The Influence of Artificial Intelligence Social Comparison on Academic Self-Efficacy among University Students in the Era of Generative AI
Keywords:
Artificial Intelligence, Artificial Intelligence Social Comparison, Academic Self-Efficacy, Generative AI, University Students.Abstract
The rapid adoption of generative Artificial Intelligence (AI) technologies, such as ChatGPT, Gemini, Claude, Copilot, and Perplexity, has transformed higher education by improving learning efficiency and academic productivity. However, students increasingly compare their academic abilities with AI-generated outputs, creating a new psychological phenomenon known as Artificial Intelligence Social Comparison. Despite the growing use of AI in education, limited empirical evidence explains how AI-based social comparison influences students' Academic Self-Efficacy. This study aimed to examine the effect of Artificial Intelligence Social Comparison on Academic Self-Efficacy among university students. A quantitative explanatory study with a cross-sectional survey design was conducted involving 312 undergraduate students selected through purposive sampling. Data were collected using structured questionnaires with a five-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results showed that Artificial Intelligence Social Comparison had a significant negative effect on Academic Self-Efficacy (β = −0.684, p < .001). Ability comparison was the strongest predictor of reduced self-efficacy, while upward AI comparison lowered students' confidence and downward comparison slightly increased it. These findings suggest that AI has become an important psychological comparison target that influences students' academic confidence. The study extends Social Comparison Theory within the context of generative AI and provides practical implications for promoting responsible AI integration and strengthening students' confidence in AI-supported learning.
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