants’ Dark Triad personality traits with the Short Dark Triad scale 46 . This instrument includes nine items on Machiavellianism (e.g., “It’s not wise to tell your secrets”), six items on psychopathy (e.g., “People often say I’m out of control”), and nine items on narcissism (e.g., “Many group activities tend to be dull without me”). Reliability analyses suggested good to very good internal consistencies for all three scales (Machiavellianism: Cronbach’s α = 0.86; psychopathy: Cronbach’s α = 0.83; narcissism: Cronbach’s α = 0.83). GAI attitudes Scale Participants’ attitudes toward GAI were assessed using the Chinese version of the newly developed GAI attitudes scale in Study 1. In the present sample (N = 864), the scale demonstrated excellent internal consistency (Cronbach’s α = 0.85). GAI Misconduct Behavior Scale In this study, we employed a GAI academic misconduct scale adapted from a previously validated instrument developed by Sun et al. 16 , designed to measure how frequently students engage in academic misconduct involving generative AI. To enhance its comprehensiveness, we added a new item to the original four-question scale: “I have used AI to answer in unauthorized exams or tests.” Therefore, the scale consists of five items, each evaluated on a 5-point Likert scale ranging from 1 (never) to 5 (always). In the present study, this instrument exhibited high internal reliability (Cronbach’s α = 0.78). Confirmatory factor analysis supported its unidimensional structure, indicating good model fit (χ2(2) = 4.083, p = 0.130; CFI = 0.976; RMSEA = 0.036; SRMR = 0.004). Results Descriptive statistics and correlations Table 4 presents the descriptive statistics (means and standard deviations) and bivariate Pearson correlations among the study variables. The sample (N = 864) had a mean age of 23.14 years (SD = 2.92). Average scores on the HEXACO dimensions ranged from 2.71 (Neuroticism) to 3.45 (Extraversion), while the Dark Triad traits ranged from 2.44 (Psychopathy) to 2.99 (Narcissism). The mean score for attitudes toward GAI was 3.33 (SD = 0.79), and for GAI academic misconduct was 1.79 (SD = 0.80). Age was positively correlated with Disciplines (r = 0.11, p < .001), while Level of study showed significant negative associations with Gender (r = –0.11, p < .001) and Disciplines (r = –0.04, p < .05). Within the HEXACO traits, Honesty–Humility was positively associated with Agreeableness (r = 0.18, p < .001), whereas Emotionality showed positive correlations with Openness to Experience (r = 0.15, p < .001). Machiavellianism correlated positively with Psychopathy (r = 0.25, p < .001) and Narcissism (r = 0.12, p < .001). Psychopathy was also positively associated with Narcissism (r = 0.14, p < .001). Attitudes toward GAI demonstrated significant positive correlations with Extraversion (r = 0.17, p < .001), Agreeableness (r = 0.18, p < .001), and Neuroticism (r = 0.11, p < .01), but a small negative correlation with Openness to Experience (r = –0.08, p < .05). GAI academic misconduct showed positive correlations with Machiavellianism (r = 0.33, p < .001), Psychopathy (r = 0.37, p < .001), Narcissism (r = 0.12, p < .001), and Neuroticism (r = 0.25, p < .001. Conversely, GAI academic misconduct was negatively correlated with Attitudes toward GAI (r = –0.25, p < .001), Emotionality (r = –0.02, p < .05), and Openness to Experience (r = –0.06, p < .05), indicating that individuals with more positive AI attitudes, higher emotionality, and greater openness were less likely to report engaging in academic misconduct involving GAI. Table 4. Descriptive statistics and correlations. Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 M SD 1. Age 23.1 2.92 2. Gender 0.02 3. Level of study -0.03 0.11** 4. Disciplines -0.11*** -0.04 -0.42*** 5. Honesty–Humility 0.04 0.01 0.04 -0.03 3.41 0.55 6. Emotionality -0.02 0.00 -0.03 0.01 -0.01 3.25 0.76 7. Extraversion 0.05 -0.03 -0.01 0.01 -0.02 0.01 3.2 0.72 8. Agreeableness -0.02 0.05 -0.01 -0.02 0.18*** -0.01 0.09* 3.45 0.89 9. Openness to Experience 0.04 -0.01 -0.02 0.00 -0.04 -0.02 0.15*** -0.04 3.39 0.56 10. Neuroticism 0.00 0.05 0.02 -0.04 0.15*** 0.00 0.04 0.13*** 0.11** 3.42 0.72 11. Machiavellianism 0.00 -0.01 0.00 -0.03 -0.27*** -0.01 -0.03 -0.24*** 0.04 0.04 2.71 0.66 12. Psychopathy 0.02 -0.02 -0.07* 0.04 -0.25*** 0.01 0.10** -0.20*** 0.05 -0.17*** 0.27*** 2.44 0.52 13. Narcissism 0.02 0.04 -0.04 0.05 -0.12*** 0.04 0.03 -0.01 0.15*** 0.01 0.18*** 0.14*** 2.99 0.82 14. GAI attitudes 0.02 0.01 0.02 0.00 -0.08* 0.01 0.17*** -0.02 0.18*** -0.06 -0.06 0.01 0.09** 3.3 0.79 15. GAI misconduct 0.05 -0.02 -0.01 0.00 -0.38*** 0.00 -0.02 -0.30*** 0.01 -0.25*** 0.33*** 0.37*** 0.12*** 0.11** 1.79 0.8 Note. N = 864. p < 0.05*, p < 0.01**, p < 0.001*** Gender is coded as: 1 = Male; 2 = Female Level of study is coded as: 1 = Undergraduate; 2 = Master’s; 3 = Doctoral Disciplines are coded as: 1 = Humanities and Social Sciences; 2 = Science and Engineering; 3 = Business and Economics; 4 = Medical and Health Sciences; 5 = Other Hierarchical linear regression Our primary data analysis, which involved multiple regression, began by confirming that all required assumptions were met: the residuals were independent and normally distributed, with no issues of multicollinearity or heteroskedasticity identified 47 . Additionally, the Cook’s distance values indicated that no influential cases were affecting the model. We therefore proceeded with hierarchical linear regression as the central analytical approach. The predictive role of HEXACO personality inventory and the Dark Triad on GAI attitudes A hierarchical linear regression analysis was conducted to examine the predictors of participants’ attitudes toward GAI. The analysis proceeded in three steps: Step 1: Gender, age, level of study, and disciplines were entered as predictors. The model accounted for a negligible amount of variance in GAI attitudes (R² = 0.001). None of them (e.g., age (β = 0.013, p = 0.656), gender (β = 0.004, p = 0.369)) emerged as significant predictors. Step 2: In the second step, the six HEXACO personality dimensions were added, resulting in a notable increase in explained variance (ΔR² = 0.125). Among these, Honesty–Humility showed a marginal effect (β = 0.011, p = 0.089), while Extraversion (β = 0.248, p < 0.001) and Openness to Experience (β = 0.358, p < 0.001) emerged as significant positive predictors of GAI attitudes. Step 3: The final step introduced the Dark Triad personality traits, which further increased the explained variance (ΔR² = 0.233). Of these, Machiavellianism was a significant positive predictor (β = 0.253, p < 0.001), whereas Narcissism (β = -0.002, p = 0.814) and Psychopathy (β = -0.004, p = 0.535) were not significant. Overall, HEXACO Extraversion and Openness to Experience, as well as Machiavellianism from the Dark Triad, emerged as robust positive predictors of GAI attitudes. In contrast, age, gender, level of study, disciplines and other personality traits were not significant predictors. The predictive role of HEXACO personality inventory and the Dark Triad on GAI misconduct behaviors A hierarchical linear regression analysis was conducted to examine the predictive roles of the HEXACO personality dimensions, Dark Triad traits, and attitudes toward GAI in GAI-related academic misconduct behaviors. Step 1: In the first step, gender, age, level of study, and disciplines were included as predictors. These variables explained only a negligible portion of variance in GAI misconduct (R² = .002), with none demonstrating a significant effect. Step 2: The addition of the six HEXACO personality dimensions in the second step led to a substantial improvement in model fit (ΔR² = .234, p < .001). Honesty–Humility (β = -0.36, p < .001), Agreeableness (β = -0.15, p < .001), and Conscientiousness (β = -0.25, p < .001) all emerged as significant negative predictors of GAI misconduct. Step 3: Incorporating the Dark Triad traits in the third step further improved the explanatory power of the model (ΔR² = .258, p < .001). Both Narcissism (β = 0.24, p < .001) and Psychopathy (β = 0.25, p < .001) were significant positive predictors of GAI misconduct, whereas Machiavellianism was not significant. Step 4: In the final step, GAI attitudes were entered into the model. However, this variable did not account for any additional variance (ΔR² = .004, p = .55), indicating that attitudes toward GAI did not contribute incremental predictive value beyond the personality variables already included. The final regression model accounted for a substantial proportion of the variance in GAI misconduct behaviors (R² = .498). These findings indicate that personality traits—particularly lower levels of Honesty–Humility, Agreeableness, and Conscientiousness, and higher levels of Narcissism and Psychopathy—are robust predictors of GAI-related academic misconduct. In contrast, after accounting for these personality dimensions, attitudes toward GAI provided no additional explanatory power. Summary of results Predictors of GAI Attitudes The hypotheses related to the HEXACO personality traits were partially supported. Extraversion and Openness to Experience emerged as significant positive predictors of GAI attitudes. Therefore, H5 and H11 were supported. However, the remaining personality traits did not show statistically significant effects, and thus H1, H3, H7, and H9 were not supported. Regarding the Dark Triad, only Machiavellianism emerged as a significant positive predictor of GAI attitudes, supporting H13. In contrast, Narcissism and Psychopathy did not show significant effects. Therefore, H15 and H17 wer e not supported . Predictors of GAI Misconduct Behaviors In terms of GAI misconduct, the HEXACO traits were more consistently aligned with the hypotheses. Honesty–Humility, Agreeableness, and Conscientiousness all emerged as significant negative predictors of GAI misconduct, supporting H2, H8, and H10. However, the remaining personality traits did not show statistically significant effects, and thus H4, H6 and H12 were not supported. In terms of the Dark Triad traits, both Narcissism and Psychopathy were significant positive predictors of GAI misconduct, supporting H16 and H18. Machiavellianism, however, did not significantly predict GAI misconduct in this analysis, and thus H14 was not supported. In addition, GAI attitudes did not contribute significant additional explanatory power in predicting GAI misconduct behaviors after accounting for personality traits. General discussion The study found that Extraversion and Openness to Experience were significant positive predictors of GAI attitudes, indicating that individuals high in these traits are more inclined to adopt and favor GAI technologies. This aligns with previous research suggesting that extraverted individuals, characterized by sociability and enthusiasm, are more open to new experiences and technologies 48,49 . Similarly, openness to experience has been associated with a greater willingness to engage with novel and complex ideas, including technological innovations. Regarding the Dark Triad traits, Machiavellianism was a significant positive predictor of GAI attitudes, supporting the hypothesis that individuals high in Machiavellianism, characterized by manipulation and strategic self-interest, may view GAI as a tool for personal gain. However, Narcissism and Psychopathy did not show significant effects, which contrasts with some studies indicating that these traits are associated with a more favorable view of technology adoption 17,50 . This discrepancy may reflect differences in the specific technologies studied or the contexts in which these studies were conducted. In terms of academic misconduct, the study found that Honesty–Humility, Agreeableness, and Conscientiousness were significant negative predictors of GAI misconduct behaviors. These findings are consistent with the literature suggesting that individuals high in these traits are more ethical and less likely to engage in unethical behaviors 51,52,53 . Conversely, Narcissism and Psychopathy were significant positive predictors of GAI misconduct, indicating that individuals high in these traits may be more willing to exploit GAI for personal gain, even at the expense of academic integrity 54 . Interestingly, Machiavellianism did not significantly predict GAI misconduct in this study, which contrasts with some research indicating that Machiavellian individuals are more likely to engage in unethical behaviors when they perceive personal benefits 55,56 . This finding suggests that the relationship between Machiavellianism and misconduct may be context-dependent, and further research is needed to explore the conditions under which this relationship holds. An interesting finding was that GAI attitudes did not contribute significant additional explanatory power in predicting GAI misconduct behaviors after accounting for personality traits. This suggests that personality traits, rather than attitudes toward GAI, are more decisive in determining whether individuals will engage in academic misconduct. This finding highlights the importance of personality in influencing ethical behaviors, even when attitudes toward the technology itself may be positive or neutral. Conclusion This study examined the predictive roles of HEXACO personality traits and the Dark Triad in shaping students’ attitudes toward GAI and their likelihood of engaging in GAI-related academic misconduct. The findings underscore the significant influence of personality traits on both GAI adoption and ethical behavior within academic contexts. Specifically, Extraversion and Openness to Experience emerged as positive predictors of favorable GAI attitudes, aligning with existing literature that associates these traits with openness to new technologies and ideas. Conversely, Honesty–Humility did not significantly predict GAI attitudes, suggesting that ethical considerations may not directly influence the adoption of GAI tools in academic settings. Regarding the Dark Triad, Machiavellianism