The study, conducted by Wu Zhangxun of the Seoul School of Integrated Sciences and Technologies, draws on two well-established theoretical foundations: the five-dimensional curiosity framework and technology acceptance theory. Technology acceptance theory, rooted in the influential work of Fred Davis and later expanded into unified models of technology adoption, holds that people’s willingness to embrace new technologies depends heavily on their perceptions of usefulness and ease of use. Attitudes toward technology, in this view, are not fixed dispositions but evaluations that can be shaped by experience, context, and motivation. The five-dimensional curiosity framework, developed by Todd Kashdan and colleagues, breaks curiosity into distinct facets — including joyous exploration, deprivation sensitivity, stress tolerance, social curiosity, and thrill seeking — recognizing that curiosity is not a single switch but a spectrum of motivational tendencies.

At the heart of the study lies a concept with a long pedigree in adult education: self-directed learning. First formalized by Malcolm Knowles in the 1970s, self-directed learning describes the process by which individuals take initiative in diagnosing their own learning needs, setting goals, identifying resources, and evaluating outcomes, with or without help from others. In an era when the half-life of professional skills keeps shrinking, self-directed learning has been described as a fundamental competence for a rapidly changing world. The study’s central hypothesis was straightforward: employees who habitually drive their own learning should hold more positive general attitudes toward artificial intelligence, because learning-oriented people are more likely to encounter AI on its own terms, understand its capabilities, and integrate it into their sense of professional identity rather than experiencing it as a threat.

To test this idea, the researcher built a moderated mediation model — a statistical architecture that allows a single study to answer several questions at once. The first question is whether self-directed learning predicts general attitudes toward artificial intelligence. The second concerns mechanism: what carries that influence from one to the other? Here the study nominated innovation climate, the shared perception among employees that their organization encourages new ideas, tolerates experimentation, and rewards creative effort. The third question concerns boundary conditions: under what personal circumstances does the relationship strengthen or weaken? For that role, the study enlisted five-dimensional curiosity as a moderator, testing whether curious employees convert their learning into more favorable AI attitudes more readily than their less curious peers.

The data told a coherent story. Self-directed learning was positively associated with employees’ general attitudes toward artificial intelligence, confirming the basic prediction that people who own their learning tend to view AI more favorably. More intriguingly, innovation climate mediated this relationship. Employees who engaged in self-directed learning perceived a stronger innovation climate around them, and that perception, in turn, was linked to more positive attitudes toward AI. The interpretation is subtle but important: learning-oriented employees do not simply absorb new technology in isolation. They appear to read their environment, and when their own proactive learning resonates with an organization that visibly values innovation, the combination translates into a more welcoming stance toward artificial intelligence.

The moderating results added a second, distinct layer to the picture. Five-dimensional curiosity moderated the direct link between self-directed learning and attitudes toward AI, but it did not significantly moderate the relationship between self-directed learning and innovation climate. In practical terms, curiosity changes how employees personally evaluate AI once they have learned about it, but it does not change how they perceive their organization’s innovativeness. The author concludes that innovation climate and employee curiosity operate through distinct mechanisms — one organizational and contextual, the other individual and motivational — and that both matter for how employees evaluate AI within organizational settings. This separation matters theoretically, because it suggests that the environmental pathway and the personality pathway are parallel rather than intertwined, each contributing independently to the formation of technology attitudes.

The study’s methodological choices deserve attention. The survey instruments were grounded in validated measures, including established scales for self-directed learning and general attitudes toward artificial intelligence, the latter a construct that has been formalized in recent years through dedicated psychometric instruments designed to capture attitudes toward AI as a general phenomenon rather than a specific application. The analysis followed modern conventions for testing mediation and moderation, drawing on statistical frameworks for conditional process models that have become standard in organizational psychology. The researcher also took steps associated with controlling common method bias, a well-known hazard in single-source survey research, and the work was conducted in compliance with the ethical principles of the Belmont Report, with written informed consent obtained from all participants for the publication of anonymized data.

