The skill-based dimensions of AI literacy were positively associated with AI dependency, whereas AI self-efficacy and academic self-efficacy were both negatively associated, suggesting a unified compensatory self-efficacy mechanism.
Abstract
The integration of artificial intelligence (AI) tools in higher education raises important questions about how students engage with these technologies and what shapes that engagement. This study examined how AI literacy, academic self-efficacy, and self-regulated resource management strategies are associated with students’ reported AI dependency. Participants were 478 students from Israeli higher education institutions who completed a cross-sectional online survey assessing AI dependency, AI literacy (four subscales), academic self-efficacy, and resource management strategies (time and study management, effort regulation, and help seeking). Multiple regression and K-means cluster analysis were used. The skill-based dimensions of AI literacy were positively associated with AI dependency, whereas AI self-efficacy and academic self-efficacy were both negatively associated, suggesting a unified compensatory self-efficacy mechanism. Effort regulation also predicted lower dependency, while general academic help seeking predicted higher dependency. The model explained 27.3% of the variance in AI dependency. Cluster analysis identified four learner profiles differing in literacy-dependency combinations and in self-regulatory resources. The findings suggest that fostering AI literacy alone is insufficient. Developing students’ self-efficacy beliefs and self-regulated learning practices appears equally important for promoting balanced and intentional AI engagement in higher education.
Artificial intelligence (AI) is rapidly being integrated into university curricula, yet quantitative indicators of AI use reveal little about how learners use AI as a learning resource or what educational outcomes follow. This cross-sectional survey study of 212 university students enrolled in AI-integrated courses examined the associations of AI literacy, instructor feedback, and two single-item AI use indicators—the proportion of in-class AI use and total weekly AI use time—with self-perceived cognitive learning outcomes, measured across the six cognitive processes of the revised Bloom’s taxonomy. Confirmatory factor analyses supported multidimensional and higher-order structures, but the cognitive domains overlapped substantially (interfactor correlations up to 0.943; HTMT up to 0.946), so domain-level distinctions should be interpreted with caution. A regression model with the four predictors explained 46.3% of the variance in overall self-perceived cognitive learning outcomes (R2 = 0.463, adjusted R2 = 0.452). When all predictors were considered simultaneously, only AI literacy showed a significant positive association (B = 0.615, β = 0.593, 95% CI [0.475, 0.754], p < 0.001); the data did not provide evidence for independent associations of instructor feedback or the two AI use indicators, whose weaker associations may partly reflect their single-item measurement. AI literacy remained significantly associated with all six cognitive domains after Benjamini–Hochberg correction. These findings suggest—within the limits of a cross-sectional, self-report design—that the quantity of AI use and learners’ competency to understand, evaluate, and self-regulate AI use are empirically distinct indicators that universities should measure separately.
Aim/Purpose
This study investigated how learner-related factors: expectancy value, AI literacy, and self-regulated learning influence university students’ engagement in AI-assisted EFL learning environments.
Background
While AI-assisted language learning offers personalized and adaptive learning opportunities, its effectiveness depends on learners’ motivation, technological understanding, and ability to regulate their own learning.
Methodology
A survey research design was employed using a structured questionnaire developed from relevant literature and pilot tested to ensure validity and reliability. The sample consisted of 322 university students from eight universities across northern, central, southern, and eastern Taiwan. Data were analyzed using descriptive statistics, independent-samples t-tests, and one-way ANOVA.
Contribution
This study provides empirical evidence on how expectancy value, AI literacy, and self-regulated learning jointly shape student engagement in AI-assisted EFL contexts, extending expectancy-value theory into technology-enhanced language learning.
Findings
Results indicate that students reported relatively high levels of AI literacy, expectancy value, and self-regulated learning. Among sub-dimensions, AI ethics scored the highest, while resource management scored the lowest. No significant gender differences were found. However, significant differences emerged across English proficiency levels for all three variables, suggesting that perceived proficiency plays a key role in shaping motivation, AI literacy, and learning regulation.
