The use of one-size-fits-all approach is getting replaced by the adaptive, personalized perspective in recently developed learning environments. This study takes a look at the need of personalization in e-learning systems and the adaptivity and distribution features of adaptive distributed learning environments. By focusing on how personalization can be achieved in e-learning systems, the technologies used for establishing adaptive learning environments are explained and evaluated briefly. Some of these technologies are web services, multi-agent systems, semantic web and AI techniques such as case-based reasoning, neural networks and Bayesian networks used in intelligent tutoring systems. Finally, by discussing some of the adaptive distributed learning systems, an overall state of the art of the field is given with some future trends.