Results show that mindfulness was positively associated with AI literacy and AI self-efficacy among university students, and 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.
Abstract
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.
This study aims to investigate the key predictors of artificial intelligence literacy (AI literacy). It explores the mediating role of digital literacy (DL) in the relationship between psychological (interest, attitude and self-efficacy) and experiential (prior experience with AI tools) factors and AI literacy.
This study used a survey-based quantitative research methodology. Data were obtained from 754 students enrolled in various programs at the University of the Punjab. Structural equation modeling (SEM) was used to test direct and mediated relationships among constructs.
Interest, attitude, self-efficacy and prior experience significantly predicted AI literacy, both directly and indirectly through DL. Self-efficacy demonstrated the strongest total effect on AI literacy. Prior experience with AI tools significantly improved DL, which in turn enhanced AI literacy. The results highlight a dynamic interplay between psychological readiness and experiential learning, positioning DL as a crucial mediator in the development of AI literacy.
Educators and policymakers should design curricula that foster digital competence, encourage early exposure to AI tools and build learner self-efficacy. Structured, hands-on experience with AI technologies can support the development of confident and competent AI users.
This study presents a comprehensive model linking psychological and experiential variables to AI literacy through DL. It offers both theoretical advancement and practical guidance for educational stakeholders preparing learners for AI-integrated futures.
Artificial intelligence (AI) is increasingly reshaping higher education, yet the psychological mechanisms that determine when and for whom AI-assisted learning translates into academic success remain insufficiently understood, particularly within developing educational contexts. This study examined the relationship between artificial intelligence-assisted learning and academic achievement among university students, with academic self-efficacy tested as a moderating variable, drawing on Bandura's Social Cognitive Theory. A quantitative, cross-sectional survey design was employed, and data were collected from 477 undergraduate and postgraduate students enrolled in public and private universities across Punjab and Sindh, Pakistan, using a convenience sampling technique. A structured questionnaire comprising validated scales measuring artificial intelligence-assisted learning, academic self-efficacy, and academic achievement was administered via Google Forms, and data were analyzed using IBM SPSS Version 27. Pearson correlation analysis revealed a strong, statistically significant positive relationship between artificial intelligence-assisted learning and academic achievement (r = .684, p < .001). Multiple linear regression confirmed that artificial intelligence-assisted learning significantly predicted academic achievement, accounting for 49.9% of the variance (R² = .499, F(1, 475) = 470.836, p < .001). An independent samples t-test showed that students with high academic self-efficacy achieved significantly higher academic outcomes than those with low academic self-efficacy (t(475) = -14.126, p < .001). Most notably, hierarchical multiple regression analysis demonstrated that academic self-efficacy significantly moderated the relationship between artificial intelligence-assisted learning and academic achievement (ΔR² = .029, ΔF(1, 473) = 12.87, p < .001), with the interaction term emerging as a significant predictor (B = .156, β = .142, p < .001), indicating that the positive effect of artificial intelligence-assisted learning on academic achievement was stronger among students with higher academic self-efficacy. These findings extend Social Cognitive Theory into AI-mediated educational settings and suggest that the effectiveness of AI-assisted learning technologies is conditional upon students' belief in their own academic capabilities. The study offers practical implications for higher education institutions seeking to maximize the benefits of AI-based learning tools by simultaneously strengthening students' academic self-efficacy.
Shumaira Rahim, Ziauddin, Saima Zaman Khalil et al.· Aposta: Revista de Ciencias...· 0 citations
Generative artificial intelligence (AI) is increasingly embedded in university students’ writing, information retrieval, knowledge organisation and task completion. Although AI tools may improve learning convenience and access to resources, they may also generate technological uncertainty, pressure to adapt to new competencies and concerns about future development. As an important negative emotional response in intelligent technology contexts, AI anxiety may be closely associated with university students’ academic motivation and psychological adaptation.
This cross-sectional questionnaire study recruited 1,484 university students in China using convenience sampling. Participants completed measures of AI anxiety, emotion regulation and academic motivation through the Wenjuanxing online survey platform. Descriptive statistics, Pearson correlation analysis and regression analyses were used to examine associations among variables. The PROCESS macro was used to test the mediation effect and the moderated mediation effect. All indirect effects were estimated using 5,000 bootstrap samples and 95% confidence intervals.
AI anxiety was negatively associated with both emotion regulation and academic motivation, whereas emotion regulation was positively associated with academic motivation. Bootstrap analyses showed a significant negative indirect association between AI anxiety and academic motivation through emotion regulation. Gender significantly moderated the association between emotion regulation and academic motivation, with a stronger positive association among male students.
AI anxiety may constitute a psychological barrier to students’ motivational adaptation to AI-supported learning. Emotion regulation represents one psychological pathway linking AI anxiety to academic motivation, and gender constitutes a boundary condition for this association. These findings suggest that AI literacy education should integrate emotion-regulation and metacognitive support with differentiated learning assistance. Given the cross-sectional and self-reported nature of the data, the findings require further validation using longitudinal or experimental designs.
