The Use of Multiple Conversational Agent Interlocutors in Learning

TL;DR

This paper explores the potential of large language models to simulate multiple conversational agents with diverse personas to enhance educational experiences and collaborative learning environments.

Contribution

It discusses prior research and conceptualizes the use of multi-persona LLMs as educational tools for augmenting user learning and interaction.

Findings

Identifies scenarios where multiple LLM-based personas can support education.

Highlights the potential benefits of multi-agent LLM interactions in learning environments.

Proposes future directions for integrating multi-persona LLMs in educational settings.

Abstract

With growing capabilities of large language models (LLMs) comes growing affordances for human-like and context-aware conversational partners. On from this, some recent work has investigated the use of LLMs to simulate multiple conversational partners, such as to assist users with problem solving or to simulate an environment populated entirely with LLMs. Beyond this, we are interested in discussing and exploring the use of LLMs to simulate multiple personas to assist and augment users in educational settings that could benefit from multiple interlocutors. We discuss prior work that uses LLMs to simulate multiple personas sharing the same environment, and discuss example scenarios where multiple conversational agent partners could be used in education.