Designing AI-based Conversational Agent for Diabetes Care in a Multilingual Context
This work addresses the problem of designing effective conversational agents for diabetes care, particularly in multilingual contexts, but it is incremental as it builds on existing technologies and focuses on specific design guidelines.
The paper tackled the lack of prescriptive knowledge for designing conversational agents in diabetes care by proposing three design principles and developing AMANDA, an AI-based multilingual agent, which achieved high speech quality and usability scores.
Conversational agents (CAs) represent an emerging research field in health information systems, where there are great potentials in empowering patients with timely information and natural language interfaces. Nevertheless, there have been limited attempts in establishing prescriptive knowledge on designing CAs in the healthcare domain in general, and diabetes care specifically. In this paper, we conducted a Design Science Research project and proposed three design principles for designing health-related CAs that embark on artificial intelligence (AI) to address the limitations of existing solutions. Further, we instantiated the proposed design and developed AMANDA - an AI-based multilingual CA in diabetes care with state-of-the-art technologies for natural-sounding localised accent. We employed mean opinion scores and system usability scale to evaluate AMANDA's speech quality and usability, respectively. This paper provides practitioners with a blueprint for designing CAs in diabetes care with concrete design guidelines that can be extended into other healthcare domains.