Disclosure willingness anthropomorphism and thinking style shape perceived fit and comfort in AI mental health chatbot interactions

Generative artificial intelligence (AI) conversational agents are increasingly used for everyday, non-clinical mental-health support, yet little is known about when these exchanges feel fitting and comfortable. We used a laboratory mixed-methods design to examine associations among perceived convenience of interaction (PCI), relational attitude (RA), willingness to self-disclose (WSD), anthropomorphism (AN), perceived fit/comfort (FC), and thinking style in 83 participants (43 rational-dominant; 40 experiential-dominant). WSD showed the strongest unique association with FC.

A post hoc reverse model also found a unique FC-WSD association, leaving their temporal order unresolved. AN had no stable direct association with FC, but the WSD-FC association became stronger as AN increased. Group comparisons and continuous Rational-Experiential Inventory (REI) analyses converged: relatively stronger experiential processing was associated with higher AN, whereas relatively stronger rational processing was associated with higher WSD.

The group-blind thematic analysis linked disclosure decisions to privacy, control, social distance, functional usefulness, and affective comfort. Together, the findings identify WSD as a key correlate of FC and show that anthropomorphism conditions this association rather than simply improving the interaction. Clear boundaries, controllability, and adjustable relational cues therefore warrant attention in future design research.

This research was supported by 2026 Sichuan Provincial Philosophy and Social Science Planning Project “Conveying the Spirit through Intelligence: Narrative Translation and International Communication of AI-Generated Animated Micro-Dramas on Bashu Culture” [Grant No.SCJJ26ND439]. Future Imaging Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314100, China College of Media and International Culture, Zhejiang University, Hangzhou, 310058, China AI Design Lab, Dept. of Smart Experience Design, Kookmin University, Seoul, 02707, Republic of Korea Department of Smart Experience Design, Graduate School of Techno Design, Kookmin University, Seoul, 02707, Republic of Korea College of Arts and Crafts, Zhejiang Guangsha Vocational and Technical University of Construction, Jinhua, 322100, China School of Arts, Southeast University, Sipailou Street, NanjingJiangsu, 201620, China The authors declare no competing interests. This study was approved by the ethics committee of Zhejiang University on December 3, 2025 (protocol code: cmic20251139).

The research was conducted in strict accordance with the ethical principles outlined in the Declaration of Helsinki (1964) and its sub-sequent amendments or similar ethical standards. All procedures involving human participants adhered to the institutional and na-tional ethical standards for research. Informed consent was obtained from all subjects involved in the study.

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