ArticleFrontiers in psychology2026
Impact of multimodal AI literacy, critical thinking, and resilience on academic performance in early childhood education: a multiple chain mediation analysis of multimodal AI self-efficacy and cognitive engagement.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: With the continuous integration of multimodal artificial intelligence (AI) into higher education, students majoring in Early Childhood Education (ECE) are expected not only to master the operational use of multimodal AI tools but also to develop the ability to evaluate, reflect on, and adapt to intelligent learning environments. However, existing studies have mainly focused on engineering and information technology contexts, while limited attention has been paid to ECE students and to the mechanisms through which multimodal AI self-efficacy and cognitive engagement influence academic achievement. Methods: This study focused on university students majoring in ECE and collected 458 valid responses through a questionnaire survey. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine the effects of multimodal AI literacy, critical thinking, and resilience on academic achievement, as well as the mediating roles of multimodal AI self-efficacy and cognitive engagement. Results: The results showed that multimodal AI literacy, critical thinking, and resilience had significant positive effects on academic achievement. Multimodal AI self-efficacy and cognitive engagement played important mediating roles in the relationships between these antecedent variables and academic achievement. Among the antecedents, critical thinking had the strongest effects on both cognitive engagement and multimodal AI self-efficacy, whereas the direct effect of resilience on cognitive engagement was not significant. Discussion: These findings enrich the theoretical understanding of how academic achievement is formed in AI-supported educational contexts. The study also provides practical implications for universities seeking to optimize ECE curricula, enhance students' multimodal AI application skills, and cultivate their broader competencies for learning and professional development in intelligent educational environments.
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