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

Providing technological support for collaborative and discussion-based learning has long been a focus in CSCL research (Gweon et al., 2006; Kollar et al., 2006; Kumar et al., 2007; Rosé and Ferschke, 2016, Naik et al., 2024). Open - source architectures like Bazaar (Adamson et al., 2014) have enabled implementation of a plethora of dynamic support interventions, even for face - to -face collaboration through multi - modal sensing (Wang et al., 2020), which can be used in a portable fashion for nearly anytime-anywhere collaboration support (Vitiello et al., 2023). Past studies highlight the benefits of interactive and context-sensitive support in group learning (Kumar et al., 2007; Kumar and Rose, 2010). While static scaffolding like fixed prompts (Vogel et al., 2021) and scripted roles (Fischer et al., 2013) have been effective, contextualized interventions within specific conversational contexts (Ai et al., 2010; Cui et al., 2009) or support for student role taking (Gweon; et al., 2007) have also shown positive outcomes. Past studies incorporating dynamic support agents in collaborative learning activities (Kumar et al., 2007; Kumar and Rosé, 2010; Rosé and Ferschke, 2016) have shown the effectiveness of discussion-based learning integrated with conversational support using dialog agents. Finally Sankaranarayanan and colleagues (Sankaranarayanan et al., 2022a; Sankaranarayanan et al., 2022b) have shown the effectiveness of reflection-based learning for collaborative software development, showing that shifting students' focus more towards reflection than actual coding can increase conceptual learning without harming the ability to write code. The contribution of this design paper is the introduction of capabilities from Large Language Models (LLMs) (Vaswani, 2017) to enable new forms of collaborative support agents. While recent studies demonstrate that this new generation of support agents can be effective learning support, the new contribution of this paper is an extension to a publicly available and open-source plat form to easily integrate LLM agents developed in the broader CSCL community in order to facilitate needed research to answer questions about how best to use new AI capabilities to support collaborative learning effectively. We provide code for the LLMbazaar extension, the illustrative instructional example described below, and instructions for obtaining support for using this resource, available on GitHub (Bazaar, 2025).