AI aversion? Effects of author disclosure on young people’s perceptions of mental health advice

Abstract

The increasing use of large language models (LLMs), such as ChatGPT, is already impacting how young people seek mental health support online. However, AI aversion, the reluctance or resistance individuals feel toward AI, may influence individuals’ perceptions and willingness to engage with LLM-generated advice. In this mixed-method study, we investigated how 440 young people (aged 17–21) perceived mental health advice from ChatGPT compared with that of health professionals, emphasizing the effect of author disclosure. Participants assessed answers from ChatGPT and health professionals across four dimensions—Validation, Relevance, Clarity, and Utility—and were asked to recommend answers. The findings indicate a preference for AI-generated answers when participants were unaware of the author’s identity: ChatGPT’s answers scored significantly higher on Validation, Relevance, Clarity, and Utility. Conversely, when the author was disclosed, participants favored responses from health professionals and rated their answers significantly higher for Validation, indicating AI aversion. Qualitative data further revealed that participants became more critical when they knew the content was AI-generated, while responses from health professionals were viewed as more credible, empathetic, and tailored. These findings may indicate human favoritism. The study makes the key contribution of identifying how source awareness impacts the reception of AI-generated content in a sensitive domain. To address the potential for AI aversion within help-seeking, our findings suggest the importance of developing hybrid human–AI support models that combine the efficiency of AI with the relational legitimacy of human professionals, improving both the acceptance and impact of digital mental health support.

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