Generative Artificial Intelligence (GenAI) has rapidly gained prominence in higher education, facilitating various academic tasks such as assignments completion and essay writing. While existing research has examined related constructs like attitudes and perceptions, the psychological construct of motivation-comprising intrinsic, extrinsic, and amotivation- remains insufficiently explored. This study seeks to address this gap by identifying and categorizing the motivations driving students’ use of GenAI in academic contexts. Using an explanatory qualitative research design, data will be collected from master’s students at the Vrije Universiteit Brussel (VUB) through semi-structured interviews. Thematic analysis, guided by a deductive approach, will classify responses based on predetermined motivational constructs. The findings aim to provide nuanced insights into the factors influencing students’ engagement with GenAI, contributing to the development of effective and ethical AI integration strategies in higher education.

This study adopts an explanatory qualitative research design to investigate the underlying motivations driving students’ use of GenAI. A qualitative approach is well-suited for capturing the complexities of students’ motivational processes which provide an in-depth exploration of how intrinsic motivation, extrinsic motivation, and amotivation influence their engagement with GenAI. This design enables an in-depth understanding of the factors shaping students’ interactions with AI tools in academic contexts. Data will be collected through semi-structured interviews, designed to explore students’ motivations and the types of motivation influencing their use of GenAI. This format allows for a structured yet flexible approach, ensuring that key topics are addressed while giving participants the opportunity to elaborate on their experiences and perspectives. The flexibility of semi-structured interviews enables deeper insights into individual motivations while it can maintain alignment with the study’s research objectives (McIntosh & Morse, 2015). The interview guide for this study will be specifically designed to explore the underlying motivations influencing students’ use of GenAI.

This research seeks to fill a crucial gap in the literature by exploring why students choose to use GenAI in academic settings. By examining different types of motivation, it provides a deeper understanding of what drives students to engage with GenAI and how their motivations shape their learning practices. Given that GenAI has the potential to both support and challenge learning outcomes, understanding these underlying factors is essential for making sense of students’ behaviors and choices. The insights gained from this study can help educators tailor their teaching strategies and guide institutions in shaping policies that promote the responsible and effective use of AI in higher education. Ultimately, this research aims to contribute to a more thoughtful and informed approach to integrating GenAI in academic environments.

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