Background As artificial intelligence (AI) becomes increasingly integrated into education, fostering student creativity is a critical priority. While prior research suggests a positive link between AI literacy and creative self-efficacy, the underlying psychological mechanisms and key contextual factors remain largely unexplored. This study aimed to address this gap by examining the mediating roles of learning adaptability (cognitive, behavioral, and emotional) and the moderating role of teacher support in this relationship. Methods A total of 509 university engineering students (68.6% male; M age = 19.36 years) participated in a survey. They completed validated measures of AI literacy, learning adaptability, teacher support, and creative self-efficacy. A moderated mediation model was tested using statistical analysis. Results The findings indicated a significant positive association between AI literacy and creative self-efficacy. This relationship was significantly mediated by behavioral and emotional adaptability, but not by cognitive adaptability. Furthermore, teacher support moderated the positive effect of AI literacy on emotional adaptability; this effect was stronger for students who perceived higher levels of teacher support. Conclusion The study advances a nuanced model demonstrating that AI literacy enhances creative confidence primarily by fostering a willingness to act (behavioral adaptability) and bolstering emotional resilience (emotional adaptability). Teacher support is identified as a crucial contextual resource that amplifies this positive process. These findings offer valuable insights for educators and policymakers aiming to design interventions that maximize the creative potential of AI in learning environments.
Lu Pan, Xue Shuang, Yiping Liu· Frontiers in Psychology· 0 citations
Artificial intelligence (AI) is progressively transforming higher education by influencing students’ learning experiences, motivation, and satisfaction with studies. However, limited evidence exists regarding the psychological mechanisms through which AI-related competencies contribute to students’ academic well-being, particularly in Latin American university contexts. This study examined the mediating role of research motivation in the relationship between AI self-efficacy and satisfaction with studies among Peruvian university students. A quantitative, cross-sectional, and explanatory design was employed with a sample of 559 Peruvian university students. Participants completed validated instruments assessing AI self-efficacy, research motivation, and satisfaction with studies. Data were analyzed using structural equation modeling (SEM) with robust estimators. The findings revealed positive and statistically significant relationships among all study variables. AI self-efficacy significantly predicted research motivation (
β
= .313,
p
< .001) and satisfaction with studies (
β
= .100,
p
= .039). In addition, research motivation significantly predicted satisfaction with studies (
β
= .448,
p
< .001) and exerted a partial mediating effect, accounting for 58.3% of the total effect of AI self-efficacy on satisfaction with studies. The results suggest that students’ perceived competence in the use of AI-based technologies contributes to satisfaction with studies both directly and indirectly through the strengthening of research motivation. These findings highlight the importance of promoting digital literacy, AI self-efficacy, and research-oriented pedagogical strategies to foster student engagement and well-being in higher education.
Willian Huamanttupa Mar, Analuz Maria Quispe-Mamani, Carlos D. Abanto-Ramírez et al.· Frontiers in Education· 0 citations
Artificial Intelligence (AI)-supported education is increasingly transforming higher education by providing personalized learning, immediate academic assistance, and flexible access to educational resources; however, its psychological implications for students remain an important area of investigation. The present study examined the relationships among Artificial Intelligence-Supported Education, Student Anxiety, Academic Engagement, and Coping Self-Efficacy, with particular emphasis on the moderating role of coping self-efficacy in the relationship between anxiety and academic engagement. A quantitative, cross-sectional research design was employed, and data were collected from 268 university students from higher education institutions in Punjab and Sindh, Pakistan. Data were collected through an adopted structured questionnaire using online Google Forms, and the completed responses were organized in Microsoft Excel and analyzed using IBM SPSS. Descriptive statistics, Cronbach’s alpha reliability analysis, Pearson correlation, multiple linear regression, independent-samples t-test, one-way ANOVA, post-hoc analysis, and moderation analysis were employed. The findings indicated significant relationships among the major study variables, with AI-supported education being positively associated with academic engagement and negatively associated with student anxiety. Student anxiety was negatively associated with academic engagement, whereas coping self-efficacy demonstrated a positive association with academic engagement. The regression findings further indicated that AI-supported education, student anxiety, and coping self-efficacy were significant predictors of academic engagement. The moderation findings demonstrated that coping self-efficacy significantly weakened the negative relationship between student anxiety and academic engagement, highlighting its potential protective role in students’ academic experiences. The study contributes to the emerging literature on AI-supported higher education by demonstrating that the effectiveness of AI-based learning should be considered alongside students’ psychological well-being and coping resources. The findings have practical implications for universities, educators, and policymakers in developing AI-supported learning environments that promote academic engagement while addressing student anxiety and strengthening coping capabilities.
Unknown authors· Journal of Global Social Tra...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
