Aug 2026· Frontiers in Public Health· Vol 14· 0 citations· 36 references
Medicine
Abstract
Background Current research does not examine how distinct AI literacy profiles are differentially associated with learning engagement, thereby impeding the development of stratified and precise training plans for nursing students. Objective To identify latent profiles of artificial intelligence literacy among undergraduate nursing students, characterize their distributional features, and examine the relationship between distinct AI literacy profiles and learning engagement. Methods The study included 479 Chinese undergraduate nursing students who finished the Utrecht Work Engagement Scale-Student Version and the Artificial Intelligence Literacy Scale. Latent profile analysis was conducted using item-level AI literacy scores as manifest indicators. Results Three distinct profiles of AI literacy were identified: low literacy—ethically cautious, medium literacy—balanced development, and high literacy—fully mature. Non-parametric test results demonstrated significant differences in learning engagement and its dimensions across the three AI literacy profiles. After controlling for relevant confounding factors in multilevel linear regression analyses, AI literacy profile remained significantly associated with learning engagement, accounting for an additional 31.2% of the variance. Students in the medium and high AI literacy groups demonstrated significantly higher levels of learning engagement compared to those in the low literacy group. Conclusion Undergraduate nursing students’ AI literacy is heterogeneous and markedly related to learning engagement. These findings provide valuable insights for improving student engagement in AI-supported learning environments.
Background Artificial intelligence (AI) is quickly revolutionizing higher education; however, there is high heterogeneity regarding the adoption of AI amongst the students. In the context of nursing education, where successful learning, critical thinking skills, and professional accountability are crucial, it becomes essential to understand how the students interact with the AI applications. The current study was conducted to determine the AI-use profiles among nursing students and how they relate to academic outcomes and demographics. Methods We used a cross-sectional survey among nursing students. AI use for learning purposes was assessed using four binary indicators capturing use for understanding concepts, summarizing content, drafting assignments, and language support. Study engagement was measured using selected items from the Online Student Engagement framework and collapsed into three ordinal categories. We assessed academic performance using three self-reported items. We performed a latent class analysis to identify distinct student profiles based on AI use and engagement indicators. Multinomial logistic regression examined associations between demographic factors and class membership. Multiple linear regression assessed differences in academic performance across classes. Results The best model was a three-profile solution with a good classification accuracy. The profiles included strategic engagers, moderate users, and passive or low engagers. As expected, strategic engagers were characterized by high levels of engagement with all kinds of behaviors studied, whereas Passive used consistently reported low levels of engagement. Usage indicators of AI revealed little variation among classes. The performance scores of strategic engagers were significantly better compared with those of passive users (β = 2.39, 95% CI = 2.08–2.69). No significant association was found for demographic characteristics. Conclusions Nursing students can be categorized into distinct profiles based on their patterns of AI use and study engagement. Our findings showed that study engagement, but not AI use alone, was the key factor associated with academic performance. These findings highlight the importance of promoting effective learning strategies alongside AI integration. Educational interventions should focus on guiding students toward strategic and responsible use of AI to enhance learning outcomes.
Majed M. Aljabri, Bandar S. Alharbi, Endale Alemayehu Ali· Frontiers in Medicine· 0 citations
This study examined the predictive influence of functional literacy and community engagement on adult learning outcomes among teacher education students at Trece Martires City College. Anchored on John Dewey's Theory of Education and Democracy and Robert Putnam's Social Capital Theory, the study investigated how these variables contribute to the cognitive, psychomotor, and affective domains of adult learning. A quantitative descriptive-correlational research design was employed involving 433 teacher education students selected through convenience sampling. Data were collected using a researcher-developed questionnaire that underwent content validation and reliability testing. Weighted mean, standard deviation, Pearson Product-Moment Correlation, and Multiple Linear Regression Analysis were used to analyze the data. The findings revealed that the respondents demonstrated proficient functional literacy, a great extent of community engagement, and positive adult learning outcomes, with affective outcomes receiving the highest ratings. Functional literacy exhibited a strong positive and statistically significant relationship with adult learning outcomes, while community engagement showed a moderate positive and statistically significant relationship. Regression analysis further established that both variables significantly predicted adult learning outcomes, with functional literacy emerging as the strongest predictor. The study also identified opportunities to strengthen higher-level communication skills, applied numeracy, leadership, and active participation in community organizations. Based on these findings, the study proposed the Civic-Pedagogical Integration Program (CPIP) to enhance communicative competence, applied numeracy, leadership, and community engagement among teacher education students. The study concludes that strengthening functional literacy together with meaningful community engagement can improve the professional preparation and lifelong learning of future educators. The study supports Sustainable Development Goal (SDG) 4: Quality Education, SDG 11: Sustainable Cities and Communities, and SDG 17: Partnerships for the Goals by promoting quality education, community participation, and collaborative educational initiatives. Its findings contribute to educational, institutional, and community sustainability by supporting evidence-based curriculum enhancement and community-based learning.
