Toward a Framework for Collaborative Learning Based on Agent-based Technologies

Experimental studies concerning distance learning show that forms of interaction like peer-to-peer collaboration and many-to-many communication lead to more desirable results than uni-lateral knowledge trasmission from teacher to students. However, existing collaboration tools (like forum, chat, Wiki, etc.) present some limits that makes them mostly ineffective from the standpoint of collaborative learning and lead teachers and students to exploit more traditional collaboration tools like face-to-face and telephonic interaction.
In this context, Multi-Agent System (MAS) seems to be a suitable paradigm to engineer distance learning systems that promote effective collaboration and overcome the aforementioned limits. In particular, we discuss how technologies like intelligent agents and advanced coordination infrastructures like TuCSoN, and meta-models like the Agent and Artefact (A&A) could be exploited to build a framework for collaborative distance learning.