In-Class AI Use and Attitudes Among University Students: The Different Mediating Roles of Cognitive Relief and Cognitive Offloading

AI use is associated with both cognitive relief and cognitive offloading, leading to uncertainty in how users make value judgments and decisions. This study focuses on in-class AI use and explores the perceptions of cognitive relief and cognitive offloading among university students, as well as the distinct mediating mechanisms through which these factors shape the attitudes of students. Based on questionnaire data from 287 respondents, structural equation modeling and bootstrap methods were employed to test the research hypotheses. The results show that cognitive relief exerts a complementary mediating effect between AI use and attitudes, whereas cognitive offloading functions as a competitive mediator. The two mechanisms produce opposing effects on students, with cognitive relief demonstrating a stronger overall mediating effect. These findings suggest that educators should guide students toward a more nuanced understanding of AI use to mitigate confusion and its potential negative psychological consequences. Moreover, educators and institutions should leverage AI to provide cognitive relief for higher-order learning activities, thereby enhancing the engagement, motivation, and deeper learning processes of students, while simultaneously implementing reflective and critical thinking practices to guard against the risks of cognitive offloading. This study is limited by its single-institution convenience sample and reliance on self-reported data; future research incorporating qualitative methods such as interviews and classroom observations is encouraged to further validate and extend these findings.