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Nowadays, the social dimension of product sustainability is increasingly in demand, however, industrial designers struggle to pursue it much more than the environmental or economic one due to their unfamiliarity in correlating design choices with social impacts. In addition, this gap is not filled even by the supporting methods that have been conceived to only support specific areas of application. To fill this gap, this study proposed a method to support social failure mode and effect analysis (SFMEA), though the automatic failure determination, based on the use of a chatbot (i.e., an artificial intelligence (AI)-based chat). The method consists of 84 specific questions to ask the chatbot, resulting from the combination of known failures and social failures, elements from design theories, and syntactic structures. The starting hypothesis to be verified is that a GPT Chat (i.e., a common AI-based chat), properly queried, can provide all the main elements for the automatic compilation of a SFMEA (i.e., to determine the social failures). To do this, the proposed questions were tested in three case studies to extract all the failures and elements that express predefined SFMEA scenarios: a coffee cup provoking gender discrimination, a COVID mask denying a human right, and a thermometer undermining the cultural heritage of a community. The obtained results confirmed the starting hypothesis by showing the strengths and weaknesses of the obtained answers in relation to the following factors: the number and type of inputs (i.e., the failures) provided in the questions;the lexicon used in the question, favoring the use of technical terms derived from design theories and social sustainability taxonomies;the type of the problem. Through this test, the proposed method proved its ability to support the social sustainable design of different products and in different ways. However, a dutiful recommendation instead concerns the tool (i.e., the chatbot) due to its filters that limit some answers in which the designer tries to voluntarily hypothesize failures to explore their social consequences.
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The global pandemic caused by Covid-19 has led to the fact that more than 1.8 billion children and young people around the world found themselves outside the classroom education process. This prompted the expansion of digitization processes, the search for effective solutions to support remote educational interaction, which is reflected in articles of the American, Malaysian, Spanish, Iranian, Ukrainian, etc. scientists The analysis of foreign experience proved the interest of the world educational community in the introduction of mobile services, messengers, in particular the Telegram messenger, as a tool for supporting the educational process. The article examines the problem of using the Telegram messenger to support the educational process in higher education institution in the conditions of quarantine restrictions caused by the global Covid-19 pandemic. Modern messengers, in particular Telegram, Viber, Facebook Messenger, WhatsApp, are analyzed due to criteria (commerciality, functionality, architecture, security) and indicators. The advantages of using the Telegram messenger to support the educational process are outlined, in particular: cross-platform, support for synchronous and asynchronous interaction, the ability to exchange messages in different formats, support for various types of interaction, the ability to ensure the fulfillment of many pedagogical tasks. The essence, advantages and possibilities of using the chatbot tool from the Telegram messenger are described. The psychological and pedagogical recommendations for increasing the pedagogical effect of using the Telegram chatbot are provided. The educational interaction results after using the Telegram messenger were analyzed (survey of 112 students, learning results evaluation in the experimental (112 persons) and control (110 persons) student groups). The empirical research shows that the Telegram messenger allows supporting the educational process in the conditions of quarantine restrictions, with saving quality indicators, as well as achieving pedagogical goals.
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Artificial-Intelligence (AI) is becoming more widespread in several areas, from economics and government to consumer-services and even healthcare. In fact, in the latter, there was a big use increase in the past three years, also due to the COVID-19 pandemic. Several solutions have been implemented to tackle the several challenges imposed by this new disease, being one of such solutions chatbots. In this article, we present the results of a Systematic Literature Review (SLR) that identifies the Chatbots applications in COVID-19 disease. In this SLR, we identified 9987 papers from which we selected 30 studies, on which we performed a full-text analysis. From our research, we could conclude that several solutions were implemented, with good acceptance by citizens, despite several limitations, such as limited time to develop the solutions (which narrowed some features, such as AI voice conversation), lack of global implementation and infrastructure limitations. © 2022 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.
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Companion bots such as chatbots in cyberspace or robots in real space gained popularity during the COVID-19 pandemic as a means of comforting humans and reducing their loneliness. These bots can also help enhance the lives of elderly people. In this paper, we present how to design and implement a quick prototype of companion bots for elderly people. A companion bot named "Hello Steve"that is able to send emails, open YouTube to provide entertainment, and remember the times an elderly person must take medicine and remind them is designed and implemented as a quick prototype. In addition, the bot combines the features of a mobile robot and a chatbot. The experimental results show the effectiveness of the design through its very high accuracy when navigating mobile-robot-like tasks and responding to chatbot-like tasks via voice commands. © 2023 MYU K.K.
