An increasing number of data across many higher education programmes indicate that the traditional construction of knowledge assessments allows students to pass all exams even if they lack knowledge in certain areas of the curriculum. This may be particularly problematic for healthcare programmes such as medicine, where students can graduate without achieving sufficient competency in certain subjects. Summative and formative knowledge assessments may indicate areas of weakness, but there is no guarantee that students will address them. Therefore, compensation of content both within and across assessments can potentially lead to graduating students with insufficient knowledge. To address this issue and remove any compensation it is now possible to use personalised knowledge assessments in the form of adaptive progress testing to improve graduate students’ knowledge and increase their safety to practice. Computerized adaptive assessments utilise algorithms to select items depending on the candidate’s previous answers. Such assessments can select questions according to their difficulty or content of the blueprint. Adaptive testing by difficulty aims to give a more reliable measure of each individual student’s performance, while adaptive testing by content aims to ensure successful performance in all required content by all students. Here we present an overview of computerised adaptive progress testing and discuss the rationale and practicality of this approach to assessment.