was positively associated with favorable GAI attitudes, indicating that individuals high in this trait may perceive GAI as a strategic tool for personal gain. In terms of academic misconduct, Honesty–Humility, Agreeableness, and Conscientiousness were significant negative predictors, highlighting the role of ethical and prosocial traits in discouraging unethical behaviors related to GAI. On the other hand, Narcissism and Psychopathy were positive predictors of GAI misconduct, suggesting that individuals high in these traits may exploit GAI for self-serving purposes without regard for ethical standards. Interestingly, Machiavellianism did not significantly predict GAI misconduct, indicating that its influence on unethical behavior may be context-dependent. These findings contribute to the growing body of literature on the intersection of personality and technology adoption, emphasizing the need for a nuanced understanding of how individual differences influence ethical decision-making in the context of emerging technologies like GAI. Future research should further explore these relationships and consider additional factors such as institutional policies and cultural norms that may mediate the impact of personality on GAI-related behaviors. Limitations and Future Research First, the study employed a cross-sectional design, which limits the ability to draw causal inferences. The relationships observed between personality traits and GAI-related outcomes could be influenced by other unmeasured variables, such as situational factors or prior experiences with technology. Future research could benefit from using longitudinal designs to track changes in attitudes and behaviors over time and assess the directionality of these relationships. Second, the study relied on self-report measures, which may be subject to social desirability bias or other response biases. While self-report inventories like HEXACO and the Dark Triad are widely used in personality research, combining them with more objective measures, such as behavioral observations or peer ratings, could provide a more accurate assessment of personality and its influence on GAI-related behaviors. Additionally, future studies could consider experimental designs to test the causal effects of personality traits on GAI adoption and misconduct in controlled settings. Third, the sample in this study was limited to academic researchers, in China which may not be fully representative of the broader population of technology users. As GAI tools are adopted across various industries, future research should explore how personality traits influence GAI attitudes and behaviors in non-academic contexts. It would be particularly interesting to examine how personality traits interact with professional roles, such as industry professionals, students, or policymakers, to shape their use of GAI. Finally, while this study focused on personality traits as predictors of GAI attitudes and misconduct, other factors, such as institutional policies, cultural norms, and ethical training, may also play a significant role in shaping GAI-related behaviors. Future research could examine these contextual factors and explore their interaction with personality traits to provide a more holistic understanding of the factors influencing ethical decision-making in the context of emerging technologies. Declarations Author contributions Haiying Liang: Conceptualization, Methodology, Formal analysis, Writing – original draft Xu Mao: Data curation, Investigation Michael Reiss: Writing – review & editing Conflict of Interest Statement The authors declare no conflict of interest. Acknowledgements We would like to express our gratitude to the students who participated in completing the questionnaires and those who helped distribute them. Conflict of Interest Statement The authors declare no conflict of interest. Acknowledgements We would like to express our gratitude to the students who participated in completing the questionnaires and those who helped distribute them. Data availability Data will be deposited to Open Science Framework upon acceptance of the article. The point of contact is Haiying Liang. Funding This research received no external funding. References Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1 Yu, H. & Guo, Y. (2023). Generative artificial intelligence empowers educational reform: current status, issues, and prospects. In Frontiers in Education (Vol. 8, p. 1183162). Frontiers Media SA. https://doi.org/10.3389/feduc.2023.1183162 Schepman, A. & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput. Hum. Behav. Rep., 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014 Sindermann, C., Riedl, R. & Montag, C. (2020). Investigating the relationship between personality and technology acceptance with a focus on the smartphone from a gender perspective: results of an exploratory survey study. Future Internet, 12(7), 110. https://doi.org/10.3390/fi12070110 Stein, J. P. et al. (2024). Attitudes towards AI: measurement and associations with personality. Scientific Reports, 14(1), 2909. https://doi.org/10.1038/s41598-024-53335-2 Kim, S. W. & Lee, Y. (2024). Investigation into the influence of socio-cultural factors on attitudes toward artificial intelligence. Education and Information Technologies, 29(8), 9907-9935 https://doi.org/10.1007/s10639-023-12172-y Li, S., Glass, R. & Records, H. (2008). The influence of gender on new technology adoption and use–mobile commerce. Journal of Internet Commerce, 7(2), 270-289. https://doi.org/10.1080/15332860802067748 Méndez-Suárez, M. et al. (2023). Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance. European Research on Management and Business Economics, 29(3), 100223. https://doi.org/10.1016/j.iedeen.2023.100223 Morris, M. G. & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375-403. https://doi.org/10.1111/j.1744-6570.2000.tb00206.x Antes, A. L. et al. (2007). Personality and ethical decision-making in research: The role of perceptions of self and others. Journal of Empirical Research on Human Research Ethics, 2(4), 15-34. https://doi.org/10.1525/jer.2007.2.4.15 Wang, Y. Y., Wang, Y. S. & Wang, Y. M. (2022). What drives students’ Internet ethical behaviour: an integrated model of the theory of planned behaviour, personality, and Internet ethics education. Behaviour & Information Technology, 41(3), 588-610. https://doi.org/10.1080/0144929x.2020.1829053 Kaya, F. et al. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730 Kovbasiuk, A. et al. (2025). The personality profile of early generative AI adopters: a big five perspective. Central European Management Journal, 33(2), 252-264. https://doi.org/10.1108/cemj-02-2024-0067 Jonason, P. K. & Sherman, R. A. (2020). Personality and the perception of situations: The Big Five and Dark Triad traits. Personality and Individual Differences, 163, 110081. https://doi.org/10.1016/j.paid.2020.110081 Svendsen, G. B. et al. (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 Sun, R. et al. (2025). The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables. Frontiers in Education, 10, 1551721. https://doi.org/10.3389/feduc.2025.1551721 Aplin-Houtz, M. J. et al. (2024). Tales from the dark side of technology acceptance: the Dark Triad and the technology acceptance model. Employee Responsibilities and Rights Journal, 36(4), 421-453. https://doi.org/10.1007/s10672-023-09453-6 Jabłońska, M. R. & Zajdel, R. (2020). The Dark Triad Traits and problematic Internet use: Their structure and relations. Polish Sociological Review, 212(4), 477-496. https://doi.org/10.1186/s40359-021-00668-6 Rahman, M. S. & Muldoon, J. (2020). Dark side of technology: Investigating the role of dark personality traits and technological factors in managing cyberloafing behavior. Journal of Strategic Innovation and Sustainability, 15(3), 36-54. https://doi.org/10.33423/jsis.v15i3.2947 Ashton, M. C. & Lee, K. (2009). The HEXACO–60: a short measure of the major dimensions of personality. J. Pers. Assess. 91, 340–345. https://doi.org/10.1080/00223890902935878 Lee, K. & Ashton, M. C. (2004). Psychometric properties of the HEXACO personality inventory. Multivariate Behavioral Research, 39(2), 329-358. https://doi.org/10.1207/s15327906mbr3902_8 Boies, K. et al. (2004). Validity studies psychometric properties of scores on the French and Korean versions of the Hexaco personality inventory. Educational and Psychological Measurement, 64(6), 992-1006. https://doi.org/10.1177/0013164404267277 De Vries, R. E., Lee, K. & Ashton, M. C. (2008). The Dutch HEXACO Personality Inventory: Psychometric properties, self–other agreement, and relations with psychopathy among low and high acquaintanceship dyads. Journal of Personality Assessment, 90(2), 142-151. https://doi.org/10.1080/00223890701845195 Kline, R. et al. (2019). Personality and prosocial behavior: A multilevel meta-analysis. Political Science Research and Methods, 7(1), 125-142. https://doi.org/10.1017/psrm.2017.14 Greitemeyer, T., & Kastenmüller, A. (2023). HEXACO, the Dark Triad, and Chat GPT: Who is willing to commit academic cheating? Heliyon, 9(9). https://doi.org/10.1016/j.heliyon.2023.e19909 Pahl, K. (2009). Emotionality: A brief introduction. MLN, 124(3), 547-554. https://doi.org/10.1353/mln.0.0147 Weller, J. A. & Tikir, A. (2011). Predicting domain‐specific risk taking with the HEXACO personality structure. Journal of Behavioral Decision Making, 24(2), 180-201. https://doi.org/10.1002/bdm.677 Sindermann, C. et al. (2020). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. Künstliche Intelligenz, 35(1), 109–118. https://doi.org/10.1007/s13218-020-00689-0 Bano, S., Shah, U. U. & Ali, S. (2019). Personality and technology: Big five personality traits as descriptors of universal acceptance and usage of technology UTAUT. Library Philosophy and Practice, 1-22. https://doi.org/10.7717/peerj-cs.1498/fig-3 Thielmann, I., Spadaro, G. & Balliet, D. (2020). Personality and prosocial behavior: A theoretical framework and meta-analysis. Psychological Bulletin, 146(1), 30. https://doi.org/10.1037/bul0000217 Dangi, M. R. M. & Saat, M. M. (2021). Interaction effects of situational context on the acceptance behaviour and the conscientiousness trait towards intention to adopt. Educational Technology & Society, 24(3), 61-84. https://doi.org/10.6007/ijarbss/v11-i1/8288 Qu, K. & Wu, X. (2024). ChatGPT as a CALL tool in language education: A study of hedonic motivation adoption models in English learning environments. Education and Information Technologies, 29(15), 19471-19503. https://doi.org/10.1007/s10639-024-12598-y Javed, B. et al. (2020). Openness to experience, ethical leadership, and innovative work behavior. The Journal of Creative Behavior, 54(1), 211-223. https://doi.org/10.1002/jocb.360 Paulhus, D. L. & Williams, K. M. (2002). The dark triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36(6), 556-563. https://doi.org/10.1016/s0092-6566(02)00505-6 Jonason, P. K. & Webster, G. D. (2010). The dirty dozen: a concise measure of the dark triad. Psychological Assessment, 22(2), 420. https://doi.org/10.1037/a0019265 Moshagen, M., Hilbig, B. E. & Zettler, I. (2018). The dark core of personality. Psychological Review, 125(5), 656 https://doi.org/10.1037/rev0000111 Bereczkei, T. & Birkas, B. (2014). The insightful manipulator: Machiavellians' interpersonal tactics may be linked to their superior information processing skills. International Journal of Psychological Studies, 6(4), 65. https://doi.org/10.5539/ijps.v6n4p65 Ináncsi, T. et al. (2018). Perceptions of close relationship through the Machiavellians dark glasses: Negativity, distrust, self-protection against risk and dissatisfaction. Europe's Journal of Psychology, 14(4), 806. https://doi.org/10.5964/ejop.v14i4.1550 Lo, C. F. & Ng, E. C. B. (2019). Machiavellianism and intimacy attitudes in the interpersonal relationships. Psychology, 10(04), 424. https://doi.org/10.4236/psych.2019.104029 Rauthmann, J. F. (2012). The Dark Triad and interpersonal perception: Similarities and differences in the social consequences of narcissism, Machiavellianism, and psychopathy. Social Psychological and Personality Science, 3(4), 487-496. https://doi.org/10.1177/1948550611427608 Song, J. & Liu, S. (2025). Dark personality traits are associated with academic misconduct, frustration, negative thinking, and generative AI use habits: the case of Sichuan art universities. BMC Psychology, 13(1), 633. https://doi.org/10.1186/s40359-025-02949-w Avelino, B. C., de Lima, G. A. S. F., da Cunha, J. V. A. & Colauto, R. D. (2017). The influence of narcissism in the professional environment: Aspects related to dishonesty. Advances in Scientific and Applied Accounting, 334-356. https://doi.org/10.4270/ruc.2017319 Ajzen, I. (2014). Attitude structure and behavior. In Attitude structure and function (pp. 241-274). Psychology Press. https://doi.org/10.4324/9781315801780-15 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T Stöber, J. (2001). The Social Desirability Scale-17 (SDS-17): Convergent validity, discriminant validity, and relationship with age. Eur. J. Psychol. Assess., 17(3), 222–232. https://doi.org/10.1027/1015-5759.17.3.222 Jones, D. N. & Paulhus, D. L. (2014). Introducing the Short Dark Triad (SD3): A brief measure of dark personality traits. Assessment, 21(1), 28–41. https://doi.org/10.1177/1073191113514105 Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited. https://doi.org/10.1024/1012-5302/a000397 Aluja, A., Garcıa, O. & Garcıa, L. F. (2003). Relationships among extraversion, openness to experience, and sensation seeking. Personality and Individual Differences, 35(3), 671-680. https://doi.org/10.1016/s0191-8869(02)00244-1 Bowden-Green, T., Hinds, J. & Joinson, A. (2020). How is extraversion related to social media use? A literature review. Personality and Individual Differences, 164, 110040. https://doi.org/10.1016/j.paid.2020.110040 Chakraborty, D. (2025). Psychological Dispositions and AI Utilization: Unpacking Narcissism, Machiavellianism, and Psychopathy in