The study enrolled students from several academic fields at Universidad Peruana Unión in the Puno Region. Participants were in their second through fifth years of university, and all were enrolled during the second academic semester of 2025. The sample included 137 female and 117 male students: 91 studied humanities and education, 78 engineering and architecture, 46 business sciences, 34 health sciences and five theology. English-language learning was a shared institutional requirement rather than a subject restricted to language majors, allowing the analysis to examine language strategies within a multidisciplinary student body. English proficiency was self-reported: 68.5% described their proficiency as basic, 28.7% as intermediate and 2.8% as advanced.

Researchers assessed AI self-efficacy with the six-item General Self-Efficacy Scale for the Use of Artificial Intelligence, adapted for Peruvian university students. Each item was rated from 1, “not at all true,” to 4, “exactly true.” Language-learning strategies were measured with an 18-item inventory covering memory, cognitive, compensation, metacognitive, affective and social strategies, with responses ranging from “never or almost never” to “always or almost always.” These dimensions capture practices such as retaining and processing language information, filling knowledge gaps, planning and monitoring learning, managing emotions and learning through interaction. Satisfaction with studies was measured with a three-item scale evaluating students’ overall academic experience. All data came from the same digital questionnaire, with no personally identifiable information collected.

Before estimating the proposed relationships, the researchers examined the instruments with confirmatory factor analysis. The three-factor measurement model showed acceptable robust fit, with a comparative fit index of .954, Tucker–Lewis index of .943, root mean square error of approximation of .067 and standardized root mean square residual of .055. All factor loadings were statistically significant, ranging from .613 to .767 for AI self-efficacy, .726 to .849 for the six strategy dimensions and .795 to .876 for satisfaction with studies. Heterotrait–monotrait ratios were below the .85 criterion used to assess construct distinctiveness, indicating that the measures represented related but distinguishable concepts. The structural model was estimated with robust maximum likelihood, and a complementary bootstrap analysis with 5,000 resamples examined uncertainty in the indirect effect.

In the structural model, AI self-efficacy was positively associated with language-learning strategies, with a standardized coefficient of .431 and p less than .001. This path accounted for 18.5% of the variance in the latent strategy construct. The descriptive analysis showed a corresponding positive correlation between the two measures, r = .37, p less than .001. The pattern means that students reporting greater confidence in their ability to use AI also tended to report more frequent or extensive use of strategies for managing English learning. It does not show that using AI caused students to adopt those strategies, nor does it mean that greater AI use automatically produces better strategic learning.

Language-learning strategies had the strongest standardized association with satisfaction with studies, with a coefficient of .523 and p less than .001. The strategies–satisfaction correlation was r = .51, p less than .001, compared with r = .25, p less than .001, between AI self-efficacy and satisfaction. Together, AI self-efficacy and language-learning strategies explained 30.9% of the variance in the modeled satisfaction variable. The association between strategies and satisfaction was consistent with the idea that students who plan, monitor, evaluate and adapt their learning, while managing cognitive, emotional and social demands, may also evaluate their wider university experience more favorably. The study did not determine whether students’ satisfaction with English learning specifically drove this result; satisfaction was assessed more broadly as an evaluation of their studies.

The direct path from AI self-efficacy to satisfaction was small and statistically nonsignificant after language-learning strategies were included in the model, with a standardized coefficient of .068 and p = .421. The standardized indirect effect was .225, p less than .001, and the total association was .294, p = .001. In the bootstrap analysis, the unstandardized indirect effect was B = .360, with a 95% confidence interval from .190 to .576; because the interval excluded zero, the result was consistent with the primary model. The findings therefore support the authors’ fourth hypothesis at a statistical level, but not a claim that language strategies transmit the influence of AI confidence in a proven causal chain. The ordering remains theoretically specified rather than temporally demonstrated.

The results offer a more qualified view of AI confidence than a simple technology-centered argument would suggest. AI self-efficacy reflects students’ perceived capability to use artificial intelligence effectively for academic tasks, whereas satisfaction with studies is a broader judgment about their educational experience. The model indicates that much of the observed relationship between these constructs was statistically accounted for by language-learning strategies. It also places particular emphasis on what students do with their perceived technological capability: organizing language learning, monitoring progress, responding to difficulties, regulating emotions and engaging socially may be more closely tied to academic satisfaction than confidence in operating AI alone. The study did not collect detailed information about how often students used AI, which applications they used or what purposes those applications served, so it could not determine whether patterns differed between occasional, intensive or purpose-specific users.

Several limitations constrain the findings. The convenience sample came from one university, limiting generalizability to Peruvian higher education as a whole and to institutions with different curricula, technology access or approaches to English instruction. All variables were self-reported at a single time point, creating the possibility of social desirability, subjective interpretation and common-method effects. The cross-sectional design cannot show whether changes in AI self-efficacy precede changes in strategy use, or whether strategy use precedes changes in satisfaction. In addition, the six strategy dimensions were modeled as indicators of an overall latent construct, so the analysis could not reveal whether particular forms of strategic learning, such as cognitive, metacognitive or affective strategies, were more strongly related to either AI confidence or satisfaction. The findings consequently identify a pattern of associations rather than a tested intervention, effect of AI use or proven educational mechanism.

For universities exploring how artificial intelligence fits into learning, the results point toward a student-centered interpretation of technological competence. The authors suggest that AI training could be considered alongside support for metacognitive planning and monitoring, information processing, emotional regulation and collaborative learning. Within English education, students might use AI to organize activities, seek feedback, practice skills, monitor progress and critically reflect on generated responses rather than treating it only as a tool for completing tasks. Those are educational directions suggested by the model, not practices tested in this study, and the researchers did not establish that any specific combination of AI training and strategy instruction improves satisfaction. Longitudinal, experimental and multicenter research using behavioral measures of AI use would be needed to determine whether the observed relationships change over time, respond to intervention or hold across different universities and student groups. The central result is that AI confidence alone was not closely linked to satisfaction in the model, whereas confidence and satisfaction were connected through the strategic ways students reported managing their language learning.

Subject of Research: Associations among AI self-efficacy, language-learning strategies and academic satisfaction in Peruvian undergraduates

Article Title: AI self-efficacy and satisfaction with studies among Peruvian university students: the indirect role of language learning strategies

Article References: Sacsi-Huamani, Y., Chusi-Pacco, R., Saire-Perez, Y. Z., Mendoza-Gutierrez, Y., Coaquira Tuco, C. M., & Huamanttupa Mar, W. (2026). AI self-efficacy and satisfaction with studies among Peruvian university students: the indirect role of language learning strategies. Frontiers in Education, 11, 1978846. https://doi.org/10.3389/feduc.2026.1978846

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1978846

Keywords: artificial intelligence, AI self-efficacy, language learning, study strategies, academic satisfaction, university students, Peru, structural equation modeling, higher education, self-efficacy, satisfaction, studies