AI self-efficacy and satisfaction with studies among Peruvian university students: the indirect role of language learning strategies
Introduction The increasing integration of artificial intelligence (AI) into higher education is transforming how students access learning resources, regulate their learning, and engage in academic activities. However, the mechanisms through which perceived competence in using these technologies rel...
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| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2026
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| Acceso en línea: | Enlace permanente (DOI o Handle) Ver en el sistema de origen |
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| Sumario: | Introduction The increasing integration of artificial intelligence (AI) into higher education is transforming how students access learning resources, regulate their learning, and engage in academic activities. However, the mechanisms through which perceived competence in using these technologies relates to students' academic experiences remain insufficiently understood, particularly in the context of language learning. This study aimed to examine whether language learning strategies statistically accounted for an indirect association between AI self-efficacy and satisfaction with studies among Peruvian university students. Methods A cross-sectional study was conducted with 254 university students. AI self-efficacy, language learning strategies, and satisfaction with studies were assessed, and their relationships were examined using structural equation modeling (SEM). Results The model showed acceptable robust fit (CFI = .954; TLI = .943; RMSEA = .067; SRMR = .055). AI self-efficacy was positively associated with language learning strategies ( β = .431, p < .001), which, in turn, were positively associated with satisfaction with studies ( β = .523, p < .001). The direct association between AI self-efficacy and satisfaction with studies was not statistically significant ( β = .068, p = .421), whereas the indirect association through language learning strategies was statistically significant ( β = .225, p < .001). The model explained 30.9% of the variance in satisfaction with studies. Discussion These findings indicate a statistical indirect association consistent with the hypothesized model, but the cross-sectional design does not establish temporal ordering or causality. The results highlight the potential relevance of integrating AI-related competencies with strategic and self-regulated learning in higher education. |
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