Recent developments in natural language understanding have sparked a great amount of interest in the large language models such as ChatGPT that contain billions of parameters and are trained for thousands of hours on all the textual data of the internet. ChatGPT has received immense attention because it has widespread applications, which it is able to do out-of-the-box, with no prior training or fine-tuning. These models show emergent skill and can perform virtually any textual task and provide glimmers, or “sparks”, of artificial general intelligence, in the form of a general problem solver as envisioned by Newell and Simon in the early days of artificial intelligence research. Researchers are now exploring the opportunities of ChatGPT in education. Yet, the factors influencing and driving users’ acceptance of ChatGPT remains largely unexplored. This study investigates users’ (n=138) acceptance of ChatGPT. We test a structural model developed using Unified Theory of Acceptance and Use of Technology model. The study reveals that performance expectancy is related to behavioral intention, which in turn is related to ChatGPT use. Findings are discussed within the context of mass adoption and the challenges and opportunities for teaching and learning. The findings provide empirical grounding to support understanding of technology acceptance decisions through the lens of students’ use of ChatGPT and further document the influence of situational factors on technology acceptance more broadly. This research contributes to body of knowledge and facilitates future research on digital innovation acceptance and use.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.