Role design considerations of conversational agents to facilitate discussion and systems thinking
Ha Nguyen
Abstract
Conversational agents can facilitate learning discussions by applying natural language understanding to process students' discourse. Agents can assume the roles of figures such as peers or mentors, to promote actions similar to human interactions. In this study, we explore how and for whom different role designs of a text-based agent (i.e., chatbot) can facilitate discussion patterns and systems thinking in small-group discussions. Participants included 172 students in 9th grade (ages 13–14). Participants were randomly assigned to groups of five students and interacted with no agent, an expert agent, or a less knowledgeable peer agent. Results suggest that both agents facilitated learning of systems mechanisms by enhancing transactive exchange, where students built on prior ideas in their discussion groups. We also found differences in the agents' effects on discussion and learning outcomes based on groups' variation in systems thinking pre-test. Findings highlight the importance of role design considerations of agents in group settings.