Understanding Trust in GenAI: The Roles of Reliability, Explanation Quality, and Confidence in Judgment

Generative Artificial Intelligence (GenAI) is increasingly utilised in academic contexts; however, concerns about reliability and transparency raise questions about user trust and adoption. Drawing on Trust in Technology Theory, this study examines how perceived reliability and explanation quality influence trust in GenAI and whether trust mediates their effects on intention to use AI in an academic context.  This research study also examines the role of confidence in judgment as a user-level factor, using survey-based data from 262 participants and hierarchical regression with a mediation analysis. The results show that perceived reliability and explanation quality significantly enhance trust in AI. However, trust partially mediates their relationship with intention to use GenAI. In contrast, confidence in judgment does not significantly predict trust or intention, highlighting a conceptual distinction between users’ self-confidence and trust in AI systems. The findings highlight the importance of perceived reliability and explanation quality in fostering calibrated trust and responsible adoption of GenAI by students and teachers in educational settings.