Artificial Intelligence (AI) is a discipline of computer science that deals with machine intelligence. It is essential to bring AI into the context of learning because it helps in analysing the enormous amounts of data that is collected from individual students, teachers and academic staff. The major priorities of implementing AI in education are making innovative use of existing digital technologies for learning, and teaching practices that significantly improve traditional educational methods. The main problem with traditional learning is that it cannot be suited to every student in class. Some students may grasp the concepts well, while some may have difficulties in understanding them and some may be more auditory or visual learners. The World Bank report on education has indicated that the learning gap created by this problem causes many students to drop out (World Development Report, 2018). Personalised learning has been able to solve this grave problem.

In a digital classroom, analysis of students' interactions with the learning media provides important information about users' behavior, which can lead to a better understanding and thus optimizes teaching and learning. However, over the period of a course, students tend to forget the lessons learned in class. Learning predictions can be used to recommend learning objects users need most, as well as to give an overview of current knowledge and the learning level. The representation of time based data in such a format is difficult since the knowledge level of a user with a learning object changes continuously depending on various factors. This paper presents work in progress for a doctoral approach to extend the traditional user-item-matrix of a recommendation engine by a third dimension - the time value. Moreover, in this approach the learning need consists of different context factors each influencing the relevance score of a learning object.