E-commerce. Journal of Promotion Management, 31(4), 549-577. https://doi.org/10.1080/10496491.2025.2484714 Anwar, S. & Shah, N. (2017). Impact of personality traits on ethical behavior. The Government-Annual Research Journal of Political Science, 6, 95-114. https://doi.org/10.4135/9781483391144.n265 Giluk, T. L. & Postlethwaite, B. E. (2015). Big Five personality and academic dishonesty: A meta-analytic review. Personality and Individual Differences, 72, 59-67. https://doi.org/10.1016/j.paid.2014.08.027 Simha, A. & Parboteeah, K. P. (2020). The big 5 personality traits and willingness to justify unethical behavior—a cross-national examination. Journal of Business Ethics, 167(3), 451-471. https://doi.org/10.1007/s10551-019-04142-7 Brunell, A. B., Staats, S., Barden, J. & Hupp, J. M. (2011). Narcissism and academic dishonesty: The exhibitionism dimension and the lack of guilt. Personality and Individual Differences, 50(3), 323-328. https://doi.org/10.1016/j.paid.2010.10.006 Den Hartog, D. N. & Belschak, F. D. (2012). Work engagement and Machiavellianism in the ethical leadership process. Journal of Business Ethics, 107(1), 35-47. https://doi.org/10.1007/s10551-012-1296-4 Harrison, A., Summers, J. & Mennecke, B. (2018). The effects of the dark triad on unethical behavior. Journal of Business Ethics, 153(1), 53-77. https://doi.org/10.1007/s10551-016-3368-3 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 25 Aug, 2025 Editor assigned by journal 25 Aug, 2025 Editor invited by journal 21 Aug, 2025 Submission checks completed at journal 14 Aug, 2025 First submitted to journal 14 Aug, 2025 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-7356639","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508342832,"identity":"e40afaf1-a170-49e8-99b4-5e616334582b","order_by":0,"name":"Haiying Liang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Haiying","middleName":"","lastName":"Liang","suffix":""},{"id":508342833,"identity":"600c0153-dfb1-43b0-ac53-ee27f2f16b1f","order_by":1,"name":"Xu Mao","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Mao","suffix":""},{"id":508342834,"identity":"9998de25-0465-4389-a626-7d82b8ebc76a","order_by":2,"name":"Michael J. Reiss","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYLCCBAYGOQYG5gYwR4JYLcYMDIykaAGCxAaiteg28D588OBPXfqG4wcbGH7UMCTObCCgxewAu7FBYtvh3A1nEhsYe44xJM4mZIvZATY2icSGA7kbbgAdxtvAkDiPCC3sPxKADjMAamH8S6QWNoYENuYEkBZmkC2EHXaYjVkC6BfDmUC/HJY5JmFM2PvH2xg//vhTJ893/PDBh29qbGRnHCBkDTMS+wAJETkKRsEoGAWjAB8AAAb2PxzsrWsYAAAAAElFTkSuQmCC","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"J.","lastName":"Reiss","suffix":""}],"badges":[],"createdAt":"2025-08-12 14:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7356639/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7356639/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-25744-4","type":"published","date":"2025-11-25T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97179023,"identity":"d12e1a2c-2dbb-483b-88d5-7d6146d5bdea","added_by":"auto","created_at":"2025-12-01 16:14:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7356639/v1/1f87e5c5-82d0-417c-a5df-3e709e68fad3.pdf"},{"id":90414586,"identity":"941c4e18-c695-42aa-88e6-5a3681365538","added_by":"auto","created_at":"2025-09-02 13:01:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":29629,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7356639/v1/7ef2dbdd45b4c9f5ede49687.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personality Predictors of Attitudes and Misconduct Behaviors Related to Generative Artificial Intelligence: Evidence from the HEXACO and Dark Triad Models","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid proliferation of generative artificial intelligence (GAI) technologies is fundamentally transforming the way people engage with information. As a specialized subfield of artificial intelligence (AI), GAI is distinguished by its capacity to autonomously generate coherent and contextually relevant content\u003csup\u003e1\u003c/sup\u003e. As GAI becomes increasingly integrated into students’ academic practices—including literature reviews, manuscript drafting, code generation, and automated feedback\u003csup\u003e2\u003c/sup\u003e—it is vital to understand how they perceive and use these tools. Such inquiry is essential not only for educators and policymakers but also for guiding the ethical integration of GAI into scholarly practices. However, rigorous investigation into these attitudes depends on the availability of psychometrically robust, theoretically grounded instruments specifically tailored to GAI. While several instruments have been developed to assess general attitudes toward AI—such as the General Attitudes Toward Artificial Intelligence Scale\u003csup\u003e3\u003c/sup\u003e, the Attitudes Toward Artificial Intelligence Scale\u003csup\u003e4\u003c/sup\u003e, and the more recent\u0026nbsp;Attitudes Towards AI\u0026nbsp;scale-12 by Stein et al.\u003csup\u003e5\u003c/sup\u003e—these tools typically treat AI as a broad and undifferentiated construct, without differentiating the specific characteristics of GAI. Furthermore, few of these instruments were developed within academic contexts, and most do not capture the tripartite structure of attitude (cognitive, affective, behavioral) foundational in social psychology. Additionally, existing measures are almost exclusively developed in English, limiting their applicability across linguistic and cultural boundaries.\u003c/p\u003e\n\u003cp\u003eTo address these gaps, the first objective of this study was to develop and validate the GAI attitudes scale—a concise Chinese-language instrument that assesses students’ attitudes toward GAI specifically within academic contexts. The scale is designed to capture both positive and negative evaluations of GAI, grounded in the tripartite model of attitude, and to conceptualize GAI as a general set of technological affordances rather than as specific platforms (e.g., ChatGPT or Midjourney). This abstraction allows for broader applicability across disciplines and reduces the influence of transient technological branding on participants’ evaluations.\u003c/p\u003e\n\u003cp\u003eAccordingly, the first research objective was:\u003cem\u003e\u0026nbsp;to construct and validate a psychometric instrument that captures students’ attitudes toward GAI within academic settings.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhile prior research on technology adoption has largely focused on demographic and sociocultural factors—such as age, gender, education level, and media exposure\u003csup\u003e6,7,8,9\u003c/sup\u003e—the role of stable psychological dispositions, particularly personality traits, remains largely understudied. Existing evidence suggests that personality may meaningfully shape attitudes toward technology, including both acceptance and ethical decision-making\u003csup\u003e10,11\u003c/sup\u003e. However, most prior studies have examined general AI or physically embodied AI (e.g., robots)\u003csup\u003e12,13\u003c/sup\u003e, with relatively few investigating GAI in academic settings.\u003c/p\u003e\n\u003cp\u003ePrevious research has primarily relied on the Big Five personality model to examine technology acceptance\u003csup\u003e5,14,15,16\u003c/sup\u003e. While the Big Five model offers valuable insights into broad dispositional tendencies, it may fall short in capturing morally relevant personality dimensions when measuring attitudes toward AI. As Stein et al.\u003csup\u003e5\u003c/sup\u003e suggest, the HEXACO Personality Inventory may provide more nuanced explanatory power, particularly through its inclusion of the Honesty-Humility dimension—a trait strongly linked to ethical decision-making and adherence to rules.\u003c/p\u003e\n\u003cp\u003eWhile the Dark Triad traits (\u003cstrong\u003eMachiavellianism\u003c/strong\u003e, \u003cstrong\u003ePsychopathy and Narcissism\u003c/strong\u003e) have been applied in studies of technology abuse\u003csup\u003e17,18,19\u003c/sup\u003e, their use in the context of GAI-related misconduct remains limited.\u003c/p\u003e\n\u003cp\u003eAccordingly, the second objective of this study was:\u003cem\u003e\u0026nbsp;to examine the predictive roles of personality—assessed via the HEXACO Personality Inventory and the Dark Triad traits—in shaping students’ attitudes toward GAI and their engagement in GAI-related misconduct.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research seeks to identify which psychological profiles are more likely to embrace or misuse generative AI tools. Understanding these associations can contribute to the development of targeted interventions, ethical training, and evidence-based policy recommendations, improving the responsible use of GAI in education and scholarly communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of studies and theoretical predictions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the two overarching research objectives, we conducted two empirical studies. Study 1 focuses on the development and validation of a novel instrument designed to measure students’ attitudes toward GAI in academic settings. Building upon and adapting the ATTARI-12 framework\u003csup\u003e5\u003c/sup\u003e, this study evaluated the psychometric properties of the new scale, including its internal consistency, test–retest reliability, and convergent validity. Additionally, we examined the potential influence of social desirability bias on self-reported GAI attitudes. These initial validation studies were critical for ensuring that the instrument accurately captures both the positive and negative dimensions of GAI evaluation within a research context. Study 2 extended this work by investigating how university students’ attitudes toward GAI and GAI misconduct behaviors relate to individual differences in personality measured through HEXACO and the Personality Inventory and the Dark Triad traits.\u003c/p\u003e\n\u003cp\u003eAccordingly, we formulated a series of hypotheses regarding how these personality dimensions may predict both attitudes towards GAI and engagement in GAI misconduct, as follows.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe HEXACO Personality Inventory\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe HEXACO Personality Inventory is a widely used model in personality psychology, measuring six major traits: Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience\u003csup\u003e20,21\u003c/sup\u003e. This model has shown strong reliability and validity across various cultural contexts and is useful for understanding human behaviors\u003csup\u003e22,23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHonesty–Humility\u003c/em\u003e, a core dimension of the HEXACO personality model, encompasses traits such as sincerity, fairness, and moral restraint. Individuals scoring high in this dimension are characterized by their reluctance to manipulate others for personal gain and their commitment to ethical behavior\u003csup\u003e21\u003c/sup\u003e. This trait has been shown to significantly influence ethical decision-making and pro-social behaviors\u003csup\u003e24\u003c/sup\u003e. In the context of GAI, researchers with high Honesty–Humility are expected to adopt more cautious attitudes toward GAI use. This expectation is grounded in evidence suggesting that individuals high in Honesty–Humility are less likely to engage in unethical behaviors\u003csup\u003e25\u003c/sup\u003e. Furthermore, these individuals are anticipated to hold more negative attitudes toward GAI misconduct behaviors. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e Students higher in Honesty–Humility will report more negative attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e Students higher in Honesty–Humility will report lower likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEmotionality\u003c/em\u003e encompasses traits such as anxiety, fearfulness, dependence, and sentimentality\u003csup\u003e20,21\u003c/sup\u003e. Individuals with high Emotionality tend to experience heightened sensitivity to stress and a strong need for emotional support from others\u003csup\u003e26\u003c/sup\u003e. This heightened sensitivity may lead them to perceive greater risks and potential negative outcomes associated with GAI technologies. Consequently, they may exhibit more cautious attitudes toward GAI use, driven by concerns about potential misuse or unintended consequences. Research has shown that individuals high in Emotionality are more likely to avoid risky behaviors and seek reassurance in uncertain situations\u003csup\u003e27\u003c/sup\u003e. This tendency suggests that such individuals may be less inclined to engage in unethical practices involving GAI due to fears of detection and feelings of guilt. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e Students higher in Emotionality will report more negative attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e Students higher in Emotionality will report lower likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExtraversion\u003c/em\u003e, characterized by sociability, enthusiasm, and assertiveness, is often associated with openness to innovation and experimentation\u003csup\u003e20,21\u003c/sup\u003e. Individuals high in extraversion are generally more willing to engage with new technologies, including GAI, due to their greater comfort in social interactions and openness to new experiences5\u003csup\u003e,28\u003c/sup\u003e. This inclination may lead them to adopt GAI more readily, especially in environments where such technologies are perceived as enhancing performance or are socially accepted. However, this same openness can also increase the likelihood of engaging in misconduct if the use of GAI is seen as socially tolerated or advantageous. