Abstract
Teaching and learning have gone online in response to the pandemic, which reveals the need for accurately tailored educational assessments to ascertain the extent to which learning outcomes or objectives are achieved. Computer Adaptive Testing (CAT) is a technology-driven form of assessment that tailors items to a candidate's ability level with empirically proven benefits over the fixed-form computer based test. A systematic review was employed which shows that item bank is a key requirement for CAT and the items must through a rigorous item development process to ensure and maintain quality  in terms of content, criterion constructs and internal consistency, determining the psychometric validation of behavioural measures while leveraging on variances of Item Response Theory (IRT). Following the item development stage is the need to compile validated items into administrable forms using advanced computer software for automatic test assembly and administration, such as FastTest which allows specifying empirically tried algorithms for CAT from start to termination of the test. This helps to ensure that assessment properly leverages the advantages that CAT holds. Furthermore, the review revealed that CAT has been widely applied with large-scale testing in various fields by educational, health and psychological professionals utilising different IRT models; however only in developed countries. This brings to bear the need for adoption in other parts of the world, for improvements in educational assessments. The interjections of 4IR with AI considering emerging technology aids the CAT algorithm for achieving expert and knowledge-based systems, being a requirement for survival in today's world.