Adoption and usage of generative AI by higher education students and its impact on learning

Abstract

Generative AI tools are becoming popular in higher education. They can help with learning, writing, and problem-solving, but they also raise concerns about dependence, cheating, and students’ skills. Although research on generative AI in education is growing, many studies focus on isolated aspects – such as adoption factors, usage patterns, or individual learning outcomes – without offering a comprehensive perspective on how these tools shape student learning. This thesis looks at how generative AI is adopted and used in higher education and how it affects learning. Using adoption models and other theories, it combines research findings to focus on three main areas: (1) defining generative AI within the academic context, (2) analyzing adoption drivers and usage patterns, and (3) evaluating effects on learning outcomes such as academic performance, motivation, writing, problem-solving, comprehension, and critical thinking. The analysis highlights the complex interplay between motivations, behaviors, and outcomes, demonstrating that adoption factors strongly shape how students use AI and the effects that result from this use. This thesis gives a clear overview of generative AI in higher education and shows what it means for teaching, learning, and policy decisions.

Date of Award	2025
Original language	English
Supervisor	Gerald Petz (Supervisor)