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e Students higher in Extraversion will report more positive attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH6\u003c/strong\u003e Students higher in Extraversion will report higher likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAgreeableness\u003c/em\u003e is characterized by traits such as empathy, cooperativeness, and trustworthiness\u003csup\u003e20,21\u003c/sup\u003e. Individuals high in agreeableness are more likely to engage in behaviors that promote social harmony and are less inclined to act unethically\u003csup\u003e15,29\u003c/sup\u003e. This tendency extends to their interactions with technology, where agreeable individuals may favor technologies that align with ethical standards and societal well-being. Research indicates that agreeableness is positively associated with prosocial behaviors and moral decision-making, suggesting that agreeable individuals are more likely to adopt technologies like GAI in ways that are ethically sound and socially responsible\u003csup\u003e24\u003c/sup\u003e. Furthermore, agreeableness has been linked to lower tendencies toward unethical behavior. Studies have shown that individuals high in agreeableness are less likely to engage in deviant behaviors, including those involving technology misuse\u003csup\u003e15,30\u003c/sup\u003e. This suggests that researchers with higher levels of agreeableness may be less inclined to misuse GAI and more likely to uphold ethical standards in their use of such technologies. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH7\u003c/strong\u003e Students higher in Agreeableness will report more positive attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH8\u003c/strong\u003e Students higher in Agreeableness will report lower likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConscientiousness\u003c/em\u003e is characterized by traits such as diligence, self-discipline, and ethical responsibility\u003csup\u003e20,21\u003c/sup\u003e. Highly conscientious individuals tend to be cautious and deliberate in their decision-making processes, often exhibiting skepticism toward technologies\u003csup\u003e15,31\u003c/sup\u003e. This skepticism arises from their preference for structured environments and adherence to established norms, leading them to critically assess the potential risks and implications of adopting new technologies\u003csup\u003e24\u003c/sup\u003e. Moreover, conscientious individuals are less likely to engage in misconduct related to GAI because their strong sense of duty and moral responsibility fosters adherence to ethical standards, reducing the likelihood of participating in activities such as academic dishonesty or misuse of AI-generated content\u003csup\u003e25\u003c/sup\u003e. Based on these considerations, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH9\u003c/strong\u003e Students higher in Conscientiousness will report more negative attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH10\u003c/strong\u003e Students higher in Conscientiousness will report lower likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOpenness to Experience\u003c/em\u003e is characterized by traits such as intellectual curiosity, creativity, and a preference for novelty\u003csup\u003e20,21\u003c/sup\u003e. Individuals high in Openness to Experience are typically more willing to explore and adopt innovative technologies\u003csup\u003e32\u003c/sup\u003e. This openness may lead to more positive attitudes toward GAI use, as these individuals are generally more accepting of technological advancements. However, Openness to Experience is not directly associated with unethical intent\u003csup\u003e33\u003c/sup\u003e. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH11\u003c/strong\u003e Students higher in Openness to Experience will report more positive attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH12\u003c/strong\u003e Students higher in Openness to Experience will not predict GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Dark Triad\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Dark Triad refers to a cluster of three interrelated but distinct personality traits—\u003cstrong\u003eMachiavellianism\u003c/strong\u003e, \u003cstrong\u003ePsychopathy, and Narcissism\u003c/strong\u003e—that are characterized by self-serving, manipulative, and often callous behaviors\u003csup\u003e34\u003c/sup\u003e. Coined by Paulhus and Williams\u003csup\u003e32\u003c/sup\u003e, the Dark Triad has become a central construct in personality psychology, particularly in understanding antisocial and socially aversive behaviors\u003csup\u003e32,35,36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMachiavellianism\u003c/em\u003e is characterized by manipulativeness, strategic self-interest, and a lack of morality\u003csup\u003e32\u003c/sup\u003e. Individuals exhibiting high levels of Machiavellianism tend to view interpersonal relationships as opportunities for exploitation, often employing deceitful tactics to achieve personal goals\u003csup\u003e37,38,39\u003c/sup\u003e. In the context of academic research, individuals high in Machiavellianism may be more inclined to exploit GAI technologies for competitive advantage, regardless of ethical considerations. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH13\u003c/strong\u003e Students higher in Machiavellianism will report more positive attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH14\u003c/strong\u003e Students higher in Machiavellianism will report a higher likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePsychopathy\u003c/em\u003e is characterized by impulsivity, low empathy, and a propensity for unethical behavior\u003csup\u003e32\u003c/sup\u003e. Individuals exhibiting high levels of psychopathy often display a lack of remorse, shallow affect, and a disregard for the impact of their actions on others\u003csup\u003e32,40\u003c/sup\u003e. These traits may contribute to ethical indifference and a greater comfort with rule-breaking, particularly in contexts where personal gain is perceived. In the realm of academic research, such individuals may be more inclined to exploit GAI technologies for competitive advantage, irrespective of ethical considerations. Their impulsive nature and focus on self-interest can lead to a higher likelihood of engaging in misconduct behaviors involving GAI. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH15\u003c/strong\u003e Students higher in psychopathy will report more positive attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH16\u003c/strong\u003e Students higher in psychopathy will report a higher likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNarcissism\u003c/em\u003e is characterized by grandiosity, a need for admiration, and a lack of empathy\u003csup\u003e32\u003c/sup\u003e. Individuals exhibiting high levels of narcissism often engage in self-enhancing behaviors and seek recognition, sometimes at the expense of ethical considerations. In academic contexts, such traits may drive researchers to utilize GAI technologies to polish their outputs or gain recognition, even through misconduct\u003csup\u003e41\u003c/sup\u003e. This inclination is supported by studies indicating that narcissism is positively correlated with academic dishonesty and unethical behavior\u003csup\u003e42\u003c/sup\u003e. Therefore, it is hypothesized that:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH17\u003c/strong\u003e Students higher in narcissism will report more positive attitudes toward GAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH18\u003c/strong\u003e Students higher in narcissism will report a higher likelihood of engaging in GAI misconduct behaviors.\u003c/p\u003e"},{"header":"Study 1","content":"\u003cp\u003eThe primary aim of Study 1 was to develop a psychometrically robust scale specifically designed to measure students\u0026rsquo; attitudes toward the use of GAI in academic contexts. The scale development process was guided by three core principles: (a) the scale should be unidimensional to enable clear interpretation of overall attitude scores; (b) it should incorporate items representing the three classic components of attitudes in psychology\u0026mdash;cognitive, affective, and behavioral; and (c) it should contain both positively and negatively worded items to capture the full evaluative spectrum, while mitigating agreement bias.\u003c/p\u003e\n\u003cp\u003eGrounded in social psychological theories of attitude structure\u003csup\u003e43\u003c/sup\u003e and informed by existing general AI attitude measures\u003csup\u003e3,5\u003c/sup\u003e, we initially generated 24 items. Item formulation aimed at achieving balance in both evaluative direction (12 positive, 12 negative) and attitudinal facet representation (8 items per facet, across valences). Given the context-specific nature of our research, all items were preceded by a standardized introductory instruction that briefly defined GAI and situated its use in academic research (e.g., literature review, writing, analysis), thereby minimizing semantic ambiguity and ensuring that participants interpreted the attitude object consistently. This instruction was considered an integral component of the measurement tool.\u003c/p\u003e\n\u003cp\u003eIn the second stage, a panel of three researchers\u0026mdash;whose expertise covered educational assessment, psychology, and educational technology\u0026mdash;reviewed the initial item pool. Items were revised or eliminated if they were semantically redundant, too contextually narrow, or ambiguous in focus. This refinement process resulted in a 12-item scale with each attitudinal component (cognitive, affective, behavioral) represented by four items: two positively and two negatively worded. Although the items span distinct psychological dimensions, they were theorized to load onto a single latent factor reflecting an individual\u0026rsquo;s general attitude toward the use of GAI in academic work.\u003c/p\u003e\n\u003cp\u003eTo assess construct validity, Study 1 also included measures of participants\u0026rsquo; intention to use GAI and actual use of GAI in academic contexts. We expected that general attitudes measured by the GAI attitudes scale would correlate positively with their intention and actual use of GAI, based on Theory of Planned Behavior\u003csup\u003e44\u003c/sup\u003e. Additionally, to evaluate potential susceptibility to social desirability bias, we included a short-form social desirability scale\u003csup\u003e45\u003c/sup\u003e. Given the careful phrasing and balanced item valence, we hypothesized that GAI attitudes scores would not be significantly associated with socially desirable responding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics Statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research received ethical approval from Peking University Institutional Review Board. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all participants prior to their participation in the study, and they were assured of their anonymity and the voluntary nature of their involvement.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants and Procedure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure sufficient statistical power for scale validation and subsequent correlational analyses, a priori power analysis conducted using semPower (Version 2.0.1) indicated that a minimum sample size of 500 participants was necessary.\u003c/p\u003e\n\u003cp\u003eA total of 625 participants, students from five universities in China, were recruited via Wenjuanxing website, which is a popular website for collecting survey responses in China. Incentives of 5 RMB were provided for each completed questionnaire. The average completion time was approximately 4 minutes. Exclusion criteria were preregistered and applied rigorously. These included completion time under 120 seconds, failure to pass at least one of two attention checks. Based on these criteria, 78 participants were excluded (31 for completion time, 47 for failing the description task), yielding a final sample of 547 participants (279 female, 268 male). Participants ranged in age from 20 to 35 years.\u003c/p\u003e\n\u003cp\u003eAfter providing informed consent, participants were first asked to create a unique anonymous identifier by combining two elements only they would know\u0026mdash;for example, the name of an elementary school teacher and the month of their birth. This identifier could not be traced back to participants\u0026rsquo; identities by the researchers but allowed for accurate matching in the follow-up test\u0026ndash;retest reliability assessment.\u003c/p\u003e\n\u003cp\u003eParticipants first answered demographic questions, followed by an attention check question. Next, they completed the GAI Attitudes Scale, which assessed their attitudes toward the use of GAI in academic contexts. They then responded to measures evaluating their intention to use GAI, actual GAI usage, a social desirability scale, and a final attention check in the form of a summary question.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGAI attitudes scale\u003c/em\u003e\u003cbr\u003e\u0026nbsp;We administered the newly developed GAI attitudes scale to assess participants\u0026rsquo; attitudes toward GAI in academic settings. The scale includes 12 items representing the cognitive, affective, and behavioral components of attitudes, each balanced with positive and negative wording. Responses were recorded using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). Psychometric properties and descriptive statistics are reported in the Results section.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBehavioral intention to use GAI in academic contexts\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Participants\u0026rsquo; intention to use generative AI in academic settings was measured with a single-item indicator adapted for this study. The item asked: \u0026ldquo;To what extent would you like to use generative AI (e.g., ChatGPT, Claude, Gemini) in your academic work (e.g., research, writing, teaching)?\u0026rdquo; Responses were recorded on a 5-point Likert scale ranging from 1 (Not at all) to 5 (Very strongly intend to).