In a typical didactic learning environment, tailoring the materials for the needs of each individual is onerous. The instructor is outnumbered and the needs are varied. However, the precepts of didactic pedagogy minimize the agency of the student and the need for adapting the material since it is long-term memory based^[1]. When we move to a reflexive environment, where active knowledge representations are key, adapting the learning to each student is crucial^[1]. If the materials are not adapted to the learner, their ability to engage in active knowledge making can be hampered since the material may not be suited for the task. If the material is above the student's understanding, it may be difficult for the student to find meaning in it. If the material is too simple, the student won't be engaged, and may not bother making the necessary knowledge representations.

The reflexive, ubiquitous, and multi-modal nature of an e-learning space is uniquely suited for tailoring the learning experience to each student's needs. This is done through adaptive learning, which is the use of computer algorithms in order to tailor the material to the learner^[2].

The following video summarizes the way adaptive learning can be applied to medical training:

The goal of adaptive learning is to identify gaps that the student has and to fill those gaps through active learning. In our example above, the student is presented with a real-world problem, and the program starts to ask questions of the student in order to identify the gaps. It then encourages the student to pursue active knowledge making in order to confidently resolve the real-world problem. Though each student is working on the same problem, their paths may be differerent since they may have different gaps in their knowledge. In reference to the title of this update, "can a computer teach?", the algorithm is simply providing a different scaffold to each student, while the student is the one teaching themselves.