JMIR Publications

ABSTRACT

Background:

Generative artificial intelligence (GenAI) is increasingly used by health information consumers to interpret medical content and support decision-making. While these systems provide accessible, timely information, they may also produce inaccurate or misleading outputs. Effective use of GenAI, therefore, depends on users’ ability to calibrate trust based on information accuracy. However, little is known about how learned dependency on GenAI influences trust calibration in health information contexts.

Objective:

This study examines how learned dependency on GenAI affects health information consumers’ trust calibration in AI-generated information and whether visual attention cues (e.g., highlighting critical information) mitigate overreliance on incorrect outputs.

Methods:

We conducted a randomized controlled experiment with 338 participants. The study employed a 2 × 2 design manipulating (1) information accuracy (correct vs incorrect) and (2) visual attention cues (highlight vs no highlight). Participants evaluated AI-generated health information presented alongside source text. Trust was measured using a multi-item scale, and learned dependency on GenAI was assessed using a validated self-reported measure. Linear regression models were used to examine main and interaction effects.

Results:

Information accuracy had a strong positive effect on trust (β = 2.107, 95% CI [1.337, 2.878], p < .001), indicating that participants generally trusted correct information more than incorrect information. Learned dependency on GenAI was also positively associated with trust (β = 0.277, 95% CI [0.033, 0.521], p = .026). Importantly, the interaction between information accuracy and learned dependency was negative and significant (β = −0.399, 95% CI [−0.695, −0.104], p = .008), suggesting that higher dependency reduces users’ ability to differentiate between accurate and inaccurate information. In contrast, visual attention cues did not significantly affect trust (β = 0.149, 95% CI [−0.622, 0.920], p = .704), nor did they moderate the effect of dependency (β = −0.009, 95% CI [−0.305, 0.287], p = .950).

Conclusions:

This study demonstrates that while users generally trust accurate AI-generated health information more than inaccurate information, learned dependency weakens trust calibration, increasing susceptibility to incorrect outputs. Visual attention cues alone are insufficient to mitigate this effect. These findings highlight the need for more effective design interventions to support critical evaluation and reduce overreliance on GenAI in health information environments. Keywords: Learned Dependency; GenAI; Trust Calibration; Attention Mechanism; Automation Bias; Health Information; Human–GenAI Interaction.