用户越依赖生成式AI，越容易误信错误健康信息。
Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: An Experimental Investigation
- 通过两组实验测试用户对AI健康信息的信任与依赖关系。
- 高度依赖者更易信任错误信息，且文本高亮无法改善此问题。
- 研究提示需改进界面设计，引导用户批判性评估AI输出。
背景：生成式人工智能（GenAI）在健康信息获取中应用日益广泛，但其对用户信任校准的影响尚不明确。目的：本研究探讨学习性依赖是否影响对生成式健康信息的信任，以及文本高亮能否减少对错误输出的过度依赖。方法：通过两组随机对照实验，分别招募338名大学生和563名亚马逊机械土耳其人参与者。采用2×2组间设计，操纵信息准确性（正确 vs. 错误）和文本高亮（高亮 vs. 无高亮）。使用验证过的量表测量信任与学习性依赖，线性回归模型检验主效应与交互效应。结果：两组实验中，信息准确性均显著提升信任（p < 0.001），学习性依赖与信任正相关（p < 0.05）。准确性和依赖性的交互作用显著（p < 0.001），表明高度依赖者更可能信任错误信息。文本高亮对信任无显著影响，也未调节依赖与信任的关系。结论：学习性依赖削弱了信任校准，使用户更易受错误信息影响。单独使用文本高亮不足以减少过度依赖，亟需更有效的界面设计以促进对GenAI输出的批判性评估。
原文摘要 · Abstract (English)
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.
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