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Erasmus students spend part of their academic stage in a foreign country, which enriches their experience significantly. During the second semester of the 2019–20 academic year and due to the COVID-19 pandemic, many students found themselves isolated and confined in a country that was not their own. Students lived with uncertainty and many doubts about what they could and could not do, while the Erasmus offices of the host universities could not cope with the many and varied issues. This paper proposes a contingency solution for an efficient communication with those students and the Erasmus offices based on a chatbot that serves as the first layer of attention to the Erasmus student facing an emergency. The chatbot has been developed within an Erasmus project with the participation of six European universities, and the first experiences gathered from its use have been very positive. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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BACKGROUND: Post-COVID-19, or long COVID, has now affected millions of individuals, resulting in fatigue, neurocognitive symptoms, and an impact on daily life. The uncertainty of knowledge around this condition, including its overall prevalence, pathophysiology, and management, along with the growing numbers of affected individuals, has created an essential need for information and disease management. This has become even more critical in a time of abundant online misinformation and potential misleading of patients and health care professionals. OBJECTIVE: The RAFAEL platform is an ecosystem created to address the information about and management of post-COVID-19, integrating online information, webinars, and chatbot technology to answer a large number of individuals in a time- and resource-limited setting. This paper describes the development and deployment of the RAFAEL platform and chatbot in addressing post-COVID-19 in children and adults. METHODS: The RAFAEL study took place in Geneva, Switzerland. The RAFAEL platform and chatbot were made available online, and all users were considered participants of this study. The development phase started in December 2020 and included developing the concept, the backend, and the frontend, as well as beta testing. The specific strategy behind the RAFAEL chatbot balanced an accessible interactive approach with medical safety, aiming to relay correct and verified information for the management of post-COVID-19. Development was followed by deployment with the establishment of partnerships and communication strategies in the French-speaking world. The use of the chatbot and the answers provided were continuously monitored by community moderators and health care professionals, creating a safe fallback for users. RESULTS: To date, the RAFAEL chatbot has had 30,488 interactions, with an 79.6% (6417/8061) matching rate and a 73.2% (n=1795) positive feedback rate out of the 2451 users who provided feedback. Overall, 5807 unique users interacted with the chatbot, with 5.1 interactions per user, on average, and 8061 stories triggered. The use of the RAFAEL chatbot and platform was additionally driven by the monthly thematic webinars as well as communication campaigns, with an average of 250 participants at each webinar. User queries included questions about post-COVID-19 symptoms (n=5612, 69.2%), of which fatigue was the most predominant query (n=1255, 22.4%) in symptoms-related stories. Additional queries included questions about consultations (n=598, 7.4%), treatment (n=527, 6.5%), and general information (n=510, 6.3%). CONCLUSIONS: The RAFAEL chatbot is, to the best of our knowledge, the first chatbot developed to address post-COVID-19 in children and adults. Its innovation lies in the use of a scalable tool to disseminate verified information in a time- and resource-limited environment. Additionally, the use of machine learning could help professionals gain knowledge about a new condition, while concomitantly addressing patients' concerns. Lessons learned from the RAFAEL chatbot will further encourage a participative approach to learning and could potentially be applied to other chronic conditions.
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COVID-19 , Adult , Child , Humans , Post-Acute COVID-19 Syndrome , Ecosystem , Health Personnel/psychology , CommunicationABSTRACT
PurposeBased on an experimental study on English for Specific Purposes (ESP) students, at the Business Department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well.Design/methodology/approachThe research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words' meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive.FindingsThe findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group.Research limitations/implicationsThe study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development.Originality/valueExamining and exploring whether the use of chatbots plays a major role in enhancing and learning ESP vocabulary in English as Foreign Language setting.
