The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for previous users or learners of such system and to use such analysis to guide and help the current user. New techniques are now available that rely on emergent or self-organising methods to analyse a large amount of interaction data being generated by large-scale online learning communities, interactions with learning resources or learning management mechanisms. One such technique is swarm intelligence, a set of computing algorithms in the form of multi-agent systems that simulate how swarms of insects or birds move or work. It has been applied to various intelligent functionalities such as adaptive content planning, computer-adaptive testing and assessment paper generation. In this article, we provide a survey of the various approaches in swarm intelligence that explores potential mechanisms to adapt and individualise learning and assessment. We hope that this article can inspire future studies in this exciting area. (Contains 3 tables.)