Predicting Postgraduates’ Use Behavior of AI-Based Chatbots for Academic Writing: Based on the UTAUT2 Model

Abstract

Based on Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research investigates the key factors that influence postgraduate students’ engagement with Artificial Intelligence (AI)-powered chatbots in the context of academic writing, while also analyzing how gender moderates these relationships. A total of 232 postgraduate participants were randomly chosen to complete a structured questionnaire evaluating their responses to nine core constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, behavioral intention, and actual use. The collected data were examined using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings reveal that effort expectancy, habit, and social influence significantly predict behavioral intention, whereas performance expectancy, hedonic motivation, price value, and facilitating conditions do not have a notable influence. Additionally, behavioral intention, habit, and facilitating conditions exert a significant positive effect on actual use behavior in AI-assisted academic writing. These predictors jointly account for 68.0% of the variance in the usage behavior of AI chatbots. Notably, gender significantly moderates the effects of habit and behavioral intention on usage behavior, while its moderating effect on facilitating conditions is not statistically significant. This study constructs a predictive model for postgraduates’ adoption behaviors in AI-based chatbots academic writing, aiming to elevate their writing proficiency, optimize educational decisions, and encourage their more proactive adoption of this technology. Moreover, it provides new theoretical and practical insights for educational technology in the higher education.

Ähnliche Arbeiten