Exploring the Student Perspective: Assessing Technology Readiness and Acceptance for Adopting Large Language Models in Higher Education





Digital technologies are changing and will continue to change how we learn and teach today and in the future. With the latest developments in the field of generative artificial intelligence (AI), particularly large language models (LLMs), the question of using AI-based tools in academic education is ruling the current discussions about the transformative impact of AI in higher education (HE).

These discussions range from banning these technologies for learning and teaching in HE to guided study support. This study avoids taking up these multifarious and partly controversial debates. Instead, we show how students perceive using AI-based tools for automated text generation for their studies. Drawing on a synthesis of two theories: the 'Technology Readiness Index' (TRI) and 'Technology Acceptance Model' (TAM). The model is validated based on survey data collected among undergraduate first-semester students (N=111) of a computer science-related study programme in Germany in winter 2022/23. The students had to evaluate their relationship to that new technology focusing on their readiness for technology adoption and acceptance. By analysing the collected data with a partial least squares model, we find that the optimism toward the new technology positively influences technology acceptance, while discomfort with the technology negatively influences perceived ease of use. The paper concludes with recommendations for action for adopting LLMs in HE. A proper investment in building AI skills in academic teaching plays a valuable role in fostering the students' positive attitude and innovativeness towards this new technology. Additionally, there is a need for more education about the risks and challenges of using this technology to reduce the impact of factors such as discomfort on ease of use. This requires a factual discourse, away from the current hype-induced exaggerated and hyperbolic statements, for instance, in developing formal guidance for universities.