Recommendations for Practitioners
Educators should design AI-assisted EFL activities that enhance learners’ motivation, develop AI literacy (especially ethical awareness), and support self-regulated learning strategies, particularly in resource management.
Recommendations for Researchers
Future studies should explore causal relationships among these variables, incorporate longitudinal designs, and examine how instructional interventions can strengthen AI literacy and self-regulated learning in diverse contexts.
Impact on Society
By improving understanding of learner factors in AI-assisted education, this study supports the development of more effective and equitable technology-enhanced language learning environments.
Future Research
Further research should investigate how different types of AI tools influence learning outcomes, as well as how individual differences such as proficiency, motivation, and digital competence interact over time in AI-supported learning contexts.
Kate Tzu-Ching Chen, Ming-Tzer Lin· InSITE Conference· 0 citations
Artificial intelligence (AI) is increasingly reshaping higher education, requiring university teachers to adapt their pedagogical roles, technological competence, and professional engagement. Drawing on the Job Demands–Resources (JD‐R) model, this study examined how organizational support and AI literacy were associated with university teachers' work engagement in AI‐supported teaching, with professional identity, resilience, and self‐efficacy as mediating personal resources. A questionnaire survey was conducted among 1135 Chinese university teachers with experience in AI‐supported teaching. Data were analysed using structural equation modelling (SEM), mediation analysis, and network analysis. SEM results showed that organizational support and AI literacy were positively associated with professional identity, resilience, and self‐efficacy, which were further positively associated with work engagement. Mediation analysis indicated that professional identity, resilience, and self‐efficacy mediated the relationships between organizational support, AI literacy, and work engagement. Network analysis further revealed that work engagement was the most central node, with self‐efficacy showing the strongest connection with engagement. These findings extend the JD‐R model to AI‐supported higher education and highlight the importance of institutional support, AI literacy development, and psychological resource building in enhancing teachers' engagement.
Xinxing Wu· European Journal of Educatio...· 0 citations
Purpose: The growing adoption of artificial intelligence (AI)-based assessment in higher education has created new opportunities to enhance learning evaluation. However, empirical evidence explaining how students’ digital literacy and self-efficacy relate to AI-based assessment use and perceived academic performance remains limited, particularly in developing higher education contexts. This study examined the structural relationships among digital literacy, self-efficacy, AI-based assessment use, and perceived academic performance among university students.Method: A quantitative cross-sectional survey was conducted using purposive sampling involving 100 undergraduate students from five teacher education programs at Universitas Nahdlatul Ulama Lampung, Indonesia. Data were collected through a structured questionnaire comprising multi-item measures of digital literacy, self-efficacy, AI-based assessment use, and perceived academic performance. The proposed structural relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).Findings: The results indicated that digital literacy was positively associated with self-efficacy and AI-based assessment use. Self-efficacy showed a significant positive relationship with perceived academic performance and emerged as the strongest predictor of this construct. In contrast, AI-based assessment use and digital literacy were not directly associated with perceived academic performance. These findings suggest that students’ psychological readiness plays a more important role than technology use alone in explaining perceived academic performance within AI-supported assessment environments.Significance: Unlike previous studies that primarily emphasize technology acceptance or technological effectiveness, this study integrates digital literacy and self-efficacy within a structural model of AI-based assessment use to explain students’ perceived academic performance in an Indonesian higher education context. The findings provide practical implications for higher education institutions seeking to strengthen students’ digital competencies and self-efficacy to support the effective implementation of AI-based assessment.
The findings suggest that student engagement in AI‐assisted learning is linked not only to the availability of technological and interpersonal support, but also to the extent to which students experience learning as focused, absorbing and meaningful.
Ping Xu, Yongbi Zhi· European Journal of Educatio...· 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.
A. Aldbyani, Guiyun Wang, Chuanxia Zhang et al.· Scientific Reports· 0 citations
