Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…

Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…

Little knowledge is available on students' attitudes and behavioural intentions towards using ChatGPT, a breakthrough innovation in recent times. This study bridges this gap by adding two relevant less-explored constructs (i.e., perceived enjoyment and perceived informativeness) to the technology acceptance model and illustrating the moderating…

This paper presents a critical review of computer assisted instruction (CAI); an overview of recent intelligent tutoring systems (ITSs), including current perceived shortcomings; major activities of the field, i.e., analysis of teaching/learning processes, and extending and developing artificial intelligence techniques for use in intelligent…