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eActual use of GAI in academic settings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess participants\u0026rsquo; current engagement with generative AI tools in academic domains, a single item was used: \u0026ldquo;How frequently do you currently use generative AI tools (e.g., ChatGPT, Claude, Gemini) in your academic work (e.g., writing papers, preparing lectures, analyzing data)?\u0026rdquo; Responses were measured on a 5-point scale ranging from 1 (Never) to 5 (Very frequently).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSocial desirability\u003c/em\u003e\u003cbr\u003eTo assess potential response bias, we included a 17-item Social Desirability Scale\u003csup\u003e44\u003c/sup\u003e. Participants responded to whether a set of socially desirable or undesirable behaviors described them (true/false format). Scores ranged from 0 to 17, with higher scores indicating greater tendency toward socially desirable responding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the factorial validity of the scale based on the assumption that all items continue to reflect a single underlying construct\u0026mdash;students\u0026rsquo; attitudes toward GAI. To evaluate this, we conducted a confirmatory factor analysis comparing a series of models with progressively fewer constraints. As shown in Table 1, among the tested models, the bifactor S-1 model with content facets (Model b) demonstrated the best overall fit. It significantly outperformed the single-factor model (Model a), as indicated by the chi-square difference test (\u0026Delta;\u0026chi;\u0026sup2;(8) = 20.02, p = .010), and showed improved CFI, RMSEA, SRMR, and lower AIC/BIC values. While the full model including both content and wording factors (Model d) had slightly better absolute fit indices (e.g., lowest RMSEA and SRMR), the improvement over Model (b) was not statistically significant (p = .236) and came at the cost of model complexity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eGoodness of fit for competing confirmatory factor models for the GAI attitudes scale.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eComp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026Delta;\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026Delta;df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eSingle factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e66.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15574.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15642.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" valign=\"top\" style=\"width: 652px;\"\u003e\n \u003cp\u003eBifactor S-1 models with one global factor and orthogonal specific factors for \u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e(b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eContent facets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e46.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15568.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15664.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e20.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e(c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eItem wording\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e60.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15571.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15659.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e(d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eContent facets and item wording\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e38.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15567.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15679.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e21.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs shown in Table 2, all 12 items of the GAI Attitudes Scale demonstrated moderate to strong standardized loadings on the general factor (range = .58 to .72), supporting the presence of a common underlying construct\u0026mdash;attitudes toward GAI in academic contexts. Items from the cognitive (Items 1\u0026ndash;4) and affective (Items 5\u0026ndash;8) subdomains also showed meaningful loadings on their respective specific factors (range = .27 to .38), indicating content-specific variance beyond the general factor. Residual variances ranged from .30 to .49, and bifactor indices were computed to further evaluate the influence of multidimensionality. The general factor accounted for 86% of the common variance (ECV = 0.86), while the specific factors contributed only marginally. The omega hierarchical coefficient for the general factor was 0.92, indicating that the majority of the reliable variance in total scores can be attributed to the general construct\u0026mdash;students\u0026rsquo; overall attitudes toward GAI. Taken together, these results support the interpretation of the scale as an essentially unidimensional measure, justifying the use of a total score.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eFactor loading pattern for the GAI attitudes scale.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized factor loadings (on general factor)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized factor loadings (on specific factor)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.31 (Cognitive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.29 (Cognitive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.33 (Cognitive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.36 (Cognitive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.27 (Affective)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.34 (Affective)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.29 (Affective)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.38 (Affective)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"9\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"10\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"11\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003col start=\"12\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDescriptive statistics and reliability estimates for the study variables are presented in Table 3. The internal consistency of the GAI attitudes scale was excellent (Cronbach\u0026rsquo;s \u0026alpha; = .92). The distribution approximated a near-normal distribution (Skew = \u0026ndash;0.08, Kurtosis = 0.02). The social desirability scale demonstrated acceptable reliability (\u0026alpha; = .79). Participants reported high behavioral intention (M = 4.18, SD = 0.51) and actual (M = 4.18, SD = 0.50) use of GAI tools, with the two items showing high internal consistency when combined (\u0026alpha; = .93).\u003c/p\u003e\n\u003cp\u003ePearson correlation analyses revealed strong positive associations between attitudes toward GAI and both behavioral intention (r = .86, p \u0026lt; .001) and actual use (r = .85, p \u0026lt; .001). Intention and use were also highly correlated (r = .86, p \u0026lt; .001), supporting the convergent validity of the measures. Social desirability showed no significant correlation with GAI attitudes (r = \u0026ndash;.03, p = .468) or use (r = \u0026ndash;.05, p = .246), suggesting minimal response bias. Gender was weakly but significantly associated with attitudes (r = .12, p = .004), intention (r = .12, p = .005), and use (r = .10, p = .011), with males scoring slightly higher. Age and degree level were not significantly related to any GAI-related variables.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eDescriptive statistics and correlation analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eCronbach\u0026rsquo;s\u0026nbsp;\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSkew\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eKurt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003et(p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGAI attitudes scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e50.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eSocial Desirability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e13.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.03 (0.468)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGAI Intention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.86 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.053 (0.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGAI use behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.853 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.047 (0.246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.863 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e37.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e10.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.04 (0.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.026 (0.518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.018 (0.657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.06 (0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.117 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.016 (0.697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.115 (0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.103 (0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.006 (0.879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.007 (0.868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.008 (0.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.032 (0.436)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.033 (0.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.036 (0.382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.014 (0.724)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eTest\u0026ndash;retest reliability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn order to further evaluate the reliability of the GAI attitudes scale by assessing its test\u0026ndash;retest reliability, the scale was administered to the same participants\u0026mdash;postgraduate students from five universities in China\u0026mdash;a second time. The instructors at these universities were re-contacted to help administer the second round of the survey to the same groups of students. A total of 383 participants completed the GAI attitudes scale for a second time, allowing for the assessment of test\u0026ndash;retest reliability. The survey was conducted via the Wenjuanxing platform, and responses were matched using the unique anonymous identifiers provided by participants. Of the 383 responses, 381 could be matched to the initial survey. Among these, 3 responses were excluded due to incompleteness, resulting in 378 valid cases for the test\u0026ndash;retest reliability analysis.\u003c/p\u003e\n\u003cp\u003eThe internal consistency reliability of the GAI attitudes scale was also high this time (Cronbach\u0026rsquo;s \u0026alpha; = 0.912), indicating strong scale coherence over time. Descriptive statistics confirmed that the distribution of scores approximated normality, with acceptable levels of skewness (\u0026ndash;0.319) and kurtosis (0.24). GAI attitudes scale scores followed a reasonably symmetrical and mesokurtic distribution at both measurement points, consistent with expectations for a psychometrically sound scale. Most importantly, the test\u0026ndash;retest reliability was strong, with a Pearson correlation of r(378) = 0.856, p \u0026lt; 0.001, demonstrating that participants\u0026rsquo; attitudes toward GAI in academic contexts were highly stable over time.\u003c/p\u003e"},{"header":" Study 2","content":"\u003cp\u003eBuilding on the initial validation of the GAI attitudes scale in Study 1, Study 2 addressed the second core research objective: examining how individual differences in personality traits predict students\u0026rsquo; attitudes toward GAI in academic contexts, as well as their GAI-related misconduct behaviors. Specifically, this study focused on two complementary personality frameworks\u0026mdash;the HEXACO Personality Inventory and the Dark Triad traits. To promote transparency, we preregistered Study 2 prior to data collection, outlining all hypotheses and the intended analyses. (https://aspredicted.org/93ny-7qhg.pdf).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn a priori power analysis conducted using G*Power (assuming a small to moderate effect size of f\u0026sup2; = 0.08, with 80% power, \u0026alpha; = 0.05, and 12 predictors in a hierarchical linear regression) indicated that a minimum sample size of 234 participants was required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo ensure adequate power and allow for potential exclusions during data screening, we recruited a total of 1007 participants via the Wenjuanxing platform. Several quality control procedures were implemented during data screening to ensure data integrity and minimize the impact of careless or inattentive responding. First, the questionnaire consisted of 107 items, with most being 5-point Likert scale questions. The expected completion time was between 4 and 8 minutes, but the data shows that many participants completed the survey between 3-4 minutes. As a result, we set the cutoff time at 3 minutes, removing 99 responses. Additionally, two attention check questions (Q5 and Q107) were included to assess participant attentiveness. For Q5, which asked, \u0026ldquo;Which of the following is a fruit?\u0026rdquo;, responses that incorrectly answered option 1 (n = 4) and option 3 (n = 2) were excluded, resulting in 6 removals. For Q107, which asked, \u0026ldquo;What is the main theme of this questionnaire?