Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: An Experimental Investigation

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AI Summary

This study investigates how learned dependency on generative AI for health information impairs users’ trust calibration and examines whether text highlighting mitigates overreliance on misinformation. Through two randomized controlled experiments manipulating information accuracy and the presence of text highlighting, combined with validated trust and dependency scales and linear regression analyses, the research provides the first empirical evidence that learned dependency significantly and positively predicts trust (p<0.05), rendering highly dependent users more susceptible to accepting inaccurate information. While information accuracy substantially enhances trust (p<0.001), text highlighting fails to effectively moderate the dependency effect or improve trust calibration. These findings underscore the detrimental role of dependency mechanisms in human-AI trust dynamics and raise critical questions about the efficacy of current visualization-based intervention strategies.

Abstract

Background: Generative artificial intelligence (GenAI) is increasingly used for health information, yet its influence on users' trust calibration remains unclear. Objective: This study examines whether learned dependency on GenAI influences trust in AI-generated health information and whether text highlighting reduces overreliance on incorrect outputs. Methods: Two randomized controlled experiments were conducted with 338 college students and 563 Amazon Mechanical Turk participants. Both experiments used a 2 by 2 between-subjects design manipulating information accuracy (correct versus incorrect) and text highlighting (highlight versus no highlight). Trust and learned dependency were measured using validated scales, and linear regression models tested main and interaction effects. Results: In both experiments, information accuracy significantly increased trust (p < 0.001), while learned dependency was positively associated with trust (p < 0.05). The interaction between accuracy and dependency was significant (p < 0.001), indicating that highly dependent users were more likely to trust incorrect AI-generated information. Text highlighting had no significant effect on trust and did not moderate the relationship between dependency and trust. Conclusions: Learned dependency weakens trust calibration, increasing susceptibility to inaccurate AI-generated health information. Text highlighting alone is insufficient to reduce overreliance, highlighting the need for more effective interface designs that encourage critical evaluation of GenAI outputs.

Problem

• Generative AI
• trust calibration
• learned dependency
• health information
• overreliance

Innovation

• learned dependency
• trust calibration
• generative AI
• health information
• human-AI interaction