Shifting dynamics in human–machine communication: the interplay of user and chatbot gender across two studies

Abstract

Abstract As conversational agents become a core interface in service, education, and decision support, understanding how users’ self-concept shapes their interaction with gendered AI agents is critical. This study investigates how chatbots’ presented gender, together with users’ gender and gender traits, influence users’ perceptions of chatbot credibility and social attraction. Across two experimental studies ( N₁ = 250, N₂ = 228), participants engaged with a chatbot presented as female, male, or gender-neutral. Study 1 employed a rule-based chatbot, whereas Study 2 used a generative AI chatbot for a more naturalistic interaction. In Study 1, results revealed that users’ gendered self-concept modulates their perceptions of the chatbot, particularly when chatbot gender aligns or misaligns with participants’ self-perceived masculinity or femininity. The more masculine users were, the more they perceived the male chatbot as competent; by contrast, users with higher femininity evaluated the neutral chatbot more favorably in goodwill, trustworthiness, and social attraction. However, these effects were not observed in Study 2, suggesting that advanced AI performance may dilute social identity-based biases. We further discuss how interaction effects reflect broader sociocultural schemas embedded in digital communication and highlight ethical implications for AI design, including risks of reinforcing gender stereotypes and marginalizing non-binary identities. Findings underscore the need for inclusive, identity-aware chatbot design that balances personalization with mindfulness.