ARTIFICIAL INTELLIGENCE IN THE CLASSROOM: EFFECTS ON MOTIVATION, SELF-EFFICACY, AND STUDENT ACHIEVEMENT

Abstract

As artificial intelligence (AI) becomes increasingly embedded in educational settings, understanding its psychological and academic impacts on learners has become critical. This study investigates the effects of AI-assisted instruction on university students’ learning motivation, self-efficacy, and academic performance. Adopting a quasi-experimental design, 120 undergraduates were divided into two groups: one received AI-supported instruction using an intelligent learning platform, while the other followed a traditional teacher-led approach. Participants completed standardized questionnaires assessing learning motivation and academic self-efficacy, and final course scores were used as performance indicators. Independent samples t-tests revealed that the AI-assisted group outperformed the traditional group across all outcome measures, with statistically significant improvements in motivation, self-efficacy, and academic achievement. Multiple regression analysis further showed that AI instruction, motivation, and self-efficacy were all significant predictors of learning outcomes, jointly explaining over 33% of the variance in final scores. These results highlight the psychological mechanisms through which AI can enhance student learning and suggest that AI technologies, when pedagogically aligned and psychologically supportive, can significantly improve both engagement and performance.