🤖 AI Summary
This study investigates the psychological mechanisms underlying university students’ trust formation in AI-powered learning assistants. Moving beyond technology-centric perspectives, it integrates cognitive appraisal, affective response, social relational dynamics, and contextual moderators into the first psychology-driven, four-dimensional dynamic trust framework. The framework conceptualizes trust as a malleable psychological process co-shaped by individual differences and educational contexts, thereby bridging theoretical psychology with educational AI design. Employing a narrative literature review, the study synthesizes empirical and theoretical insights from mental health, human–AI interaction, and automation trust literatures to derive testable hypotheses and key research questions. The findings provide evidence-informed, mechanism-based intervention pathways for educators, educational policymakers, and AI designers—advancing the principled development and implementation of trustworthy, pedagogically grounded AI systems in higher education. (149 words)
📝 Abstract
Artificial intelligence (AI) based learning assistants and chatbots are increasingly integrated into higher education. While these tools are often evaluated in terms of technical performance, their successful and ethical use also depends on psychological factors such as trust, perceived risk, technology anxiety, and students general attitudes toward AI. This paper adopts a psychology oriented perspective to examine how university students form trust in AI based learning assistants. Drawing on recent literature in mental health, human AI interaction, and trust in automation, we propose a conceptual framework that organizes psychological predictors of trust into four groups: cognitive appraisals, affective reactions, social relational factors, and contextual moderators. A narrative review approach synthesizes empirical findings and derives research questions and hypotheses for future studies. The paper highlights that trust in AI is a psychological process shaped by individual differences and learning environments, with practical implications for instructors, administrators, and designers of educational AI systems.