See, trust, and interact: how AI disclosure shapes high school students’ trust
DOI:
https://doi.org/10.47989/ir31iConf64165Keywords:
Artificial Intelligence Generated Content(AIGC), AI label, User trust, Human - AI interaction, Information disclosureAbstract
Introduction. The rise of AI-generated content challenges adolescents’ ability to evaluate information and calibrate trust. This study explores how AI disclosure influences high school students’ attention, trust, and interaction with AI-generated news and comments.
Method. A field experiment was conducted at a county-level high school in Henan with 60 students. Participants were randomly assigned to a control group (no disclosure) or one of two experimental groups (simple vs. detailed disclosure), enabling examination of group-level effects. Data collection combined eye-tracking, post-test questionnaires, and interviews.
Analysis. Eye-tracking metrics and survey data were analysed quantitatively to examine the main and moderating effects of AI disclosure, while interview transcripts were thematically coded to provide qualitative insights.
Results. Simple disclosure increased attention and trust in AI bots but reduced trust and sharing for news content. Detailed disclosure lowered engagement overall, slightly reducing trust in conversational settings and strongly reducing news-sharing. Individual differences moderated these effects: light internet users benefited most from simple labels, whereas heavy users showed stronger gains from detailed explanations in AI trust and technical understanding.
Conclusion. AI disclosure produces context-dependent effects. Effective design should align label complexity with content type and user experience to provide guidance for ethical AI integration in education and social media.
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