Managing Hallucination Risk in LLM-Generated Outputs: The Roles of Prompting Literacy, Trust Calibration, and Verification Behavior

作者：Tam T. Nguyen, Phu Nguyen Huynh Hoai · 发表于：International Conference on Advances in Information Technology · 年份：2026 · DOI：10.1145/3816713.3820249 · 研究领域：Computer Science

Large language models (LLMs) are increasingly used by student users for academic learning and knowledge work, but their ability to generate fluent yet inaccurate outputs creates hallucination risk. Prior research has mainly examined generative AI adoption and ethical concerns, while less is known about how student users verify LLM-generated outputs before relying on them. Drawing on AI literacy, epistemic vigilance theory, trust calibration, and processing fluency theory, this study develops a dual-path model examining how prompting literacy is associated with hallucination-risk management among student users. Survey data were collected from 210 students who had used LLM tools for academic tasks. After removing 10 straight-lining responses, 200 valid responses were analyzed using PLS-SEM. The findings indicate that prompting literacy is positively associated with hallucination awareness, trust calibration, and perceived output fluency. Hallucination awareness and trust calibration are positively associated with scenario-based self-reported verification behavior, which is strongly associated with responsible LLM use. However, perceived output fluency is not significantly associated with verification behavior. The findings suggest that responsible LLM use among student users is associated mainly with protective mechanisms of awareness, calibrated trust, and verification rather than with fluency-based risk. This study contributes by shifting attention from LLM adoption to verifi...