Context matters for interpreting these findings. China has made artificial intelligence a national priority since the State Council’s 2017 New Generation Artificial Intelligence Development Plan, and state-owned enterprises in particular have been pushed to modernize through digital transformation initiatives. That policy backdrop makes Chinese corporate employees a revealing population for studying AI attitudes: they sit at the intersection of rapid technological deployment, organizational reform, and intense public discussion about the future of work. Attitudes toward AI in such an environment are not abstract opinions; they shape whether training programs succeed, whether AI tools are actually used once purchased, and whether employees experience technological change as opportunity or displacement.

For organizations, the practical implications are tangible. If innovation climate carries part of the effect of self-directed learning on AI attitudes, then companies cannot simply exhort employees to be curious about AI and expect results. They need to build visible, credible environments in which experimentation is safe, new ideas circulate, and learning is resourced — conditions long associated in the organizational literature with psychological safety and creative work environments. At the same time, because curiosity operates on the individual pathway, recruitment and development practices that identify and cultivate curious employees may pay dividends specifically in technology adoption. The two levers are complementary: one shapes the stage, the other shapes the actor.

The study also carries the usual caveats of cross-sectional survey research. Measuring learning, climate, curiosity, and attitudes at a single point in time means the causal ordering, however plausible theoretically, is inferred rather than observed — a limitation that methodologists have long flagged for mediation models of this kind. The sample, drawn from corporate employees in China, invites replication in other national and cultural contexts before the findings are generalized. And attitudes toward AI are a moving target: as generative tools reshape daily work, the meaning of a positive AI attitude itself may shift. Still, the study’s core contribution stands. It disentangles two forces — a contextual one and a personal one — that are often blurred together in discussions of technological change, and it suggests that winning hearts and minds for AI in the workplace requires attention not only to what employees learn, but to where they learn it and who they are when they do.

Subject of Research: The relationship between corporate employees' self-directed learning and their attitudes toward artificial intelligence, mediated by innovation climate and moderated by five-dimensional curiosity.

Article Title: Corporate employees’ self-directed learning and attitudes toward artificial intelligence: the mediating role of innovation climate and the moderating role of five-dimensional curiosity

Article References: Zhangxun, W. (2026). Corporate employees’ self-directed learning and attitudes toward artificial intelligence: the mediating role of innovation climate and the moderating role of five-dimensional curiosity. Current Psychology, 45(17), Article 1493. https://doi.org/10.1007/s12144-026-10045-4

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10045-4

Keywords: artificial intelligence, self-directed learning, innovation climate, five-dimensional curiosity, technology acceptance, corporate employees, organizational psychology, attitudes toward AI, workplace learning, moderated mediation, Current Psychology, China

The research team, led by Liping Wang, Manhua Nie, Yanchao Xiao, and corresponding author Sai Liu, recruited nursing students from ten universities spanning seven provinces in China between May and June 2025. Using convenience sampling, they collected data through a battery of validated instruments: a self-designed demographic questionnaire, the General Attitudes Towards Artificial Intelligence Scale, the Artificial Intelligence Literacy Scale, and the Artificial Intelligence Anxiety Scale. The final analytical sample comprised 455 nursing students, a substantial cohort drawn from a wide geographic spread that lends the findings considerable weight within the constraints of the study design. All data were analyzed using SPSS Statistics 26.0 and AMOS 29.0, the latter allowing the team to construct and test structural equation models that could probe the relationships among literacy, anxiety, and attitudes simultaneously rather than one pair at a time.

The results paint a nuanced picture of a generation caught between enthusiasm and unease. Nursing students in the sample reported moderate levels of both AI literacy and AI anxiety, while generally maintaining positive attitudes toward artificial intelligence overall. That combination—middling knowledge, middling worry, and a broadly favorable outlook—may reflect the reality of contemporary nursing education, where AI is increasingly visible in coursework and clinical discussion but has not yet become a fully integrated pillar of the curriculum. The students, in other words, are neither technophiles nor technophobes; they occupy a middle ground where their attitudes remain open but their confidence and their fears are both very much alive.

