Predicting Chinese Postgraduates’ Intention to Use Generative Artificial Intelligence in Academic Writing: A Sequential Exploratory Mixed-Method Study

Abstract

Generative Artificial Intelligence (GenAI) is exerting an increasingly profound influence on education. However, existing research lacks a systematic investigation into postgraduate students’ behavioral patterns and influencing mechanisms in academic writing. Addressing this gap, the first phase of this study collected 49 qualitative responses from Chinese postgraduates, identifying six key constructs: perceived usefulness, facilitating conditions, information privacy risk, security risk, attitude, and distrust. In the second phase, drawing on the Technology Acceptance Model (TAM) and Distrust Theory, Structural Equation Modeling (SEM) was conducted with 382 survey responses. The findings reveal that attitude, facilitating conditions, perceived usefulness, and security risk are significant direct predictors of intention to use GenAI. Attitude fully mediates the effect of distrust and partially mediates the effects of facilitating conditions, perceived usefulness, and security risk. This model explains 67.9% of the variance in usage intention. Highlighting attitude and risk perception as key mechanisms, this study contributes by integrating TAM with Distrust Theory, providing novel evidence from the Chinese postgraduate context and offering practical implications for higher education.

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