Designing Conversational Agents to Support Learning from Scientific Graphs

Abstract

The need for accountability in research and an informed policy-making and society has increased demand for publicly accessible research data. However, the tendency in the scientific context to rely on graphs to illustrate findings can be challenging for users lacking domain expertise. Rooted in Conversation theory, this study explores using conversational agents to help users interpret such graphs. Using design science research, we develop and test a prototype conversational agent, addressing previously identified challenges like accessibility as well as language and education barriers in graph interpretation. While users with conversational agent access in a large-scale experiment do not demonstrate improvements in objective learning success, they report higher perceived learning, perceived empowerment and user experience, with reduced cognitive load. This research highlights the potential of large language model-based agents to improve access to complex data, offering insights into science communication as well as educational agent design and their limitations.