Perceptions of AI-CBT: Trust and Barriers in Chinese Postgrads

TL;DR

This study investigates how Chinese graduate students perceive AI-CBT chatbots for mental wellbeing using semi-structured interviews and reflexive thematic analysis. Guided by the Health Belief Model and Theory of Planned Behavior, it reveals conditional openness to AI-CBT driven by perceived benefits (immediacy, privacy) and barriers (privacy, emotional safety, and cultural stigma), as well as attitudinal and normative factors that shape adoption. The findings highlight the need for transparent data practices, more empathic and culturally tailored interactions, and campus-level normalization and governance to enable scalable, acceptable AI-based mental health support in Chinese academic contexts. Methodologically, the work fills a gap by offering context-specific qualitative insights that inform design, deployment, and policy for AI mental health tools in non-Western higher-education settings.

Abstract

The mental well-being of graduate students is an increasing concern, yet the adoption of scalable support remains uneven. Artificial intelligence-powered cognitive behavioral therapy chatbots (AI-CBT) offer low barrier help, but little is known about how Chinese postgraduates perceive and use them. This qualitative study explored perceptions and experiences of AI-CBT chatbots among ten Chinese graduate students recruited through social media. Semi-structured Zoom interviews were conducted and analyzed using reflexive thematic analysis, with the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB) as sensitizing frameworks. The findings indicate a cautious openness to AI-CBT chatbots: perceived usefulness and 24/7 access supported favorable attitudes, while data privacy, emotional safety, and uncertainty about `fit' for complex problems restricted the intention to use. Social norms (e.g., stigma and peer views) and perceived control (digital literacy, language quality) further shaped adoption. The study offers context-specific information to guide the culturally sensitive design, communication, and deployment of AI mental well-being tools for student populations in China and outlines the design implications around transparency, safeguards, and graduated care pathways.