Agent technologies are a promising approach to solving a number of prob-lems concerned with personalised learning due to the inherent autonomy and independence they provide for learners. The objective of this thesis is to find out whether a multiagent system could potentially replace a centralised infra-structure, and to explore the impact of agents taking different strategies. More specifically, our aim is to show how intelligent agent systems can not only form a good framework for distributed e-learning systems, but also how they can be applied in contexts where learners are autonomous and independent. The study also aims to investigate fairness issues and propose a simple framework of fair-ness definitions derived from the relevant literature. To this end, a university course selection scenario has been chosen, where the university has many courses available, but has only sufficient resources to run the most preferred ones. Instead of a centralised system, we consider a de-centralised approach where individuals can make a collective decision about which courses should run by using a multi-agent system based on voting. This voting process consists of multiple rounds, allowing a student agent to accurate-ly represent the student’s preferences, and learn from previous rounds. The ef-fectiveness of this research is demonstrated in three experiments. The first ex-periment explores whether voting procedures and multiagent technology could potentially replace a centralised infrastructure. It also explores the impact of agents using different strategies on overall student satisfaction. The second ex-periment demonstrates the potential for using multiagent systems and voting in settings where students have more complex preferences. The last experiment investigates how intelligent agent-based e-learning systems can ensure fairness between individuals using different strategies. This work shows that agent technology could provide levels of decentrali-sation and personalisation that could be extended to various types of personal and informal learning. It also highlights the importance of the issue of fairness in intelligent and personalised e-learning systems. In this context, it may be said that there is only one potential view of fairness that is practical for these systems, which is the social welfare view that looks to the overall outcome.
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