Confiding to AI: Impacts of ICE Framework-Based Body Movements of VR Chatbots on User Self-Disclosure and Experience

Chatbots enhanced by VR can deliver rich social cues through both verbal and non-verbal communication. While existing research emphasizes verbal factors and visual anthropomorphism, systematic exploration of body movements remains limited. This study proposes the Interactive-Cheerful-Empathic (ICE) Movements Framework, mapping body movements to three psychological needs: autonomy, competence, and relatedness. We developed a VR chatbot (Hilie) with four movement modes (interactive, cheerful, empathic, and no movement) and conducted a single-factor within-subjects experiment involving 56 university students. Quantitative and qualitative results revealed that chatbots with body movements—particularly cheerful movements—significantly enhanced users’ self-disclosure willingness, satisfaction, trust, and intention to use compared to static counterparts. The ICE framework effectively addresses multi-level psychological needs through coordinated movements. This work pioneers the operationalization of self-determination theory in chatbot design, providing theoretical models and practical guidelines for developing highly anthropomorphic chatbots, while advancing optimization strategies for online mental health services.