The Effect of Healthcare Chatbots’ Information Presentation Styles on User Acceptance in a Knowledge Seeking Task

The rise of large language models offers new opportunities for disseminating more effective and equitable healthcare information. While past research has extensively investigated users’ perceptions of chatbots, few have examined the design strategies for chatbot information presentation. The objective of this research aims to explore how communication style (conversational or informative) and language style (technical or non-technical) affect user acceptance and ability performing a knowledge seeking task with a healthcare chatbot in a 2 × 2 between-subjects design. Following this, participants engaged in semi-structured interviews, where we analyzed participants’ experiences through inductive thematic analysis. Our findings indicate users generally found conversational chatbots to be more understandable with greater perceived interaction freedom than informative chatbots. These insights highlight the importance of aligning chatbot communication with users’ expectations. Future research should validate these findings with larger and more diverse populations, considering the impact of various chatbot interaction styles on user experience and long-term effectiveness.