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BACKGROUND: Before the COVID-19 pandemic, adolescents with type 1 diabetes (T1D) had already experienced far greater rates of psychological distress than their peers. With the pandemic further challenging mental health and increasing the barriers to maintaining optimal diabetes self-management, it is vital that this population has access to remotely deliverable, evidence-based interventions to improve psychological and diabetes outcomes. Chatbots, defined as digital conversational agents, offer these unique advantages, as well as the ability to engage in empathetic and personalized conversations 24-7. Building on previous work developing a self-compassion program for adolescents with T1D, a self-compassion chatbot (COMPASS) was developed for adolescents with T1D to address these concerns. However, the acceptability and potential clinical usability of a chatbot to deliver self-compassion coping tools to adolescents with T1D remained unknown. OBJECTIVE: This qualitative study was designed to evaluate the acceptability and potential clinical utility of COMPASS among adolescents aged 12 to 16 years with T1D and diabetes health care professionals. METHODS: Potential adolescent participants were recruited from previous participant lists, and on the web and in-clinic study flyers, whereas health care professionals were recruited via clinic emails and from diabetes research special interest groups. Qualitative Zoom (Zoom Video Communications, Inc) interviews exploring views on COMPASS were conducted with 19 adolescents (in 4 focus groups) and 11 diabetes health care professionals (in 2 focus groups and 6 individual interviews) from March 2022 to April 2022. Transcripts were analyzed using directed content analysis to examine the features and content of greatest importance to both groups. RESULTS: Adolescents were broadly representative of the youth population living with T1D in Aotearoa (11/19, 58% female; 13/19, 68% Aotearoa New Zealand European; and 2/19, 11% Maori). Health care professionals represented a range of disciplines, including diabetes nurse specialists (3/11, 27%), health psychologists (3/11, 27%), dieticians (3/11, 27%), and endocrinologists (2/11, 18%). The findings offer insight into what adolescents with T1D and their health care professionals see as the shared advantages of COMPASS and desired future additions, such as personalization (mentioned by all 19 adolescents), self-management support (mentioned by 13/19, 68% of adolescents), clinical utility (mentioned by all 11 health care professionals), and breadth and flexibility of tools (mentioned by 10/11, 91% of health care professionals). CONCLUSIONS: Early data suggest that COMPASS is acceptable, is relevant to common difficulties, and has clinical utility during the COVID-19 pandemic. However, shared desired features among both groups, including problem-solving and integration with diabetes technology to support self-management; creating a safe peer-to-peer sense of community; and broadening the representation of cultures, lived experience stories, and diabetes challenges, could further improve the potential of the chatbot. On the basis of these findings, COMPASS is currently being improved to be tested in a feasibility study.
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Although intensive care medicine (ICM) is a relatively young discipline, it has rapidly developed into a full-fledged and highly specialized specialty covering several fields of medicine. The COVID-19 pandemic led to a surge in intensive care unit demand and also bring unprecedented development opportunities for this area. Multiple new technologies such as artificial intelligence (AI) and machine learning (ML) were gradually being applied in this field. In this study, through an online survey, we have summarized the potential uses of ChatGPT/GPT-4 in ICM range from knowledge augmentation, device management, clinical decision-making support, early warning systems, and establishment of intensive care unit (ICU) database.
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BACKGROUND: During the COVID-19 pandemic, health care systems were faced with the urgent need to implement strategies to address the behavioral health needs of health care workers. A primary concern of any large health care system is developing an easy-to-access, streamlined system of triage and support despite limited behavioral health resources. OBJECTIVE: This study provides a detailed description of the design and implementation of a chatbot program designed to triage and facilitate access to behavioral health assessment and treatment for the workforce of a large academic medical center. The University of California, San Francisco (UCSF) Faculty, Staff, and Trainee Coping and Resiliency Program (UCSF Cope) aimed to provide timely access to a live telehealth navigator for triage and live telehealth assessment and treatment, curated web-based self-management tools, and nontreatment support groups for those experiencing stress related to their unique roles. METHODS: In a public-private partnership, the UCSF Cope team built a chatbot to triage employees based on behavioral health needs. The chatbot is an algorithm-based, automated, and interactive artificial intelligence conversational tool that uses natural language understanding to engage users by presenting a series of questions with simple multiple-choice answers. The goal of each chatbot session was to guide users to services that were appropriate for their needs. Designers developed a chatbot data dashboard to identify and follow trends directly through the chatbot. Regarding other program elements, website user data were collected monthly and participant satisfaction was gathered for each nontreatment support group. RESULTS: The UCSF Cope chatbot was rapidly developed and launched on April 20, 2020. As of May 31, 2022, a total of 10.88% (3785/34,790) of employees accessed the technology. Among those reporting any form of psychological distress, 39.7% (708/1783) of employees requested in-person services, including those who had an existing provider. UCSF employees responded positively to all program elements. As of May 31, 2022, the UCSF Cope website had 615,334 unique users, with 66,585 unique views of webinars and 601,471 unique views of video shorts. All units across UCSF were reached by UCSF Cope staff for special interventions, with >40 units requesting these services. Town halls were particularly well received, with >80% of attendees reporting the experience as helpful. CONCLUSIONS: UCSF Cope used chatbot technology to incorporate individualized behavioral health triage, assessment, treatment, and general emotional support for an entire employee base (N=34,790). This level of triage for a population of this size would not have been possible without the use of chatbot technology. The UCSF Cope model has the potential to be scaled, adapted, and implemented across both academically and nonacademically affiliated medical settings.
