Factors Influencing AI Ethical Awareness: Digital Literacy and Critical Thinking Disposition among Nursing Students

The Effects of Metacognition, Digital Literacy, and Perceptions of Artificial Intelligence on Learning Ethics among Nursing Students

This descriptive survey study aimed to identify the factors influencing learning ethics among 134 nursing students in G city. Data were collected from May 25 to June 3, 2026, and analyzed using the SPSS 23.0 program. The results indicated relatively high levels of metacognition(4.34±0.43), digital literacy(4.38±0.51), artificial intelligence(AI) perception(3.79±0.87), and learning ethics(3.25±0.53). Significant positive correlations were observed among all research variables. Multiple regression analysis revealed that learning ethics was significantly predicted by AI perception(β=.423, p<.001), AI education experience(β=.422, p<.001), major satisfaction('satisfied': β=.257, 'very satisfied': β=.228, p<.001), being a 3rd-year student(β=.245, p<.001), metacognition(β=.207, p<.001), and gender(β=.135, p=.003). The model’s total explanatory power was 82.4%.These findings suggest that establishing learning ethics in an educational environment with expanding generative AI usage requires fostering correct AI perceptions, providing practical AI ethics education, and enhancing metacognition as a self-regulatory capacity. Consequently, systematic educational programs that integrate AI ethics and metacognition while considering learner characteristics and educational experiences should be developed and implemented.

Assessment of the relationship between critical thinking and information literacy in dental students: A cross-sectional study

Artificial Intelligence Literacy, Critical Thinking Disposition, and Clinical Competence Among Nursing Students: A Cross-Sectional Study

AI literacy and critical thinking disposition were significantly associated with clinical competence among nursing students with clinical practice experience and nursing education may consider strategies that address both competencies.

Gender-Based Differences in Critical Thinking Levels among Nursing Students: A Cross -Sectional Study

Aims: This study aimed to examine gender-based differences in critical thinking levels among nursing students. Methods: A quantitative descriptive-analytic study with a cross-sectional design was performed. A total of 267 nursing students were included as respondents using a total sampling technique. Data were collected using the Nursing Critical Thinking in Students Questionnaire (N-CT-4). Univariate analysis was used to describe the respondents’ characteristics and critical thinking levels. The Mann–Whitney U test was used to compare critical thinking domain scores between male and female students, while the Chi-square test was applied to compare the distribution of critical thinking levels by gender.  Results: The mean age of the respondents was 21.09±0.70 years, with the majority being female (86.9%). Most students demonstrated moderate critical thinking (50.9%). Male students showed higher median scores across all domains of critical thinking, including personal (130 vs. 123), intellectual (148 vs. 145), interpersonal (71 vs. 62), and technical (21 vs. 19) domains.  However, the Mann–Whitney U test showed no statistically significant differences between male and female students in the personal (p = 0.396), intellectual (p = 0.759), interpersonal (p = 0.270), or technical (p = 0.523) domains. The Chi-square test showed a significant difference in the distribution of critical thinking levels by gender (χ²=6.276; p=0.043). Conclusion: Nursing students generally demonstrated moderate critical thinking, with intellectual skills strongest and technical skills weakest. Gender influenced overall level distribution but not domain scores, highlighting the need for adaptive, inclusive, and student-centered learning.  

The Associations of AI Literacy, AI Self-Efficacy and AI Attitudes Among Nursing Students: A Cross-Sectional Path Analysis

Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into nursing education, yet structured AI literacy training and ethical guidance remain limited. Consequently, nursing students often rely on informal learning, resulting in variability in AI readiness, confidence, and responsible use. Aims: This study was conducted to examine (1) whether AI literacy was positively associated with AI self-efficacy and AI attitudes and (2) whether AI self-efficacy mediated the relationship between AI literacy and AI attitudes. Methods: A cross-sectional survey using convenience sampling was conducted with 100 prelicensure nursing students in New York City. Data were collected using the AI Literacy Scale (AILS), AI Self-Efficacy Scale (AISES), and Generative AI Attitude Scale (GAIAS). Correlation and path analyses were performed using SPSS and Amos 30.0. Results: The participants had a mean age of 30.25 years, and 71% were women. AI literacy and AI self-efficacy were both positively associated with AI attitudes (all p < 0.001). Path analysis showed that AI literacy significantly predicted AI self-efficacy (β = 0.39, p < 0.001) and AI attitudes (β = 0.28, p = 0.003). AI self-efficacy significantly predicted AI attitudes (β = 0.31, p = 0.001) and partially mediated the relationship between AI literacy and AI attitudes. Conclusions: AI self-efficacy partially mediated the relationship between AI literacy and AI attitudes. Nursing curricula may benefit from structured AI education that integrates guided GenAI practice, case-based learning, and faculty feedback. Such educational frameworks warrant further empirical investigation regarding their potential to foster AI literacy, AI self-efficacy, and positive attitudes toward responsible AI integration, particularly through longitudinal studies assessing subsequent behavioral outcomes.

From AI use to critical thinking among medical students: a moderated mediation perspective on cognitive load and self-regulated learning

Background The increasing integration of artificial intelligence (AI) in medical education has raised important questions regarding its impact on higher-order cognitive processes, particularly critical thinking. Aim and Objectives This study examines the interplay of cognitive load and self-regulated learning in explaining how AI-based educational technology influences critical thinking among medical university students. Methods Using a cross-sectional research design, data were collected from 480 undergraduate medical students with prior experience using AI tools. Data were collected using standardized measures assessing AI usage, cognitive load, self-regulated learning, and critical thinking. Hayes' PROCESS Model 14 was employed to test mediation and moderated mediation effects. Results The findings revealed that AI-based technology use was positively associated with critical thinking and self-regulated learning, while cognitive load showed a negative association with both variables. Mediation analysis indicated that cognitive load partially mediated the relationship between AI use and critical thinking. Furthermore, the moderated mediation analysis demonstrated that self- regulated learning significantly moderated the indirect effect, such that the negative impact of cognitive load on critical thinking weakened at higher levels of self-regulation. Conclusion These findings highlight that the effectiveness of AI in enhancing critical thinking depends not only on its cognitive support but also on learners' ability to regulate their engagement. The study provides theoretical and practical implications for integrating AI in educational contexts.