User gratification with artificial intelligence features: a case study of ChatGPT among mainland Chinese students in Malaysia

This study explores how Chinese international students in Malaysia experience satisfaction when using ChatGPT, an AI chatbot, based on the Uses and Gratifications (U&G) theory. It builds on and refines the framework proposed by Cheng and Jiang (2020) by examining the relationship between five core AI features, which include productivity enhancement, content generation, customer service automation, support and motivation, and collaboration facilitation. These features are analyzed in relation to four types of user gratification: modality, agency, interactivity, and navigability. A quantitative survey method was employed, with data collected from 54 Chinese students via an online questionnaire. Statistical analysis using SPSS confirmed that all five ChatGPT features positively correlate with the four gratification types. Among these, collaboration facilitation consistently emerged as the strongest predictor in most models. Navigability-based gratification showed the highest explanatory power, suggesting that users value smooth, intuitive interface experiences supported by task efficiency and responsiveness. While support and motivation had the smallest effect sizes, they remained statistically significant, highlighting the role of emotional reinforcement in AI-mediated interactions. These findings extend Sundar and Limperos’ (2013) gratification model into the domain of AI chatbots and confirm that satisfaction is shaped not only by access to information but also by interaction quality, cognitive ease, and creative agency. This study offers cross-cultural validation of the U&G theory and highlights ChatGPT’s potential as a multifaceted educational tool.