The most consequential finding, however, lies in the mediation analysis. When the researchers modeled the pathways between variables, they found that AI anxiety statistically mediated the association between AI literacy and attitudes toward AI. In practical terms, this means the relationship between knowing about AI and feeling good about AI is not a simple straight line. Higher AI literacy was significantly associated with more positive attitudes, but part of that association appeared to flow through reduced anxiety. Students who understood more about how artificial intelligence works tended to fear it less, and that lower anxiety, in turn, contributed to a more welcoming stance toward the technology. Anxiety, in this model, is not merely a background emotion—it is a mechanism through which education exerts part of its effect.

This mediation structure carries real theoretical weight. In psychological research, a mediator explains how or why one variable influences another, and identifying one changes the design of interventions. If literacy influenced attitudes only directly, the prescription would be straightforward: teach more AI content. But because anxiety sits in the pathway, the study’s authors argue that nursing education may benefit from a dual-path approach—one designed to simultaneously enhance cognitive competencies, meaning AI literacy, and to manage affective responses, meaning AI anxiety. A curriculum that builds technical understanding while explicitly addressing students’ fears about job displacement, clinical error, or professional obsolescence could, according to this model, produce more durable gains in acceptance than either strategy alone.

The technical machinery behind the conclusion deserves attention. Structural equation modeling, implemented in AMOS 29.0, allows researchers to test whether a hypothesized network of causal-style pathways is consistent with observed data, and the team supplemented their primary analysis with a stepwise model modification procedure documented in their supplementary materials, including fit indices and the rationale for each adjustment. The study also reported complete subdimension and total score results for the full sample of 455 participants, and the authors followed the STROBE checklist for reporting cross-sectional research. These methodological guardrails matter because mediation claims are easy to overstate; the authors are careful to describe the mediation as statistical, which is the appropriate caution for cross-sectional data collected at a single point in time.

That caution points to the study’s principal limitation. Because the data are cross-sectional, the direction of the pathways cannot be definitively established. It is plausible that students with more positive attitudes seek out AI knowledge, or that anxiety suppresses the motivation to learn about AI, creating a feedback loop rather than a one-way street. Convenience sampling also limits generalizability, since students who volunteer for surveys about AI may differ systematically from those who decline. The authors acknowledge these constraints implicitly through their careful language, and the field will need longitudinal and experimental designs—perhaps curricular interventions that track literacy, anxiety, and attitudes across an entire degree program—to confirm the causal architecture the mediation model implies.

Even with those caveats, the stakes for nursing are high. Artificial intelligence is widely described as a key driver of the Fourth Industrial Revolution, and its impact on healthcare is accelerating through diagnostic support tools, predictive analytics, intelligent monitoring systems, and administrative automation. Nursing students’ acceptance and readiness to use these systems are crucial for successful integration, because nurses are the professionals who will interact with AI-enabled tools most frequently at the bedside. A workforce that understands AI but fears it may hesitate at the point of care; a workforce that neither understands nor trusts it may resist adoption outright. The study’s framing of perceptions as vital preparation for the intelligent healthcare era reflects this operational reality.

The emotional dimension of technological adoption has often been treated as secondary in health professional education, where competencies and knowledge checklists dominate. This study adds to a growing body of evidence that affect is not noise in the adoption equation but a signal worth measuring and addressing. AI anxiety among students may stem from many sources—uncertainty about future employment, unfamiliarity with algorithmic decision-making, high-profile narratives about automation replacing human work—and the finding that it channels the relationship between literacy and attitudes suggests that ignoring it could undercut even well-designed technical training. Educators who pair AI content with opportunities to discuss fears openly, practice with real tools, and build confidence through supervised exposure may be acting directly on the mechanism the study identified.