Francis Quarre, Anarey Cordero, Jenger Gomez et al.· International Journal of Sus...· 0 citations
The findings indicate that nursing students had generally positive levels of AI literacy and attitudes toward AI, and higher AI literacy was associated with more positive attitudes toward AI.
M. Çil, Berna Eren Fidancı, D. Yildiz· Journal of Education and Res...· 0 citations
Nursing students had a wavier level of artificial intelligence literacy of knowledge, attitude and practice and its application in nursing learning, which foster effective integration of artificial intelligence in nursing education.
Haider Mohammed Majeed, Ali Hussein Alek Al-Ganmi, Ali Dhahir Abdulyemmah et al.· F1000Research· 0 citations
Higher AI literacy was associated with lower AI anxiety, and this association was partly accounted for by AI attitudes and AI self-efficacy in the proposed serial mediation model, which suggests that more favorable attitudes may be linked to stronger self-efficacy, which may be related to lower anxiety.
Qin Zeng, Shenghua Zhang, Jiachen Hu et al.· Frontiers in Public Health· 0 citations
University students now use artificial intelligence (AI) tools in several academic activities, including information retrieval, writing support, and problem-solving. While these technologies may enhance learning efficiency, effective student engagement with AI requires relevant psychological characteristics as well as technology-related competencies. Earlier studies indicate that learner characteristics, including mindfulness, may relate to patterns of engagement with digital technologies. Mindfulness, AI literacy, and AI self-efficacy have been studied separately, but their combined relationships remain insufficiently examined. Accordingly, this study examined the associations among these three variables in university students and assessed whether specific dimensions of AI literacy were involved in the statistical association between mindfulness and students' self-rated readiness to use AI-supported tools for academic purposes. The study used a cross-sectional survey design. Undergraduate students at Shandong Xiehe University in China completed an online questionnaire. After screening the responses, 929 valid questionnaires were retained for analysis. Participants were between 18 and 22 years of age. Mindfulness, AI literacy, and AI self-efficacy were assessed using established self-report scales. The analysis first summarized the main study variables using descriptive statistics. Bivariate relationships among mindfulness, AI literacy, and AI self-efficacy were examined using Pearson correlation analysis. PROCESS Model 4 was applied with 5,000 bootstrap resamples to test the proposed indirect-association model. The results indicated positive relationships between mindfulness, AI literacy, and AI self-efficacy. Regression analyses also indicated that mindfulness showed significant relationships with each AI literacy dimension, namely AI awareness, AI usage, AI evaluation, and AI ethics. Mindfulness showed significant positive associations on multiple dimensions of AI self-efficacy. The mediation analyses identified AI literacy represented a significant indirect pathway linking mindfulness with AI self-efficacy. Specifically, the dimensions of AI awareness, AI usage, and AI evaluation showed significant indirect effects, AI ethics did not show consistent indirect effects across the tested models. The findings show that mindfulness was positively associated with AI literacy and AI self-efficacy among university students. They also suggest that students reporting higher AI literacy also reported greater perceived capability in using AI-supported tools, indicating that technological understanding may be statistically related to the association between mindfulness and perceived competence. The results show links between psychological awareness, AI literacy, and students' engagement with AI tools in higher education. The results may inform the development of AI literacy initiatives in higher education that support students' informed, insightful, and confident use of AI tools in academic contexts.