AI beyond the feed: Social media competency as predictors of self-efficacy in artificial intelligence learning

Zhanna M. Sizova 1, Ivan P. Polovikov 2, Ludmila Y. Grebenshchikova 3, Lyudmila M. Smirnova 4, Alexey A. Chistyakov 5 *
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1 Department of Medical and Social Assessment, Emergency, and Ambulatory Practice, Sechenov First Moscow State Medical University, Moscow, RUSSIA
2 Institute of Clinical Medicine, Sechenov First Moscow State Medical University, Moscow, RUSSIA
3 Department of Reproductive Medicine and Perinatology, Tver State Medical University, Tver, RUSSIA
4 Department of Dermatovenereology, Sechenov First Moscow State Medical University, Moscow, RUSSIA
5 Department of Criminal Law, Criminal Procedure and Criminalistics, Peoples’ Friendship University of Russia (RUDN University), Moscow, RUSSIA
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 16, Issue 2, Article No: e202629. https://doi.org/10.30935/ojcmt/18563
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This article belongs to the special issue "Interdisciplinary Perspectives on Communication, Education, and Ethics in the Digital Age"

ABSTRACT

This study examines the relationship between social media competence (SMC) and self-efficacy for artificial intelligence (AI) learning in university students on the basis of social cognitive theory. A quantitative and cross-sectional survey design was used in the study. In the data collection process, SMC scale and AI learning self-efficacy measures were used. The data obtained from 451 undergraduate students studying at Sechenov University in Russia were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings showed that the content generation dimension (beta = .302, p < .001) and the technical usability dimension (beta = .273, p < .001) were the strongest positive predictors of AI learning self-efficacy. The anticipatory reflection dimension showed a weak but statistically significant negative effect (beta = -0.094, p = .034). The content interpretation dimension was found to have a weakly positive but statistically insignificant effect (beta = .073, p = .151). The model explained approximately 25 percent of the variance in AI learning self-efficacy (R² = .251). These findings suggest that competencies developed through active content production and technical use on social media platforms can increase students’ confidence in learning AI. This result is in line with social cognitive theory’s approach that emphasizes direct experience of success as the main source of self-efficacy. The study contributes to understanding how digital competencies transfer to learning confidence in new technological domains and provides practical implications for how social media-based strategies can be integrated into AI education.
Keywords: social media competency, artificial intelligence education, self-efficacy, PLS-SEM

CITATION

Sizova, Z. M., Polovikov, I. P., Grebenshchikova, L. Y., Smirnova, L. M., & Chistyakov, A. A. (2026). AI beyond the feed: Social media competency as predictors of self-efficacy in artificial intelligence learning. Online Journal of Communication and Media Technologies, 16(2), e202629. https://doi.org/10.30935/ojcmt/18563

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