Designing AI-based Conversational Agent for Diabetes Care in a Multilingual Context
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 38 references that are in the map. First posted on arXiv 20 May 2021.
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5 of 38 references.
References · 5
- 2016Conditional Image Generation with PixelCNN Decoderscited by 862
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
- 2017Parallel WaveNet: Fast High-Fidelity Speech Synthesiscited by 215
- 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
- 2019Forward-Backward Decoding for Regularizing End-to-End TTScited by 3
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