AI Literacy, Epistemic Justification, and Synthetic Knowledge Evaluation among University Students: A Structural Equation Modelling Approach

Purpose: AI is increasingly incorporated into higher education, so knowledge of AI systems and critical enquiry of AI-generated knowledge is essential for students. The study explores the relationships between Epistemic Justification, Critical Thinking Disposition, AI Literacy, and Synthetic Knowledge Evaluation among university students. Specifically, it explores the relationships between AI Literacy and Epistemic Justification, the role of Epistemic Justification as a mediator, and Critical Thinking Disposition as a moderator. Methodology: A survey technique of quantitative study is employed; the survey is conducted among 350 university students of New Delhi based on stratified random sampling; the survey instrument is a close ended questionnaire developed by the researcher. For demographic, SPSS is used and, for the Structural Equation Modelling analysis, SmartPLS 4 is used. Findings: The results indicate that AI Literacy and Epistemic Justification predict the Synthetic Knowledge Evaluation and that the interaction between Critical Thinking Disposition yields a modest contribution. The study highlights the need for integrated AI literacy and evidence-based evaluation practices. Unique Contribution to Theory, Practice and Policy: The study contributes to understanding responsible AI engagement by linking technological competence with evidence-based reasoning and critical knowledge evaluation. There are limitations to the cross-sectional design and contextual sample. Future studies could rely on longitudinal and mixed methods approaches.