The research, conducted by a team based in the Clinical Nursing Teaching and Research Section and the Department of Kidney Transplantation at The Second Xiangya Hospital of Central South University, received ethical approval from the hospital’s Ethics Committee and was funded in part by the Natural Science Foundation of Hunan Province and the Hunan Nursing Association’s Young Talent Initiative. Published open access in BMC Medical Education, it offers educators across nursing and the wider health professions a concrete, testable framework: measure literacy, measure anxiety, and design curricula that move both. As AI continues its advance into clinical medicine, the study suggests that the battle for acceptance will be fought not only in lecture halls and skills labs but in the emotional lives of the students training to become the next generation of caregivers.

Subject of Research: The mediating role of AI anxiety in the relationship between AI literacy and attitudes toward artificial intelligence among nursing students

Article Title: The mediating role of AI anxiety between AI literacy and attitudes towards AI in nursing students: a cross-sectional study

Article References: The mediating role of AI anxiety between AI literacy and attitudes towards AI in nursing students: a cross-sectional study. (n.d.). https://doi.org/10.1186/s12909-026-10522-1

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10522-1

Keywords: artificial intelligence, AI literacy, AI anxiety, nursing students, nursing education, mediation analysis, cross-sectional study, structural equation modeling, healthcare technology, medical education, China, attitudes toward AI

The research, published in the journal Discover Education, surveyed 388 student teachers enrolled in the faculties of education at the two universities during the 2025/2026 academic year. The team, led by Aisha Fadl Al-Kaabi of Qatar University and Yahya Abdulrazaq Qutran of Sana’a University, used a descriptive, analytical and comparative design, combining a 41-item questionnaire measuring usage and ethical awareness with a 24-item attitude scale, all administered electronically through Google Forms. The instruments were validated by a panel of eleven experts and showed strong internal consistency, with Cronbach’s alpha coefficients ranging from 0.85 to 0.91 across the three measured dimensions.

The choice of settings was deliberate. Yemen and Qatar represent starkly different educational and economic environments: Qatar has made digital learning and artificial intelligence integration central pillars of its national development strategy, while Yemen continues to grapple with economic instability, limited internet connectivity and scarce formal AI education. If the digital divide shapes how future teachers engage with generative AI, it should have shown up clearly in this comparison. Instead, the results were strikingly even.

ChatGPT emerged as the undisputed tool of choice in both countries. At Qatar University, 93.15 percent of surveyed student teachers reported using it, while at Sana’a University the figure was 73.02 percent. Other applications such as Gemini, Microsoft Copilot, DeepSeek and Meta AI trailed far behind. The researchers attribute ChatGPT’s dominance to its early launch in late 2022, its user-friendly interface and its broad free accessibility, factors that appear to have levelled the playing field between students in a wealthy Gulf state and those in one of the world’s most economically challenged environments.

Statistically, the two groups were nearly indistinguishable. Qatar University students scored slightly higher on usage (a mean of 3.53 versus 3.48 on a five-point scale) and on ethical awareness (3.99 versus 3.88), but independent samples t-tests found neither difference statistically significant. Even more counterintuitively, Sana’a University students edged out their Qatari peers on attitudes toward generative AI, with means of 3.52 and 3.49 respectively, a difference that was also not significant. The effect sizes, ranging from Cohen’s d of 0.05 to 0.19, were negligible to small. The authors suggest that the ubiquity and accessibility of these tools may have decoupled AI adoption from local infrastructure and wealth, at least at the level of perception and self-reported use.

