How Cognitive Dissonance Affects Student Engagement and Learning in AI Powered Education Systems

Abstract

This paper examines the impact of cognitive dissonance on student engagement and learning adaptation in AI-powered education. Based on a quantitative research design, data from 270 university teachers in Punjab were retrieved using a self-administered questionnaire. Findings show a moderate negative correlation between cognitive dissonance and student engagement with r = -0.542, p = 0.001, indicating that increased dissonance results in lower motivation and participation levels. A significant effect of the regression analysis for AI-generated feedback is that motivation and learning adaptation increase (B = 0.612, β = 0.548, p = 0.000, R² = 0.476), thus demonstrating the necessity for structured AI feedback. ANOVA results support that strategies of cognitive dissonance reduction highly enhance engagement and learning outcomes significantly (F = 9.76, p = 0.000). The validation lies in the appropriateness of adaptive learning techniques and metacognitive training. These findings recommend that AI-driven education should contain personalized feedback mechanisms and structured interventions to reduce the effects of cognitive dissonance. Longitudinal effects and cross-cultural variations in AI-based learning are left for future studies.