\u0026rdquo;, incorrect responses (option 1, n = 30 and option 3, n = 8) led to the removal of 38 responses. In total, 143 responses were excluded, leaving a final sample of 864 valid responses for analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe final sample had a mean age of 23.1 years (SD = 2.92), and was composed of 558 females, 306 males. The distribution of participants\u0026rsquo; level of study is as follows: 50.3% (n = 434) were undergraduates, 38.5% (n = 333) were master\u0026rsquo;s students, and 11.2% (n = 97) were doctoral students. Among the five disciplinary categories, the highest number of participants were from the \u0026ldquo;Humanities and Social Sciences\u0026rdquo; category, with 417 participants, accounting for 48.3% of the sample. The second largest group was from the \u0026ldquo;Medical and Health Sciences\u0026rdquo; category, with 140 participants, making up 16.2% of the sample, while the \u0026ldquo;Business and Economics\u0026rdquo; category had the fewest participants, with 77, equating to 8.9%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll measures were administered using 5-point Likert-type response scales (1 = strongly disagree, 5 = strongly agree), unless otherwise specified.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHEXACO Personality Inventory\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; broad personality traits were measured using the 60-item HEXACO Personality Inventory \u0026ndash; Revised (HEXACO-60)\u003csup\u003e20\u003c/sup\u003e. This instrument assesses six core dimensions: Honesty\u0026ndash;Humility (e.g., \u0026ldquo;I wouldn\u0026rsquo;t use flattery to get a raise or promotion at work\u0026rdquo;), Emotionality (e.g., \u0026ldquo;I sometimes can\u0026rsquo;t help worrying about little things\u0026rdquo;), Extraversion (e.g., \u0026ldquo;I feel reasonably satisfied with myself overall\u0026rdquo;), Agreeableness (e.g., \u0026ldquo;People sometimes tell me that I am too critical of others\u0026rdquo;), Conscientiousness (e.g., \u0026ldquo;I plan ahead and organize things to avoid scrambling at the last minute\u0026rdquo;), and Openness to Experience (e.g., \u0026ldquo;I enjoy looking at maps of different places\u0026rdquo;). Participants responded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Internal consistency was acceptable to excellent across the six dimensions (Honesty\u0026ndash;Humility: \u0026alpha; = 0.92; Emotionality: \u0026alpha; = 0.91; Extraversion: \u0026alpha; = 0.84; Agreeableness: \u0026alpha; = 0.90; Conscientiousness: \u0026alpha; = 0.91; Openness: \u0026alpha; = 0.83).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDark Triad\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed participants\u0026rsquo; Dark Triad personality traits with the Short Dark Triad scale\u003csup\u003e46\u003c/sup\u003e. This instrument includes nine items on Machiavellianism (e.g., \u0026ldquo;It\u0026rsquo;s not wise to tell your secrets\u0026rdquo;), six items on psychopathy (e.g., \u0026ldquo;People often say I\u0026rsquo;m out of control\u0026rdquo;), and nine items on narcissism (e.g., \u0026ldquo;Many group activities tend to be dull without me\u0026rdquo;). Reliability analyses suggested good to very good internal consistencies for all three scales (Machiavellianism: Cronbach\u0026rsquo;s \u0026alpha; = 0.86; psychopathy: Cronbach\u0026rsquo;s \u0026alpha; = 0.83; narcissism: Cronbach\u0026rsquo;s \u0026alpha; = 0.83).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGAI attitudes Scale\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; attitudes toward GAI were assessed using the Chinese version of the newly developed GAI attitudes scale in Study 1. In the present sample (N = 864), the scale demonstrated excellent internal consistency (Cronbach\u0026rsquo;s \u0026alpha; = 0.85).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGAI Misconduct Behavior Scale\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we employed a GAI academic misconduct scale adapted from a previously validated instrument developed by Sun et al.\u003csup\u003e16\u003c/sup\u003e, designed to measure how frequently students engage in academic misconduct involving generative AI. To enhance its comprehensiveness, we added a new item to the original four-question scale: \u0026ldquo;I have used AI to answer in unauthorized exams or tests.\u0026rdquo; Therefore, the scale consists of five items, each evaluated on a 5-point Likert scale ranging from 1 (never) to 5 (always). In the present study, this instrument exhibited high internal reliability (Cronbach\u0026rsquo;s \u0026alpha; = 0.78). Confirmatory factor analysis supported its unidimensional structure, indicating good model fit (\u0026chi;2(2) = 4.083, p = 0.130; CFI = 0.976; RMSEA = 0.036; SRMR = 0.004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDescriptive statistics and correlations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents the descriptive statistics (means and standard deviations) and bivariate Pearson correlations among the study variables. The sample (N = 864) had a mean age of 23.14 years (SD = 2.92). Average scores on the HEXACO dimensions ranged from 2.71 (Neuroticism) to 3.45 (Extraversion), while the Dark Triad traits ranged from 2.44 (Psychopathy) to 2.99 (Narcissism). The mean score for attitudes toward GAI was 3.33 (SD = 0.79), and for GAI academic misconduct was 1.79 (SD = 0.80). Age was positively correlated with Disciplines (r = 0.11, p \u0026lt; .001), while Level of study showed significant negative associations with Gender (r = \u0026ndash;0.11, p \u0026lt; .001) and Disciplines (r = \u0026ndash;0.04, p \u0026lt; .05). Within the HEXACO traits, Honesty\u0026ndash;Humility was positively associated with Agreeableness (r = 0.18, p \u0026lt; .001), whereas Emotionality showed positive correlations with Openness to Experience (r = 0.15, p \u0026lt; .001). Machiavellianism correlated positively with Psychopathy (r = 0.25, p \u0026lt; .001) and Narcissism (r = 0.12, p \u0026lt; .001). Psychopathy was also positively associated with Narcissism (r = 0.14, p \u0026lt; .001). Attitudes toward GAI demonstrated significant positive correlations with Extraversion (r = 0.17, p \u0026lt; .001), Agreeableness (r = 0.18, p \u0026lt; .001), and Neuroticism (r = 0.11, p \u0026lt; .01), but a small negative correlation with Openness to Experience (r = \u0026ndash;0.08, p \u0026lt; .05). GAI academic misconduct showed positive correlations with Machiavellianism (r = 0.33, p \u0026lt; .001), Psychopathy (r = 0.37, p \u0026lt; .001), Narcissism (r = 0.12, p \u0026lt; .001), and Neuroticism (r = 0.25, p \u0026lt; .001. Conversely, GAI academic misconduct was negatively correlated with Attitudes toward GAI (r = \u0026ndash;0.25, p \u0026lt; .001), Emotionality (r = \u0026ndash;0.02, p \u0026lt; .05), and Openness to Experience (r = \u0026ndash;0.06, p \u0026lt; .05), indicating that individuals with more positive AI attitudes, higher emotionality, and greater openness were less likely to report engaging in academic misconduct involving GAI.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eDescriptive statistics and correlations.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1. Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2. Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3. Level of study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4. Disciplines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.11***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.42***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5. Honesty\u0026ndash;Humility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6. Emotionality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7. Extraversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8. Agreeableness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9. Openness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.15***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10. Neuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.15***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.13***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11. Machiavellianism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.24***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12. Psychopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13. Narcissism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.15***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14. GAI attitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15. GAI misconduct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.38***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.30***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. N = 864. p \u0026lt; 0.05*, p \u0026lt; 0.01**, p \u0026lt; 0.001***\u003c/p\u003e\n\u003cp\u003eGender is coded as: 1 = Male; 2 = Female\u003c/p\u003e\n\u003cp\u003eLevel of study is coded as: 1 = Undergraduate; 2 = Master\u0026rsquo;s; 3 = Doctoral\u003c/p\u003e\n\u003cp\u003eDisciplines are coded as: 1 = Humanities and Social Sciences; 2 = Science and Engineering; 3 = Business and Economics; 4 = Medical and Health Sciences; 5 = Other\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHierarchical linear regression\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur primary data analysis, which involved multiple regression, began by confirming that all required assumptions were met: the residuals were independent and normally distributed, with no issues of multicollinearity or heteroskedasticity identified\u003csup\u003e47\u003c/sup\u003e. Additionally, the Cook\u0026rsquo;s distance values indicated that no influential cases were affecting the model. We therefore proceeded with hierarchical linear regression as the central analytical approach.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe predictive role of HEXACO personality inventory and the Dark Triad on GAI attitudes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA hierarchical linear regression analysis was conducted to examine the predictors of participants\u0026rsquo; attitudes toward GAI. The analysis proceeded in three steps:\u003c/p\u003e\n\u003cp\u003eStep 1: Gender, age, level of study, and disciplines were entered as predictors. The model accounted for a negligible amount of variance in GAI attitudes (R\u0026sup2; = 0.001). None of them (e.g., age (\u0026beta; = 0.013, p = 0.656), gender (\u0026beta; = 0.004, p = 0.369)) emerged as significant predictors.\u003c/p\u003e\n\u003cp\u003eStep 2: In the second step, the six HEXACO personality dimensions were added, resulting in a notable increase in explained variance (\u0026Delta;R\u0026sup2; = 0.125). Among these, Honesty\u0026ndash;Humility showed a marginal effect (\u0026beta; = 0.011, p = 0.089), while Extraversion (\u0026beta; = 0.248, p \u0026lt; 0.001) and Openness to Experience (\u0026beta; = 0.358, p \u0026lt; 0.001) emerged as significant positive predictors of GAI attitudes.\u003c/p\u003e\n\u003cp\u003eStep 3: The final step introduced the Dark Triad personality traits, which further increased the explained variance (\u0026Delta;R\u0026sup2; = 0.233). Of these, Machiavellianism was a significant positive predictor (\u0026beta; = 0.253, p \u0026lt; 0.001), whereas Narcissism (\u0026beta; = -0.002, p = 0.814) and Psychopathy (\u0026beta; = -0.004, p = 0.535) were not significant.\u003c/p\u003e\n\u003cp\u003eOverall, HEXACO Extraversion and Openness to Experience, as well as Machiavellianism from the Dark Triad, emerged as robust positive predictors of GAI attitudes. In contrast, age, gender, level of study, disciplines and other personality traits were not significant predictors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe predictive role of HEXACO personality inventory and the Dark Triad on GAI misconduct behaviors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA hierarchical linear regression analysis was conducted to examine the predictive roles of the HEXACO personality dimensions, Dark Triad traits, and attitudes toward GAI in GAI-related academic misconduct behaviors.\u003c/p\u003e\n\u003cp\u003eStep 1: In the first step, gender, age, level of study, and disciplines were included as predictors. These variables explained only a negligible portion of variance in GAI misconduct (R\u0026sup2; = .002), with none demonstrating a significant effect.\u003c/p\u003e\n\u003cp\u003eStep 2: The addition of the six HEXACO personality dimensions in the second step led to a substantial improvement in model fit (\u0026Delta;R\u0026sup2; = .234, p \u0026lt; .001). Honesty\u0026ndash;Humility (\u0026beta; = -0.36, p \u0026lt; .001), Agreeableness (\u0026beta; = -0.15, p \u0026lt; .001), and Conscientiousness (\u0026beta; = -0.25, p \u0026lt; .001) all emerged as significant negative predictors of GAI misconduct.\u003c/p\u003e\n\u003cp\u003eStep 3: Incorporating the Dark Triad traits in the third step further improved the explanatory power of the model (\u0026Delta;R\u0026sup2; = .258, p \u0026lt; .001). Both Narcissism (\u0026beta; = 0.24, p \u0026lt; .001) and Psychopathy (\u0026beta; = 0.25, p \u0026lt; .001) were significant positive predictors of GAI misconduct, whereas Machiavellianism was not significant.\u003c/p\u003e\n\u003cp\u003eStep 4: In the final step, GAI attitudes were entered into the model. However, this variable did not account for any additional variance (\u0026Delta;R\u0026sup2; = .004, p = .55), indicating that attitudes toward GAI did not contribute incremental predictive value beyond the personality variables already included.\u003c/p\u003e\n\u003cp\u003eThe final regression model accounted for a substantial proportion of the variance in GAI misconduct behaviors (R\u0026sup2; = .498). These findings indicate that personality traits\u0026mdash;particularly lower levels of Honesty\u0026ndash;Humility, Agreeableness, and Conscientiousness, and higher levels of Narcissism and Psychopathy\u0026mdash;are robust predictors of GAI-related academic misconduct. In contrast, after accounting for these personality dimensions, attitudes toward GAI provided no additional explanatory power.