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COVID-19 , Humans , Pandemics , Artificial Intelligence , Health Personnel , CommunicationABSTRACT
As COVID-19 emerged and prolonged, various changes have occurred in our lives. For example, as restrictions on daily life are lengthening, the number of people complaining of depression is increasing. In this paper, we conduct a sentiment analysis by modelling public emotions and issues through social media. Text data written on Twitter is collected by dividing it into the early and late stages of COVID-19, and emotional analysis is performed to reclassify it into positive and negative tweets. Therefore, subject modelling is performed with a total of four datasets to review the results and evaluate the modelling results. Furthermore, topic modelling results are visualised using dimensional reduction, and public opinions on COVID-19 are intuitively confirmed by generating representative words consisting of each topic in the word cloud. Additionally, we implement a COVID-chatbot that provides a question-and-answer service on COVID-19 and verifies the performance in our experiments.
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BACKGROUND: Despite significant progress in reducing tobacco use over the past 2 decades, tobacco still kills over 8 million people every year. Digital interventions, such as text messaging, have been found to help people quit smoking. Chatbots, or conversational agents, are new digital tools that mimic instantaneous human conversation and therefore could extend the effectiveness of text messaging. OBJECTIVE: This scoping review aims to assess the extent of research in the chatbot literature for smoking cessation and provide recommendations for future research in this area. METHODS: Relevant studies were identified through searches conducted in Embase, MEDLINE, APA PsycINFO, Google Scholar, and Scopus, as well as additional searches on JMIR, Cochrane Library, Lancet Digital Health, and Digital Medicine. Studies were considered if they were conducted with tobacco smokers, were conducted between 2000 and 2021, were available in English, and included a chatbot intervention. RESULTS: Of 323 studies identified, 10 studies were included in the review (3 framework articles, 1 study protocol, 2 pilot studies, 2 trials, and 2 randomized controlled trials). Most studies noted some benefits related to smoking cessation and participant engagement; however, outcome measures varied considerably. The quality of the studies overall was low, with methodological issues and low follow-up rates. CONCLUSIONS: More research is needed to make a firm conclusion about the efficacy of chatbots for smoking cessation. Researchers need to provide more in-depth descriptions of chatbot functionality, mode of delivery, and theoretical underpinnings. Consistency in language and terminology would also assist in reviews of what approaches work across the field.
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Smoking Cessation , Text Messaging , Communication , Humans , Smokers , Smoking , Smoking Cessation/methodsABSTRACT
The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%. © 2023 IEEE.
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The Internet of Things (IoT) has had a significant impact on human existence. This branch of study will lead to the creation of technology and concepts that will enable humans to communicate with machines. that is specifically developed it for a specific job. Students and faculty members need to have access to educational service. When learning became fully electronic, during the Corona pandemic, every effort was made to strengthen services such as technical support and access to education materials. Chat bot is the most popular feature on Telegram which allows a third party or user to design bot functionalities based on user requirements. As a result, autoresponder messages can help solve a variety of issues, including searching for educational sources, accessing technical support channels, and FAQs, and easing the heavy burden on technical support that occurred during the COVID-19 pandemic. By developing this service, you may get reply by essential information to lecturers, students, and the academic community while saving time and handling many requests concurrently. Where the service has been developed to be available 24 hours to provide all data and access links directly without having to search for them. © 2023 IEEE.
