In delivering mental health services, the ability of AI chatbots to emulate natural interpersonal interactions with users is of vital importance. However, perceived lack of human-like capabilities and traits in these chatbots may hinder user engagement in such interactions. This study investigated two dimensions of anthropomorphism in human–chatbot interactions: a content-based factor (level of emotional support: high vs. low) and a source-based factor (chatbot profile: human-like vs. machine-like) within the context of mental health interactions. It specifically explored how these factors can encourage users to disclose personal information, a social process traditionally reserved for human-to-human interactions, by reducing psychological reactance that arises during conversations with the chatbot. Results from an experimental study with GPT-4–based AI chatbots revealed that high emotional support from a chatbot, particularly when presented with a human-like profile, effectively reduced psychological reactance. This reduction was associated with increased personal disclosure during interactions, which, in turn, promoted the adoption of adaptive coping strategies for managing stress. These findings offer practical insights for designing emotionally intelligent AI chatbots.
Kim et al. (2025) studied this question.