Pragmatic GAI continuance through human-AI collaboration amid trendiness and creepiness

As generative artificial intelligence (GAI) becomes embedded in university learning, understanding why students continue using it is increasingly important. This study integrates the Expectation-Confirmation Model (ECM) and Task-Technology Fit (TTF) with Collaborative Effectiveness, Trendiness, and Creepiness to explain students’ GAI continuance. Survey data from 271 university students were analyzed using PLS-SEM. The model explained 63.6% of the variance in continuance intention. Notably, TTF did not directly increase Perceived Usefulness; instead, its effect emerged through Confirmation, indicating that task fit becomes valuable when students’ actual experiences confirm their expectations. Collaborative Effectiveness, by contrast, directly enhanced Perceived Usefulness while also operating through Confirmation. These post-use evaluations subsequently strengthened Satisfaction and Continuance Intention, revealing distinct but complementary pathways from functional fit and human–AI collaboration to continued use. Trendiness also promoted continuance, whereas Creepiness showed no significant direct effect. The findings extend post-adoption research by demonstrating how TTF is translated into sustained GAI use through experiential confirmation and subsequent post-use evaluations. Practically, universities should move beyond simply providing AI access and design task-aligned learning activities that enable effective human–AI collaboration, verify AI outputs, and turn initial adoption into sustainable academic use.