The Moderating Role of AI Literacy in the Relationships Between Generative AI Reliance, Academic Self-Efficacy, and Cognitive Engagement Among Adolescents
DOI:
https://doi.org/10.61838/kman.jayps.6307Keywords:
generative artificial intelligence, AI literacy, academic self-efficacy, cognitive engagement, adolescents, generative AI reliance, moderation analysisAbstract
Objective: This study aimed to examine the relationships between generative AI reliance, academic self-efficacy, and cognitive engagement among Canadian adolescents and to determine whether AI literacy moderates the associations between generative AI reliance and these two academic outcomes.
Methods and Materials: A cross-sectional correlational design was employed with 612 secondary school students aged 14–18 years from Ontario, British Columbia, Alberta, and Quebec, Canada. Participants completed self-report measures assessing generative AI reliance, AI literacy, academic self-efficacy, and cognitive engagement. Data were analyzed using Pearson correlation coefficients and multiple regression-based moderation analyses using the PROCESS macro in SPSS. Generative AI reliance was specified as the predictor, AI literacy as the moderator, and academic self-efficacy and cognitive engagement as separate dependent variables. Age, gender, grade level, and frequency of academic generative AI use were included as covariates. Interaction effects were examined using simple-slope and Johnson-Neyman analyses.
Findings: Generative AI reliance was significantly and negatively correlated with academic self-efficacy, r = -.36, p < .001, and cognitive engagement, r = -.41, p < .001, whereas AI literacy was positively associated with academic self-efficacy, r = .44, p < .001, and cognitive engagement, r = .51, p < .001. The moderation model predicting academic self-efficacy was significant, F(7, 604) = 51.11, p < .001, R² = .372, and the interaction between generative AI reliance and AI literacy was significant, B = 0.18, SE = 0.04, β = .15, p < .001, ΔR² = .021. The cognitive engagement model was also significant, F(7, 604) = 70.87, p < .001, R² = .451, with a significant interaction effect, B = 0.21, SE = 0.04, β = .17, p < .001, ΔR² = .025. In both models, the negative effects of generative AI reliance weakened as AI literacy increased and became nonsignificant at high levels of AI literacy.
Conclusion: AI literacy appears to function as a protective factor that attenuates the negative associations of generative AI reliance with adolescents’ academic self-efficacy and cognitive engagement, highlighting the importance of developing critical, evaluative, and responsible AI competencies alongside increased educational use of generative AI.
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