Beneath the headline parity, however, the study revealed telling differences in how the two groups actually deployed the technology. Sana’a University students most commonly used generative AI to explain and simplify difficult topics, while their Qatari counterparts favoured summarizing long texts. Both groups ranked searching for highly relevant information among their top applications. The ethical priorities also diverged in revealing ways. Students at both universities placed the highest importance on considering sustainability when using AI to prevent harm to the environment and society, but Sana’a students next prioritized avoiding biased or inaccurate content, whereas Qatari students emphasized avoiding plagiarism, reflecting global concerns about academic integrity and institutional policy.

The correlational analysis produced perhaps the study’s most thought-provoking finding. Usage and ethical awareness showed a statistically significant but weak positive correlation (r = 0.16, p < 0.001), and usage and attitudes were similarly weakly linked (r = 0.24, p < 0.001). Most strikingly, ethical awareness and attitudes were essentially unrelated (r = 0.07, p = 0.160), failing to reach statistical significance. In plain terms, enthusiasm for generative AI does not translate into ethical vigilance. The authors interpret this statistical decoupling as evidence that positive attitudes, driven largely by perceived utility, develop independently from ethical concerns, and they point to a persistent gap between theoretical awareness of ethical principles and their practical application, compounded by insufficient training and the absence of clear institutional guidelines.

Demographic variables told their own story. Gender produced no statistically significant differences in any of the three domains, a result the researchers link to the narrowing of the gender digital gap in academic contexts, since generative AI tools are now equally accessible to all students and ethical issues are addressed uniformly in curricula. Academic specialization, however, mattered in one crucial respect: students in scientific disciplines held significantly more positive attitudes toward generative AI than those in literary fields (means of 3.62 versus 3.42, t = 3.32, p = 0.01, Cohen’s d = 0.34), a small-to-moderate effect the authors attribute to the more immediate perceived relevance of AI to science careers. No specialization differences emerged in usage or ethical awareness.

The study is candid about its limitations. The sample was heavily imbalanced, with 315 participants from Sana’a University and only 73 from Qatar University, a disparity that weakens statistical power and raises the possibility of Type II errors, meaning real differences between the institutions could have gone undetected. The gender distribution, 338 females to 50 males, mirrors the actual population but similarly constrains gender-based comparisons. The cross-sectional, correlational design rules out causal inference, and the reliance on self-reporting introduces the risk of response bias, particularly in the ethical awareness domain, where participants may have overstated their adherence to responsible-use principles. The authors also note that multiple statistical tests were performed without formal alpha adjustments such as the Bonferroni correction, so significant findings, including the specialization effect, warrant cautious interpretation.

Despite these caveats, the implications are clear and urgent. The researchers recommend that both colleges of education adopt comprehensive institutional frameworks for integrating generative AI, including explicit academic integrity rules, human-centred guidelines emphasizing fairness, transparency and data privacy, and structured training programs to bridge the gap between theoretical knowledge and practice. They call for targeted support to boost positive attitudes among humanities students, a fundamental review of curricula and assessment methods away from traditional written tasks toward critical thinking and problem-solving, and future research using qualitative methods, longitudinal designs and balanced samples to probe why ethical awareness and enthusiasm for AI travel on such separate tracks. As generative AI becomes embedded in classrooms worldwide, this study suggests that the technology itself may be the great equalizer, but the ethical and pedagogical scaffolding around it remains the decisive variable.

Subject of Research: Generative AI use, ethics, and attitudes among student teachers in Yemen and Qatar

Article Title: The relationship between the uses of generative artificial intelligence and ethics and attitudes among student teachers in the faculties of education at Sana’a and Qatar Universities

Article References: Al-Kaabi, A. F., & Qutran, Y. A. (2026). The relationship between the uses of generative artificial intelligence and ethics and attitudes among student teachers in the faculties of education at Sana’a and Qatar Universities. Discover Education, 5(1), Article 1078. https://doi.org/10.1007/s44217-026-02204-3

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02204-3

Keywords: generative artificial intelligence, ChatGPT, student teachers, teacher education, AI ethics, Sana'a University, Qatar University, higher education, digital divide, academic integrity, attitudes toward AI, comparative study