Generative AI chatbots in higher education: addiction, cognitive offloading, and the role of AI literacy

Generative AI chatbots (GAICs) are becoming integral to higher education, offering students instant explanations, personalized support, and efficient academic assistance. While prior studies have focused on adoption and continuance, limited attention has been given to the potential risks of overreliance. This study applies Uses and Gratifications (U&G) theory to examine the antecedents and consequences of GAIC addiction. An exploratory sequential mixed-method design was employed. In the qualitative phase, open-ended responses were collected from 80 university students using purposive sampling to identify GAIC-specific gratifications. These insights were used to develop measurement items, which were subsequently validated in the quantitative phase through a survey of 402 university students in Pakistan. An online questionnaire was used for data collection, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was used for data analysis. The findings reveal that six gratifications, perceived usefulness, perceived ease of use, personalization, trust, emotional attachment, and anthropomorphism significantly predict GAIC addiction, which in turn leads to increased cognitive offloading. Furthermore, AI literacy negatively moderates this relationship, indicating its role as a protective factor. This study extends U&G theory by linking user motivations to maladaptive behavioral outcomes and provides practical insights for promoting responsible AI use in higher education.