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During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health planning, and misinformation mitigation. However, creating such an information portal is challenging because 1) domain expertise is required to identify and curate credible and intelligible content, 2) the information needs to be updated promptly in response to the fast-changing environment, and 3) the information should be easily accessible by the general public;which is particularly difficult when most people do not have the domain expertise about the crisis. In this paper, we presented an expert-sourcing framework and created Jennifer, an AI chatbot, which serves as a credible and easy-to-access information portal for individuals during the COVID-19 pandemic. Jennifer was created by a team of over 150 scientists and health professionals around the world, deployed in the real world and answered thousands of user questions about COVID-19. We evaluated Jennifer from two key stakeholders' perspectives, expert volunteers and information seekers. We first interviewed experts who contributed to the collaborative creation of Jennifer to learn about the challenges in the process and opportunities for future improvement. We then conducted an online experiment that examined Jennifer's effectiveness in supporting information seekers in locating COVID-19 information and gaining their trust. We share the key lessons learned and discuss design implications for building expert-sourced and AI-powered information portals, along with the risks and opportunities of misinformation mitigation and beyond. © 2023 Owner/Author.
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Taking care of one's mental health properly is very important as we are trying to get past the effects caused by the COVID pandemic era, especially since the rate of COVID spread is still persistent. Many organizations, universities, and schools are continuing an online mode of learning or working from home situation to tackle the spreading of the coronavirus. Due to these situations, the user could be using electronic gadgets like laptops for long hours, often without breaks in between. This has eventually affected their mental health. The ‘ViDepBot', Video-Depression-Bot aims in helping the user to maintain their mental health by detecting their depression level early, and taking appropriate actions by faculty/counselors, parents, and friends to help them to come back to normalcy and maintaining a strong mental life. In this work, a system is proposed to determine the depression level from both the facial emotions and chat texts by the user. The FER2013 dataset is trained using deep learning architecture VGG-16 base model with additional layers which acquired an accuracy of around 87% for classifying the live face emotions. Since people tend to post their feelings and thoughts (when feeling down, depressed, or even happy) on social media such as Twitter, the sentiment140 twitter dataset was taken and trained using the machine learning algorithm Bayes theorem which acquired an accuracy of around 80% for classifying the user input texts. The user is monitored through a webcam and the emotions are recognized live. The ViDepBot regularly chats with the user and takes feedback on the mental condition of the user by analyzing the chat texts received. The emotions and chat texts help to find the depression level of the user. After determining the depression level, the ViDepBot framework provides ideal recommendations to improve the user's mood. This ViDepBot can be further developed to keep track of each student/subject person's depression level, where they would be physically present in the classrooms, once the pandemic situation subsides. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Medical care is vital to having a decent existence. Be that as it may, it is undeniably challenging to get an appointment with a specialist for each medical issue and due to the current global pandemic in the form of Coronavirus, the healthcare industry is under immense pressure to meet the ends of patients' needs. Doctors and nurses are working relentlessly to treat and help the patients in the best possible way and still, they face problems in terms of time management, technical resources, healthcare infrastructure, support staff as well as healthcare personnel. To resolve this problem, we have made a chatbot utilizing Artificial Intelligence (AI) that can analyze the illness and give fundamental insights regarding the infection by looking at the data of a patient who was previously counselled at a health specialist This will also assist in lessening the medical services costs. The chatbot is a product application intended to recreate discussions with human clients through intuitive and customized content. It is in many cases portrayed as the most moving and promising articulations of communication among people and machines utilizing Artificial Intelligence and Natural Language Processing (NLP). The chatbot stores the information in the data set to recognize the sentence and pursue an inquiry choice and answer the corresponding inquiry. Through this paper, we aim to create a fully functional chatbot that will help the patients/users to know about the disease by simply entering the symptoms they possess. Additionally, they can also get information about certain medicine by simply typing the name of the medicine. Another additional feature is the ability of the bot to answer general questions regarding healthcare and wellbeing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Social isolation has affected people globally during the COVID-19 pandemic and had a major impact on older adult's well-being. Chatbot interventions may be a way to provide support to address loneliness and social isolation in older adults. The aims of the current study were to (1) understand the distribution of a chatbot's net promoter scores, (2) conduct a thematic analysis on qualitative elaborations to the net promoter scores, (3) understand the distribution of net promoter scores per theme, and (4) conduct a single word analysis to understand the frequency of words present in the qualitative feedback. A total of 7,099 adults and older adults consented to participate in a chatbot intervention on reducing social isolation and loneliness. The average net promoter score (NPS) was 8.67 out of 10. Qualitative feedback was provided by 766 (10.79%) participants which amounted to 898 total responses. Most themes were rated as positive (517), followed by neutral (311) and a minor portion as negative (70). The following five themes were found across the qualitative responses: positive outcome (277, 30.8%), user did not address question (262, 29.2%), bonding with the chatbot (240, 26.7%), negative technical aspects (70, 7.8%), and ambiguous outcome (49, 5.5%). Themes with a positive valence were found to be associated with a higher NPS. The word "help" and it's variations were found to be the most frequently used words, which is consistent with the thematic analysis. These results show that a chatbot for social isolation and loneliness was perceived positively by most participants. More specifically, users were likely to personify the chatbot (e.g., "Cause I feel like I have a new friend!") and perceive positive personality features such as being non-judgmental, caring, and open to listen. A minor portion of the users reported dissatisfaction with chatting with a machine. Implications will be discussed.
