TY  - JOUR
AU  - Lim, Wendell Adrian
AU  - Custodio, Razel
AU  - Sunga, Monica
AU  - Amoranto, Abegail Jayne
AU  - Sarmiento, Raymond Francis
PY  - 2024
DA  - 2024/1/5
TI  - General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review
JO  - J Med Internet Res
SP  - e43112
VL  - 26
KW  - COVID-19
KW  - health chatbot
KW  - conversational agent in health care
KW  - artificial intelligence
KW  - systematic review
KW  - mobile phone
AB  - Background: A conversational agent powered by artificial intelligence, commonly known as a chatbot, is one of the most recent innovations used to provide information and services during the COVID-19 pandemic. However, the multitude of conversational agents explicitly designed during the COVID-19 pandemic calls for characterization and analysis using rigorous technological frameworks and extensive systematic reviews. Objective: This study aims to describe the general characteristics of COVID-19 chatbots and examine their system designs using a modified adapted design taxonomy framework. Methods: We conducted a systematic review of the general characteristics and design taxonomy of COVID-19 chatbots, with 56 studies included in the final analysis. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select papers published between March 2020 and April 2022 from various databases and search engines. Results: Results showed that most studies on COVID-19 chatbot design and development worldwide are implemented in Asia and Europe. Most chatbots are also accessible on websites, internet messaging apps, and Android devices. The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context for system design trends. From the temporal profile perspective, almost half of the COVID-19 chatbots interact with users for several weeks for >1 time and can remember information from previous user interactions. From the appearance perspective, most COVID-19 chatbots assume the expert role, are task oriented, and have no visual or avatar representation. From the intelligence perspective, almost half of the COVID-19 chatbots are artificially intelligent and can respond to textual inputs and a set of rules. In addition, more than half of these chatbots operate on a structured flow and do not portray any socioemotional behavior. Most chatbots can also process external data and broadcast resources. Regarding their interaction with users, most COVID-19 chatbots are adaptive, can communicate through text, can react to user input, are not gamified, and do not require additional human support. From the context perspective, all COVID-19 chatbots are goal oriented, although most fall under the health care application domain and are designed to provide information to the user. Conclusions: The conceptualization, development, implementation, and use of COVID-19 chatbots emerged to mitigate the effects of a global pandemic in societies worldwide. This study summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives, which may help developers conveniently choose a future-proof chatbot archetype that will meet the needs of the public in the face of growing demand for a better pandemic response. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e43112
UR  - https://doi.org/10.2196/43112
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38064638
DO  - 10.2196/43112
ID  - info:doi/10.2196/43112
ER  -