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.

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 time of COVID calls for characterization and analysis using rigorous technological frameworks and extensive systematic review. OBJECTIVE This study aimed 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 on the general characteristics and design taxonomy of COVID-19 chatbots with 59 studies included in the final analysis. This review used PRISMA in selecting articles published from January 1, 2019 to April 30, 2022 in databases and search engines. RESULTS Results show that 41/59 (70%) of studies on COVID-19 chatbot design and development are implemented in Asia (41%) and Europe (29%), and can be accessed on websites (32%), messaging apps (29%), and Android devices (24%). The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context. In terms of the temporal profile perspective, almost half of COVID-19 chatbots interact with users for several weeks (49%) for more than a single time (58%) and can remember information from previous user interactions (44%). Regarding appearance, the majority of the chatbots assume the expert role (61%), are task-oriented (63%), and have no visual representation (56%). As for intelligence, almost half of the chatbots are artificially intelligent (48%) and respond to textual input and a set of rules (42%). More than half of these chatbots operate on a structured flow (53%) and do not portray any socio-emotional behavior (56%). In addition, nearly half can process both external data and broadcast resources (37%). Under the interaction perspective, the majority of the chatbots communicate through text (68%), react to user input (53%), and are adaptive (63%). Relative to this, more than half of the chatbots do not require additional human support (75%) and are not gamified (80%). When it comes to context, all of the COVID-19 chatbots are goal-oriented (100%), while the majority fall under the healthcare application domain (79%) and are designed to provide information to the user (61%). CONCLUSIONS The conceptualization, development, implementation, and utilization of these conversational agents emerged to mitigate the effects of the pandemic in societies worldwide. We strongly believe that this study is a starting point to help the developers conveniently choose a future-proof chatbot archetype that would meet the needs of the public amid the growing demand for a more structured pandemic response.