LLM Bazaar: A Service Design for Supporting Collaborative Learning with an LLM-Powered Multi-Party Collaboration Infrastructure

TL;DR

This paper introduces LLM Bazaar, an architecture integrating large language models into collaborative learning agents to enhance group interactions and critical thinking in educational settings.

Contribution

It presents a novel service design and infrastructure that incorporates LLMs into collaborative learning environments, enabling real-time, context-sensitive support for group work.

Findings

Enables real-time, context-aware support in group learning.

Facilitates exploration of LLMs' impact on collaboration.

Provides an open source architecture for future research.

Abstract

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.