Determinants of Students’ Perceived Usefulness of Large Language Models: The Role of Relevance, Enjoyment, and Ease of Use

Abstract

Perceived usefulness (PU) is one of the most important determinants for the acceptance of technologies as it strongly influences both the intention to use and the actual use of the technology. As large language models (LLMs), such as ChatGPT, are increasingly used in higher education, it is important to understand what factors influence students’ perceptions of the usefulness of LLMs for academic learning. Based on the Technology Acceptance Model (TAM), this study investigated the role of relevance to academic learning, perceived enjoyment, and perceived ease of use (PEOU) on students’ perceptions of the usefulness of LLMs. The study involved 102 students from a Croatian university. The data were analyzed using Spearman correlation and multivariate regression analysis. The correlation analysis showed that all three factors had a statistically significant positive correlation with the perceived usefulness of LLMs. However, the regression analysis showed that only relevance to academic learning and perceived enjoyment of using LLMs for learning were significant positive predictors, while perceived ease of use played a minor role. Together, these two variables explained 71.8% of the variance in students’ perceptions of the usefulness of LLMs. The results emphasize the importance of identifying the factors that shape students’ perceptions of the usefulness of LLMs as they are an important predictor of intention to use the technology. The findings suggest that there is a need to develop LLM-based tools that are pedagogically relevant and engaging for students and that can serve as guidelines for their successful integration into higher education.

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