Exploring student experiences with generative AI in master’s thesis writing: an experiential learning approach

The rapid adoption of Generative Artificial Intelligence (GenAI) in higher education has raised critical questions about its effective use within pedagogical practices. Multiple authors highlight both benefits such as interactivity, personalized learning, and assessment support and challenges, including risks of plagiarism, algorithmic bias, and data privacy concerns. Despite the growing attention to GenAI in higher education, empirical research on its effective integration into advanced academic programs remains scarce. This paper bridges this gap by applying Kolb's experiential learning framework to examine students' experiences using GenAI in a Master's thesis-writing course within a Management Information Systems (MIS) curriculum. The findings reveal that students predominantly used GenAI as a productivity and cognitive support tool, particularly for idea structuring, proofreading, and transcription. While its use improved efficiency and clarity, issues such as inconsistent reliability emphasized the necessity for critical engagement and guided pedagogical support. These results highlight the importance of establishing structured frameworks that promote responsible, effective, and ethically informed use of GenAI to enhance learning outcomes.