LLM Bazaar: A Service Design for Supporting Collaborative Learning with an LLM-Powered Multi-Party Collaboration Infrastructure
This work addresses the problem of improving collaborative learning outcomes and interaction patterns for students, but it is incremental as it builds on an existing architecture.
The researchers tackled the challenge of enhancing collaborative learning by integrating a large language model (LLM) into an existing open-source collaboration infrastructure called Bazaar, resulting in a new service design that enables real-time, context-sensitive support for group learning.
For nearly two decades, conversational agents have played a critical role in structuring interactions in collaborative learning, shaping group dynamics, and supporting student engagement. The recent integration of large language models (LLMs) into these agents offers new possibilities for fostering critical thinking and collaborative problem solving. In this work, we begin with an open source collaboration support architecture called Bazaar and integrate an LLM-agent shell that enables introduction of LLM-empowered, real time, context sensitive collaborative support for group learning. This design and infrastructure paves the way for exploring how tailored LLM-empowered environments can reshape collaborative learning outcomes and interaction patterns.