Frontiers in psychology · 2026
Social influence and academic performance: a serial mediation model in AI-enhanced learning.
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Bhunia Anish Kumar, Alzahrani Saeed
Аннотация
INTRODUCTION: This study examines how social influence affects the academic performance of Saudi university students, focusing on the mediating roles of AI self-efficacy, smart learning, academic stress, and mental wellbeing. The study is grounded in Social Learning Theory.
METHODS: A quantitative research design was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 345 university students enrolled at three public universities in Saudi Arabia. A theoretical model based on Social Learning Theory was developed to examine the relationships among social influence, AI self-efficacy, smart learning, academic stress, mental wellbeing, and academic performance.
RESULTS: The findings supported all 10 proposed hypotheses. Social influence had a significant direct effect on academic performance (β = 0.156). Significant indirect effects were also observed through AI self-efficacy(β = 0.223), smart learning (β = 0.050), academic stress (β = 0.032), and mental wellbeing (β = 0.064). The serial mediation pathway was also significant (β = 0.003). All mediation effects were partial, indicating that social influence influences academic performance through both direct and indirect pathways. Among the constructs examined, AI self-efficacy was the strongest predictor of academic performance (β = 0.331).
DISCUSSION: The findings show that social influence affects academic performance through multiple psychological and learning-related pathways. AI self-efficacy emerged as the strongest predictor, highlighting the importance of students' confidence in using AI for learning. Overall, the study extends Social Learning Theory to AI-supported higher education and emphasizes the importance of AI self-efficacy, smart learning, and psychological wellbeing in promoting academic success among Saudi university students.
METHODS: A quantitative research design was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 345 university students enrolled at three public universities in Saudi Arabia. A theoretical model based on Social Learning Theory was developed to examine the relationships among social influence, AI self-efficacy, smart learning, academic stress, mental wellbeing, and academic performance.
RESULTS: The findings supported all 10 proposed hypotheses. Social influence had a significant direct effect on academic performance (β = 0.156). Significant indirect effects were also observed through AI self-efficacy(β = 0.223), smart learning (β = 0.050), academic stress (β = 0.032), and mental wellbeing (β = 0.064). The serial mediation pathway was also significant (β = 0.003). All mediation effects were partial, indicating that social influence influences academic performance through both direct and indirect pathways. Among the constructs examined, AI self-efficacy was the strongest predictor of academic performance (β = 0.331).
DISCUSSION: The findings show that social influence affects academic performance through multiple psychological and learning-related pathways. AI self-efficacy emerged as the strongest predictor, highlighting the importance of students' confidence in using AI for learning. Overall, the study extends Social Learning Theory to AI-supported higher education and emphasizes the importance of AI self-efficacy, smart learning, and psychological wellbeing in promoting academic success among Saudi university students.