Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education

Artificial intelligence systems such as early warning systems are becoming more common in Higher Education. However, the students' reactions to such techno-pedagogical innovations are much less explored in settings beyond the development and testing. This paper analyses the students' acceptance of an early warning system developed at a fully online university. Following a pre-usage and post-usage experimental design based on the Unified Theory of Acceptance and Use of Technology model and the Structural Equation Modelling, we observed how, within four courses (839 participants in the academic year 2019-20, of which 347 participants answered both a pre-and post-usage questionnaire), the students' acceptance changed overtime. Our findings revealed a disconfirmation effect in the acceptance of the early warning system, namely, a difference between expectations surrounding the technology pre-and post-usage, and shed light on the ways artificial intelligence systems should be integrated within Higher Education virtual classrooms.