Unveiling Student Motivations for Using Generative AI in Higher Education: A Qualitative Exploration of Intrinsic, Extrinsic, and Amotivation Factors

Abstract

Higher education students are increasingly using Generative Artificial Intelligence (GenAI) in academic tasks. Although many studies provide insights into students’ attitudes and perceptions, the psychological constructs underpinning motivation have not been thoroughly investigated. This is important as motivational constructs can provide an indication of whether students are likely to have an enhanced or diminished learning experience with GenAI. This paper investigates how different types of motivation (intrinsic, extrinsic, amotivation) drive student use of GenAI. Adopting an exploratory qualitative research design underpinned by Self-Determination Theory (SDT), this study collected data from 13 second-year master’s students in non-technical fields from a university in Belgium through a structured interview. A hybrid thematic analysis was conducted. Findings suggest that while students’ motivations could be classified in different categories, they were often intertwined with contextual and institutional dynamics. Motivation is an individual trait, but structural and pedagogical conditions have a significant role in shaping motivation. The study underscores the importance of an environment that can encourage mindful and meaningful use of GenAI.

Date of Award	2025
Original language	English
Supervisor	Chang Zhu (Promotor)