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BACKGROUND: Chatbots are increasingly used to support COVID-19 vaccination programs. Their persuasiveness may depend on the conversation-related context. OBJECTIVE: This study aims to investigate the moderating role of the conversation quality and chatbot expertise cues in the effects of expressing empathy/autonomy support using COVID-19 vaccination chatbots. METHODS: This experiment with 196 Dutch-speaking adults living in Belgium, who engaged in a conversation with a chatbot providing vaccination information, used a 2 (empathy/autonomy support expression: present vs absent) × 2 (chatbot expertise cues: expert endorser vs layperson endorser) between-subject design. Chatbot conversation quality was assessed through actual conversation logs. Perceived user autonomy (PUA), chatbot patronage intention (CPI), and vaccination intention shift (VIS) were measured after the conversation, coded from 1 to 5 (PUA, CPI) and from -5 to 5 (VIS). RESULTS: There was a negative interaction effect of chatbot empathy/autonomy support expression and conversation fallback (CF; the percentage of chatbot answers "I do not understand" in a conversation) on PUA (PROCESS macro, model 1, B=-3.358, SE 1.235, t186=2.718, P=.007). Specifically, empathy/autonomy support expression had a more negative effect on PUA when the CF was higher (conditional effect of empathy/autonomy support expression at the CF level of +1SD: B=-.405, SE 0.158, t186=2.564, P=.011; conditional effects nonsignificant for the mean level: B=-0.103, SE 0.113, t186=0.914, P=.36; conditional effects nonsignificant for the -1SD level: B=0.031, SE=0.123, t186=0.252, P=.80). Moreover, an indirect effect of empathy/autonomy support expression on CPI via PUA was more negative when CF was higher (PROCESS macro, model 7, 5000 bootstrap samples, moderated mediation index=-3.676, BootSE 1.614, 95% CI -6.697 to -0.102; conditional indirect effect at the CF level of +1SD: B=-0.443, BootSE 0.202, 95% CI -0.809 to -0.005; conditional indirect effects nonsignificant for the mean level: B=-0.113, BootSE 0.124, 95% CI -0.346 to 0.137; conditional indirect effects nonsignificant for the -1SD level: B=0.034, BootSE 0.132, 95% CI -0.224 to 0.305). Indirect effects of empathy/autonomy support expression on VIS via PUA were marginally more negative when CF was higher. No effects of chatbot expertise cues were found. CONCLUSIONS: The findings suggest that expressing empathy/autonomy support using a chatbot may harm its evaluation and persuasiveness when the chatbot fails to answer its users' questions. The paper adds to the literature on vaccination chatbots by exploring the conditional effects of chatbot empathy/autonomy support expression. The results will guide policy makers and chatbot developers dealing with vaccination promotion in designing the way chatbots express their empathy and support for user autonomy.
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In the current post-pandemic era, the rapid spread of respiratory viruses among children and infants resulted in hospitals and pediatric intensive care units (PICUs) becoming overwhelmed. Healthcare providers around the world faced a significant challenge from the outbreak of respiratory viruses like respiratory syncytial virus (RSV), metapneumovirus, and influenza viruses. The chatbot generative pre-trained transformer, ChatGPT, which was launched by OpenAI in November 2022, had both positive and negative aspects in medical writing. Still, it has the potential to generate mitigation suggestions that could be rapidly implemented. We describe the generated suggestion from ChatGPT on 27 Feb 2023 in response to the question "What's your advice for the pediatric intensivists?" We as human authors and healthcare providers, do agree with and supplement with references these suggestions of ChatGPT. We also advocate that artificial intelligence (AI)-enabled chatbots could be utilized in seeking a vigilant and robust healthcare system to rapidly adapt to changing respiratory viruses circulating around the seasons, but AI-generated suggestions need experts to validate them, and further research is warranted.