Applying and validating the METUX model in Chinese higher education: a psychometric assessment of AI-based need satisfaction scales.

As artificial intelligence (AI) technologies become increasingly embedded in higher education, understanding students' psychological experiences with these tools is essential. Drawing on the METUX model (Motivation, Engagement, and Thriving in User Experience), this study validated five Technology-based Experience of Need Satisfaction (TENS) scales (Adoption, Interface, Task, Behavior, and Life) among Chinese university students. In Phase I, 320 students completed the translated TENS scales. Three scales demonstrated acceptable reliability and model fit, while the TENS-Interface and TENS-Task scales showed poor performance, particularly in dimensions that combined positively and negatively worded items. In Phase II, revised versions of these two scales were administered to a new sample (N = 189), with mixed-directional items reworded into a consistent positive format. CFA results indicated substantial improvements in model fit, internal consistency, and convergent validity. The findings underscore the importance of item wording consistency and the value of iterative validation when adapting instruments across cultural contexts. Importantly, this study extends the applicability of the METUX framework to Chinese higher education, offering empirical evidence that its core constructs are transferable when appropriate linguistic and contextual modifications are made. The refined TENS scales provide a robust foundation for assessing students' basic psychological need satisfaction in AI-supported learning environments and offer methodological guidance for future scale adaptation in non-English-speaking contexts.