With the increasing integration of AI chatbots into education, understanding the factors that influence students' adoption intentions offers an empirical basis for further research in educational technology. However, empirical studies integrating multiple analytical approaches remain limited, particularly in China context. This study investigated Chinese university students' intention to adopt AI chatbots for learning by examining six individual-level predictors: performance expectancy (PE), effort expectancy (EE), social influence (SI), perceived trust (PT), personal innovativeness (PI), and perceived enjoyment (PEN). Survey data from 635 Chinese university students were analyzed using an integrated methodological approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM), Importance-Performance Map Analysis (IPMA), and Artificial Neural Network (ANN) methodologies. Results consistently identified PEN as the most influential factor, followed by PT, while PI and PE were significant but exhibited weaker predictive power. EE and SI showed no significant influence. IPMA further revealed that students' trust in AI security remains relatively low, indicating substantial room for improvement. The methodological triangulation demonstrated the complementary strengths of different analytical techniques, with PLS-SEM providing structural relationships, IPMA revealing performance gaps, and ANN capturing non-linear relationships while validating importance rankings. The study contributes to educational technology literature by demonstrating how PLS-SEM, IPMA, and ANN can be effectively integrated to provide comprehensive insights into behavioral intention models, extending the UTAUT framework with underexamined variables in the context of AI chatbot adoption. Future research could explore the integration of additional analytical techniques within this methodological framework and investigate contextual factors that shape technology adoption patterns.
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