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSummary of results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePredictors of GAI Attitudes\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003eThe hypotheses related to the HEXACO personality traits were partially supported. \u003cstrong\u003eExtraversion\u003c/strong\u003e and \u003cstrong\u003eOpenness to Experience\u003c/strong\u003e emerged as significant positive predictors of GAI attitudes. Therefore, H5 and H11 were supported. However, the remaining personality traits did not show statistically significant effects, and thus H1, H3, H7, and H9 were not supported. Regarding the Dark Triad, only Machiavellianism emerged as a significant positive predictor of GAI attitudes, supporting H13. In contrast, Narcissism and Psychopathy did not show significant effects. Therefore, H15 and H17 wer\u003cstrong\u003ee not supported\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePredictors of GAI Misconduct Behaviors\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e In terms of GAI misconduct, the HEXACO traits were more consistently aligned with the hypotheses. Honesty\u0026ndash;Humility, Agreeableness, and Conscientiousness all emerged as significant negative predictors of GAI misconduct, supporting H2, H8, and H10. However, the remaining personality traits did not show statistically significant effects, and thus H4, H6 and H12 were not supported. In terms of the Dark Triad traits, both Narcissism and Psychopathy were significant positive predictors of GAI misconduct, supporting H16 and H18. Machiavellianism, however, did not significantly predict GAI misconduct in this analysis, and thus H14 was not supported. In addition, \u003cstrong\u003eGAI attitudes\u003c/strong\u003e did not contribute significant additional explanatory power in predicting GAI misconduct behaviors after accounting for personality traits.\u0026nbsp;\u003c/p\u003e"},{"header":"General discussion ","content":"\u003cp\u003eThe study found that Extraversion and Openness to Experience were significant positive predictors of GAI attitudes, indicating that individuals high in these traits are more inclined to adopt and favor GAI technologies. This aligns with previous research suggesting that extraverted individuals, characterized by sociability and enthusiasm, are more open to new experiences and technologies\u003csup\u003e48,49\u003c/sup\u003e. Similarly, openness to experience has been associated with a greater willingness to engage with novel and complex ideas, including technological innovations.\u003c/p\u003e\n\u003cp\u003eRegarding the Dark Triad traits, Machiavellianism was a significant positive predictor of GAI attitudes, supporting the hypothesis that individuals high in Machiavellianism, characterized by manipulation and strategic self-interest, may view GAI as a tool for personal gain. However, Narcissism and Psychopathy did not show significant effects, which contrasts with some studies indicating that these traits are associated with a more favorable view of technology adoption\u003csup\u003e17,50\u003c/sup\u003e. This discrepancy may reflect differences in the specific technologies studied or the contexts in which these studies were conducted.\u003c/p\u003e\n\u003cp\u003eIn terms of academic misconduct, the study found that Honesty–Humility, Agreeableness, and Conscientiousness were significant negative predictors of GAI misconduct behaviors. These findings are consistent with the literature suggesting that individuals high in these traits are more ethical and less likely to engage in unethical behaviors\u003csup\u003e51,52,53\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eConversely, Narcissism and Psychopathy were significant positive predictors of GAI misconduct, indicating that individuals high in these traits may be more willing to exploit GAI for personal gain, even at the expense of academic integrity\u003csup\u003e54\u003c/sup\u003e. Interestingly, Machiavellianism did not significantly predict GAI misconduct in this study, which contrasts with some research indicating that Machiavellian individuals are more likely to engage in unethical behaviors when they perceive personal benefits\u003csup\u003e55,56\u003c/sup\u003e. This finding suggests that the relationship between Machiavellianism and misconduct may be context-dependent, and further research is needed to explore the conditions under which this relationship holds.\u003c/p\u003e\n\u003cp\u003eAn interesting finding was that GAI attitudes did not contribute significant additional explanatory power in predicting GAI misconduct behaviors after accounting for personality traits. This suggests that personality traits, rather than attitudes toward GAI, are more decisive in determining whether individuals will engage in academic misconduct. This finding highlights the importance of personality in influencing ethical behaviors, even when attitudes toward the technology itself may be positive or neutral.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the predictive roles of HEXACO personality traits and the Dark Triad in shaping students\u0026rsquo; attitudes toward GAI and their likelihood of engaging in GAI-related academic misconduct. The findings underscore the significant influence of personality traits on both GAI adoption and ethical behavior within academic contexts.\u003c/p\u003e\u003cp\u003eSpecifically, Extraversion and Openness to Experience emerged as positive predictors of favorable GAI attitudes, aligning with existing literature that associates these traits with openness to new technologies and ideas. Conversely, Honesty\u0026ndash;Humility did not significantly predict GAI attitudes, suggesting that ethical considerations may not directly influence the adoption of GAI tools in academic settings. Regarding the Dark Triad, Machiavellianism was positively associated with favorable GAI attitudes, indicating that individuals high in this trait may perceive GAI as a strategic tool for personal gain.\u003c/p\u003e\u003cp\u003eIn terms of academic misconduct, Honesty\u0026ndash;Humility, Agreeableness, and Conscientiousness were significant negative predictors, highlighting the role of ethical and prosocial traits in discouraging unethical behaviors related to GAI. On the other hand, Narcissism and Psychopathy were positive predictors of GAI misconduct, suggesting that individuals high in these traits may exploit GAI for self-serving purposes without regard for ethical standards. Interestingly, Machiavellianism did not significantly predict GAI misconduct, indicating that its influence on unethical behavior may be context-dependent.\u003c/p\u003e\u003cp\u003eThese findings contribute to the growing body of literature on the intersection of personality and technology adoption, emphasizing the need for a nuanced understanding of how individual differences influence ethical decision-making in the context of emerging technologies like GAI. Future research should further explore these relationships and consider additional factors such as institutional policies and cultural norms that may mediate the impact of personality on GAI-related behaviors.\u003c/p\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Research\u003c/h2\u003e\u003cp\u003eFirst, the study employed a cross-sectional design, which limits the ability to draw causal inferences. The relationships observed between personality traits and GAI-related outcomes could be influenced by other unmeasured variables, such as situational factors or prior experiences with technology. Future research could benefit from using longitudinal designs to track changes in attitudes and behaviors over time and assess the directionality of these relationships.\u003c/p\u003e\u003cp\u003eSecond, the study relied on self-report measures, which may be subject to social desirability bias or other response biases. While self-report inventories like HEXACO and the Dark Triad are widely used in personality research, combining them with more objective measures, such as behavioral observations or peer ratings, could provide a more accurate assessment of personality and its influence on GAI-related behaviors. Additionally, future studies could consider experimental designs to test the causal effects of personality traits on GAI adoption and misconduct in controlled settings.\u003c/p\u003e\u003cp\u003eThird, the sample in this study was limited to academic researchers, in China which may not be fully representative of the broader population of technology users. As GAI tools are adopted across various industries, future research should explore how personality traits influence GAI attitudes and behaviors in non-academic contexts. It would be particularly interesting to examine how personality traits interact with professional roles, such as industry professionals, students, or policymakers, to shape their use of GAI.\u003c/p\u003e\u003cp\u003eFinally, while this study focused on personality traits as predictors of GAI attitudes and misconduct, other factors, such as institutional policies, cultural norms, and ethical training, may also play a significant role in shaping GAI-related behaviors. Future research could examine these contextual factors and explore their interaction with personality traits to provide a more holistic understanding of the factors influencing ethical decision-making in the context of emerging technologies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaiying Liang: Conceptualization, Methodology, Formal analysis, Writing – original draft\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXu Mao: Data curation, Investigation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMichael Reiss: Writing – review \u0026amp; editing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We would like to express our gratitude to the students who participated in completing the questionnaires and those who helped distribute them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We would like to express our gratitude to the students who participated in completing the questionnaires and those who helped distribute them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be deposited to Open Science Framework upon acceptance of the article. The point of contact is Haiying Liang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBanh, L. \u0026amp; Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1 \u003c/li\u003e\n\u003cli\u003eYu, H. \u0026amp; Guo, Y. (2023). Generative artificial intelligence empowers educational reform: current status, issues, and prospects. In Frontiers in Education (Vol. 8, p. 1183162). Frontiers Media SA. https://doi.org/10.3389/feduc.2023.1183162 \u003c/li\u003e\n\u003cli\u003eSchepman, A. \u0026amp; Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput. Hum. Behav. Rep., 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014\u003c/li\u003e\n\u003cli\u003eSindermann, C., Riedl, R. \u0026amp; Montag, C. (2020). Investigating the relationship between personality and technology acceptance with a focus on the smartphone from a gender perspective: results of an exploratory survey study. Future Internet, 12(7), 110. https://doi.org/10.3390/fi12070110 \u003c/li\u003e\n\u003cli\u003eStein, J. P. et al. (2024). Attitudes towards AI: measurement and associations with personality. Scientific Reports, 14(1), 2909. https://doi.org/10.1038/s41598-024-53335-2 \u003c/li\u003e\n\u003cli\u003eKim, S. W. \u0026amp; Lee, Y. (2024). Investigation into the influence of socio-cultural factors on attitudes toward artificial intelligence. Education and Information Technologies, 29(8), 9907-9935 https://doi.org/10.1007/s10639-023-12172-y \u003c/li\u003e\n\u003cli\u003eLi, S., Glass, R. \u0026amp; Records, H. (2008). The influence of gender on new technology adoption and use\u0026ndash;mobile commerce. Journal of Internet Commerce, 7(2), 270-289. https://doi.org/10.1080/15332860802067748 \u003c/li\u003e\n\u003cli\u003eM\u0026eacute;ndez-Su\u0026aacute;rez, M. et al. (2023). Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance. European Research on Management and Business Economics, 29(3), 100223. https://doi.org/10.1016/j.iedeen.2023.100223 \u003c/li\u003e\n\u003cli\u003eMorris, M. G. \u0026amp; Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375-403. https://doi.org/10.1111/j.1744-6570.2000.tb00206.x \u003c/li\u003e\n\u003cli\u003eAntes, A. L. et al. (2007). Personality and ethical decision-making in research: The role of perceptions of self and others. Journal of Empirical Research on Human Research Ethics, 2(4), 15-34. https://doi.org/10.1525/jer.2007.2.4.15 \u003c/li\u003e\n\u003cli\u003eWang, Y. Y., Wang, Y. S. \u0026amp; Wang, Y. M. (2022). What drives students\u0026rsquo; Internet ethical behaviour: an integrated model of the theory of planned behaviour, personality, and Internet ethics education. Behaviour \u0026amp; Information Technology, 41(3), 588-610. https://doi.org/10.1080/0144929x.2020.1829053 \u003c/li\u003e\n\u003cli\u003eKaya, F. et al. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human\u0026ndash;Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730 \u003c/li\u003e\n\u003cli\u003eKovbasiuk, A. et al. (2025). The personality profile of early generative AI adopters: a big five perspective. Central European Management Journal, 33(2), 252-264. https://doi.org/10.1108/cemj-02-2024-0067 \u003c/li\u003e\n\u003cli\u003eJonason, P. K. \u0026amp; Sherman, R. A. (2020). Personality and the perception of situations: The Big Five and Dark Triad traits. Personality and Individual Differences, 163, 110081. https://doi.org/10.1016/j.paid.2020.110081 \u003c/li\u003e\n\u003cli\u003eSvendsen, G. B. et al. (2013). Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour \u0026amp; Information Technology, 32(4), 323-334. https://doi.org/10.1080/0144929x.2011.553740 \u003c/li\u003e\n\u003cli\u003eSun, R. et al. (2025). The dark tetrad as associated factors in generative AI academic misconduct: insights beyond personal attribute variables. Frontiers in Education, 10, 1551721. https://doi.org/10.3389/feduc.2025.1551721 \u003c/li\u003e\n\u003cli\u003eAplin-Houtz, M. J. et al. (2024). Tales from the dark side of technology acceptance: the Dark Triad and the technology acceptance model. Employee Responsibilities and Rights Journal, 36(4), 421-453. https://doi.org/10.1007/s10672-023-09453-6 \u003c/li\u003e\n\u003cli\u003eJabłońska, M. R. \u0026amp; Zajdel, R. (2020). The Dark Triad Traits and problematic Internet use: Their structure and relations. Polish Sociological Review, 212(4), 477-496. https://doi.org/10.1186/s40359-021-00668-6 \u003c/li\u003e\n\u003cli\u003eRahman, M. S. \u0026amp; Muldoon, J. (2020). Dark side of technology: Investigating the role of dark personality traits and technological factors in managing cyberloafing behavior. Journal of Strategic Innovation and Sustainability, 15(3), 36-54. https://doi.org/10.33423/jsis.v15i3.2947 \u003c/li\u003e\n\u003cli\u003eAshton, M. C. \u0026amp; Lee, K. (2009). The HEXACO\u0026ndash;60: a short measure of the major dimensions of personality. J. Pers. Assess. 91, 340\u0026ndash;345. https://doi.org/10.1080/00223890902935878 \u003c/li\u003e\n\u003cli\u003eLee, K. \u0026amp; Ashton, M. C. (2004). Psychometric properties of the HEXACO personality inventory. Multivariate Behavioral Research, 39(2), 329-358. https://doi.org/10.1207/s15327906mbr3902_8 \u003c/li\u003e\n\u003cli\u003eBoies, K. et al. (2004). Validity studies psychometric properties of scores on the French and Korean versions of the Hexaco personality inventory. Educational and Psychological Measurement, 64(6), 992-1006. https://doi.org/10.1177/0013164404267277 \u003c/li\u003e\n\u003cli\u003eDe Vries, R. E., Lee, K. \u0026amp; Ashton, M. C. (2008). The Dutch HEXACO Personality Inventory: Psychometric properties, self\u0026ndash;other agreement, and relations with psychopathy among low and high acquaintanceship dyads. Journal of Personality Assessment, 90(2), 142-151. https://doi.org/10.1080/00223890701845195 \u003c/li\u003e\n\u003cli\u003eKline, R. et al. (2019). Personality and prosocial behavior: A multilevel meta-analysis. Political Science Research and Methods, 7(1), 125-142. https://doi.org/10.1017/psrm.2017.14 \u003c/li\u003e\n\u003cli\u003eGreitemeyer, T., \u0026amp; Kastenm\u0026uuml;ller, A. (2023). HEXACO, the Dark Triad, and Chat GPT: Who is willing to commit academic cheating? Heliyon, 9(9). https://doi.org/10.1016/j.heliyon.2023.e19909 \u003c/li\u003e\n\u003cli\u003ePahl, K. (2009). Emotionality: A brief introduction. MLN, 124(3), 547-554. https://doi.org/10.1353/mln.0.0147 \u003c/li\u003e\n\u003cli\u003eWeller, J. A. \u0026amp; Tikir, A. (2011). Predicting domain‐specific risk taking with the HEXACO personality structure. Journal of Behavioral Decision Making, 24(2), 180-201. https://doi.org/10.1002/bdm.677 \u003c/li\u003e\n\u003cli\u003eSindermann, C. et al. (2020). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. K\u0026uuml;nstliche Intelligenz, 35(1), 109\u0026ndash;118. https://doi.org/10.1007/s13218-020-00689-0\u003c/li\u003e\n\u003cli\u003eBano, S., Shah, U. U. \u0026amp; Ali, S. (2019). Personality and technology: Big five personality traits as descriptors of universal acceptance and usage of technology UTAUT. Library Philosophy and Practice, 1-22. https://doi.org/10.7717/peerj-cs.1498/fig-3 \u003c/li\u003e\n\u003cli\u003eThielmann, I., Spadaro, G. \u0026amp; Balliet, D. (2020). Personality and prosocial behavior: A theoretical framework and meta-analysis. Psychological Bulletin, 146(1), 30. https://doi.org/10.1037/bul0000217 \u003c/li\u003e\n\u003cli\u003eDangi, M. R. M. \u0026amp; Saat, M. M. (2021). Interaction effects of situational context on the acceptance behaviour and the conscientiousness trait towards intention to adopt. Educational Technology \u0026amp; Society, 24(3), 61-84. https://doi.org/10.6007/ijarbss/v11-i1/8288 \u003c/li\u003e\n\u003cli\u003eQu, K. \u0026amp; Wu, X. (2024). ChatGPT as a CALL tool in language education: A study of hedonic motivation adoption models in English learning environments. Education and Information Technologies, 29(15), 19471-19503. https://doi.org/10.1007/s10639-024-12598-y \u003c/li\u003e\n\u003cli\u003eJaved, B. et al. (2020). Openness to experience, ethical leadership, and innovative work behavior. The Journal of Creative Behavior, 54(1), 211-223. https://doi.org/10.1002/jocb.360 \u003c/li\u003e\n\u003cli\u003ePaulhus, D. L. \u0026amp; Williams, K. M. (2002). The dark triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36(6), 556-563. https://doi.org/10.1016/s0092-6566(02)00505-6 \u003c/li\u003e\n\u003cli\u003eJonason, P. K. \u0026amp; Webster, G. D. (2010). The dirty dozen: a concise measure of the dark triad. Psychological Assessment, 22(2), 420. https://doi.org/10.1037/a0019265 \u003c/li\u003e\n\u003cli\u003eMoshagen, M., Hilbig, B. E. \u0026amp; Zettler, I. (2018). The dark core of personality. Psychological Review, 125(5), 656 https://doi.org/10.1037/rev0000111 \u003c/li\u003e\n\u003cli\u003eBereczkei, T. \u0026amp; Birkas, B. (2014). The insightful manipulator: Machiavellians\u0026apos; interpersonal tactics may be linked to their superior information processing skills. International Journal of Psychological Studies, 6(4), 65. https://doi.org/10.5539/ijps.v6n4p65 \u003c/li\u003e\n\u003cli\u003eIn\u0026aacute;ncsi, T. et al. (2018). Perceptions of close relationship through the Machiavellians dark glasses: Negativity, distrust, self-protection against risk and dissatisfaction. Europe\u0026apos;s Journal of Psychology, 14(4), 806. https://doi.org/10.5964/ejop.v14i4.1550 \u003c/li\u003e\n\u003cli\u003eLo, C. F. \u0026amp; Ng, E. C. B. (2019). Machiavellianism and intimacy attitudes in the interpersonal relationships. Psychology, 10(04), 424. https://doi.org/10.4236/psych.2019.104029 \u003c/li\u003e\n\u003cli\u003eRauthmann, J. F. (2012). The Dark Triad and interpersonal perception: Similarities and differences in the social consequences of narcissism, Machiavellianism, and psychopathy. Social Psychological and Personality Science, 3(4), 487-496. https://doi.org/10.1177/1948550611427608 \u003c/li\u003e\n\u003cli\u003eSong, J. \u0026amp; Liu, S. (2025). Dark personality traits are associated with academic misconduct, frustration, negative thinking, and generative AI use habits: the case of Sichuan art universities. BMC Psychology, 13(1), 633. https://doi.org/10.1186/s40359-025-02949-w \u003c/li\u003e\n\u003cli\u003eAvelino, B. C., de Lima, G. A. S. F., da Cunha, J. V. A. \u0026amp; Colauto, R. D. (2017). The influence of narcissism in the professional environment: Aspects related to dishonesty. Advances in Scientific and Applied Accounting, 334-356. https://doi.org/10.4270/ruc.2017319 \u003c/li\u003e\n\u003cli\u003eAjzen, I. (2014). Attitude structure and behavior. In Attitude structure and function (pp. 241-274). Psychology Press. https://doi.org/10.4324/9781315801780-15 \u003c/li\u003e\n\u003cli\u003eAjzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179\u0026ndash;211. https://doi.org/10.1016/0749-5978(91)90020-T\u003c/li\u003e\n\u003cli\u003eSt\u0026ouml;ber, J. (2001). The Social Desirability Scale-17 (SDS-17): Convergent validity, discriminant validity, and relationship with age. Eur. J. Psychol. Assess., 17(3), 222\u0026ndash;232. https://doi.org/10.1027/1015-5759.17.3.222\u003c/li\u003e\n\u003cli\u003eJones, D. N. \u0026amp; Paulhus, D. L. (2014). Introducing the Short Dark Triad (SD3): A brief measure of dark personality traits. Assessment, 21(1), 28\u0026ndash;41. https://doi.org/10.1177/1073191113514105\u003c/li\u003e\n\u003cli\u003eField, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited. https://doi.org/10.1024/1012-5302/a000397 \u003c/li\u003e\n\u003cli\u003eAluja, A., Garcıa, O. \u0026amp; Garcıa, L. F. (2003). Relationships among extraversion, openness to experience, and sensation seeking. Personality and Individual Differences, 35(3), 671-680. https://doi.org/10.1016/s0191-8869(02)00244-1 \u003c/li\u003e\n\u003cli\u003eBowden-Green, T., Hinds, J. \u0026amp; Joinson, A. (2020). How is extraversion related to social media use? A literature review. Personality and Individual Differences, 164, 110040. https://doi.org/10.1016/j.paid.2020.110040 \u003c/li\u003e\n\u003cli\u003eChakraborty, D. (2025). Psychological Dispositions and AI Utilization: Unpacking Narcissism, Machiavellianism, and Psychopathy in E-commerce. Journal of Promotion Management, 31(4), 549-577. https://doi.org/10.1080/10496491.2025.2484714 \u003c/li\u003e\n\u003cli\u003eAnwar, S. \u0026amp; Shah, N. (2017). Impact of personality traits on ethical behavior. The Government-Annual Research Journal of Political Science, 6, 95-114. https://doi.org/10.4135/9781483391144.n265 \u003c/li\u003e\n\u003cli\u003eGiluk, T. L. \u0026amp; Postlethwaite, B. E. (2015). Big Five personality and academic dishonesty: A meta-analytic review. Personality and Individual Differences, 72, 59-67. https://doi.org/10.1016/j.paid.2014.08.027 \u003c/li\u003e\n\u003cli\u003eSimha, A. \u0026amp; Parboteeah, K. P. (2020). The big 5 personality traits and willingness to justify unethical behavior\u0026mdash;a cross-national examination. Journal of Business Ethics, 167(3), 451-471. https://doi.org/10.1007/s10551-019-04142-7 \u003c/li\u003e\n\u003cli\u003eBrunell, A. B., Staats, S., Barden, J. \u0026amp; Hupp, J. M. (2011). Narcissism and academic dishonesty: The exhibitionism dimension and the lack of guilt. Personality and Individual Differences, 50(3), 323-328. https://doi.org/10.1016/j.paid.2010.10.006 \u003c/li\u003e\n\u003cli\u003eDen Hartog, D. N. \u0026amp; Belschak, F. D. (2012). Work engagement and Machiavellianism in the ethical leadership process. Journal of Business Ethics, 107(1), 35-47. https://doi.org/10.1007/s10551-012-1296-4 \u003c/li\u003e\n\u003cli\u003eHarrison, A., Summers, J. \u0026amp; Mennecke, B. (2018). The effects of the dark triad on unethical behavior. Journal of Business Ethics, 153(1), 53-77. https://doi.org/10.1007/s10551-016-3368-3 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7356639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7356639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates how personality traits, specifically those measured by the HEXACO Personality Inventory and the Dark Triad, predict university students\u0026rsquo; attitudes toward generative artificial intelligence (GAI) and their engagement in GAI-related academic misconduct. The first objective was to develop and validate a Chinese-language scale to measure students\u0026rsquo; attitudes toward GAI in academic contexts. The newly developed GAI attitudes scale was tested for psychometric properties, showing high internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.92) and reliability. In the second part of the study, hierarchical linear regression analyses explored the relationship between personality traits and both GAI attitudes and misconduct behaviors. Findings indicated that Extraversion (β\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Openness to Experience (β\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant positive predictors of favorable GAI attitudes. Regarding misconduct behaviors, Honesty-Humility (β = -0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Agreeableness (β = -0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Conscientiousness (β = -0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant negative predictors of GAI-related misconduct, while Narcissism (β\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Psychopathy (β\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant positive predictors. Notably, GAI attitudes did not provide additional predictive value for misconduct beyond personality traits (ΔR\u0026sup2; = .004, p\u0026thinsp;=\u0026thinsp;0.55). These results contribute to the understanding of personality\u0026rsquo;s role in GAI adoption and unethical academic behaviors, with implications for the responsible integration and regulation of GAI in academic practices.\u003c/p\u003e","manuscriptTitle":"Personality Predictors of Attitudes and Misconduct Behaviors Related to Generative Artificial Intelligence: Evidence from the HEXACO and Dark Triad Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 13:01:05","doi":"10.21203/rs.3.rs-7356639/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T06:50:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T21:35:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T13:09:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273322880178596967113551064015284571935","date":"2025-09-07T04:39:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104421893677006909192874536420613629895","date":"2025-09-05T15:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266957743277535451546804870842133702244","date":"2025-09-05T12:44:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280047621490358716528670710479825064300","date":"2025-09-05T10:04:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145264082250780855186584669656370083634","date":"2025-09-05T08:21:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110621594799903072436231200806258402270","date":"2025-09-05T01:48:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-25T07:44:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T07:39:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-21T09:11:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T21:40:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-14T21:37:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d2b8858-35a5-4d23-8170-a83a055885c5","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53983623,"name":"Biological sciences/Psychology"},{"id":53983624,"name":"Social science/Psychology"},{"id":53983625,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2025-12-01T16:07:57+00:00","versionOfRecord":{"articleIdentity":"rs-7356639","link":"https://doi.org/10.1038/s41598-025-25744-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-25 15:58:26","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-09-02 13:01:05","video":"","vorDoi":"10.1038/s41598-025-25744-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-25744-4","workflowStages":[]},"version":"v1","identity":"rs-7356639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7356639","identity":"rs-7356639","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}