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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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BackgroundBefore COVID pandemic, rheumatologists were not confident with telehealth for the need to adquire new technology, need of specific training and poorer reimbursement [1]. Two groups of rheumatoid arthritis (RA) patients have been identified in a study of PROMS-based telehealth use (2): the keen and the reluctant. We proposed teleconsultation followup with a whatsapp platform chatbot to our axial spondyloarthritis (AxSPA) patients with controlled disease and we asked them for preferences at the end of the study.ObjectivesTo explore the degree of acceptance of asynchronous telehealth followup with whatsapp platform chatbot among our controlled AxSPA patients under biological therapy, and to search for a patient profile more prone to telehealth consultation.MethodsA prospective study with retrospective control was performed, chosing AxSPA patients under biological therapy with stable disease, visited in our centre from 01/01 to 30/11/2021. We recruited 62 patients, but finally include 60 (2 quit for home moving or personal reasons). We offered them two teleconsultation visits (using their personal mobile), every four months, and a presential final visit one year after inclusion. The chatbot sends PROMS (BASDAI, VAS for patient global disease assessment, ASDAS, and 3 questions for extraarticular disease), and feedback and schedule for the following visits. In the case of lab test or PROMs deviation or when the patient asks for contact, he/she is phoned by nurse/doctor who solves the question and/or arranges an additional presential visit. We collect patient and disease characteristics (age, gender, educational level, employment, disease activity, duration and treatments), and patient´s satisfation and preferences in the final visit.ResultsWe included 60 patients (83,3% men), mean aged 48,22 years (SD 12,128), 36% under 45 years at inclusion. 27% had received primary, 33.9% secondary and 39% tertiary education. 83.3% were active working and only 10 patients were jobless or retired. They were Ankylosing Spondylitis (AS) (90%), HLA B27 positive (85%) with longstanding disease (mean 23 years, SD 12,8), and were receiving the first (71%), or the second (23%) biological therapy (51,7% tapered anti-TNF). 50% were never smokers and 70% presented no remarkable comorbidity;25% presented peripheral impairment, and over 40% extraarticular manifestations.At inclusion 93,3% were at remission/LDA by ASDAS/BASDAI-RCP and 4 patients were considered clinically controlled in spite of higher scores. At followup 3 patients with reduced dose needed to increase to standard dose of biological drug, with no other need of treatment change. There was no worsening from basal to final visits according BASDAI, BASFI, ASDAS-RCP or AsQOL.Patients final VAS score (1-10) assessment of telehealth consultation was very high: mean 9,14 (DS 1,498);91.7% ≥ 8 and 76.7% ≥ 9.83,3% preferred telehealth followup. There was a trend towards telehealth preferences in higher educational levels, and active working (86% vs 70%) but not statistically significant. We found no correlation with gender, age and disease characteristics tested.ConclusionAsynchronous teleconsultation seems promising, not inferior to presential consultation and preferred for follow-up by our AxSpa patients with stable disease with biological drugs. We met some "reluctant patients”, that were more inactive working and with lower educational levels, but the differences were not significant. Further reserarch is needed with this telehealth model in other age and disease populations (RA), in order to characterize the reluctant and keen patients.References[1]Muehlensiepen F, et al. Acceptance of Telerheumatology by Rheumatologists and General Practitioners in Germany: Nationwide Cross-sectional Survey Study. J Med Internet Res. 2021 Mar 29;23(3):e23742.[2]Knudsen LR, et al. Experiences With Telehealth Followup in Patients With Rheumatoid Arthritis: A Qualitative Interview Study. Arthritis Care Res (Hoboken). 2018 Sep;70(9):1366-1372.AcknowledgementsGrupo INNOBIDE.Disclosure of I terestsNone Declared.
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BackgroundThe use of telehealth in the control of rheumatic diseases had been scarce, but COVID pandemic forced to try alternatives to classic face-to-face consultation, and an overflow of telehealth consultations appeared, mainly synchronous (phone, video calls), and finally asynchronous. We try to demonstrate that asynchronous WhatsApp teleconsultation is a good alternative, at least for followup of patients that find it difficult to attend face-to-face visits. We chose axial spondyloarthritis (AxSPA) patients under biological therapy with controlled disease and we proposed teleconsultation with a WhatsApp platform chatbot created for this purpose. The chatbot sends PROMS (BASDAI, VAS for patient global disease assessment, ASDAs, and 3 questions for extraarticular disease), and receive feedback and schedule for the following visits.ObjectivesTo prove that teleconsultation through WhatsApp platform is not inferior to face-to-face consultation in terms of maintaining axial SPA patients disease controlled.MethodsProspective study with retrospective control of patients diagnosed of Axial SPA, fulfilling ASAS criteria and with stable disease under biological therapy for the previous year, recruited from 01 jan to 30 nov 2021. We recruited 62 patients, but two of them gave up (personal reasons, one moved to other region), so we finally include 60 patients. We offer them two teleconsultation visits with their personal mobile device, every four months, and a face-to-face final visit one year after inclusion. In the case of lab test or PROMs deviation or when the patient asks for contact (possible via WhatsApp) he/she is called up by the person in charge (nurse/doctor) that solves the question and arranges an additional presential visit if needed. We consider disease controlled if BASDAI <4, ASDAS < 2,1 or if in rheumatologist´s opinion there is no need to change treatment. We collect patient and disease information (age, gender, employment, characteristics of the disease, previous and actual treatment), activity (BASDAI, PCR, ASDAS), physical function (BASFI), and Quality of life (AsQol).Results60 patients (50 men, 83,3%) were included, mean aged 48,22 years (SD 12,128), 36% were under 45 years at the time of inclusion. They were mostly Ankylosing Spondylitis (AS) (90%;only 6 non radiographic SPA), positive HLA B27 (85%) and with longstanding disease (mean 23 years, SD 12,8), and only 6 patients less than five years. 25% had peripheral impairment (arthritis/dactylitis/enthesitis), and more than 40% presented extraarticular manifestations, mainly psoriasis (26,7%) and uveitis (21%)71,7% were under their first biological (TNF inhibitor, mostly adalimumab), 23,3% were refractory to the first, and 3 patients to at least two biologicals. 51,7% of patients were treated with tapered dose of TNF inhibitors. At inclusion 93,3 % presented remission/LDA by ASDAS/BASDAI-RCP. Only 4 patients included presented higher activity scores but were considered clinically controlled.Table 1.We did not find meaningful clinical differences between basal to final visits in BASDAI, BASFI, ASDAS-RCP or AsQOL.3 patients with reduced dose of biological drug needed to increase to standard dose with no other need to treatment adjustment.ConclusionWe consider asynchronous teleconsultation is promising, and not inferior to face to face consultation in terms of keeping disease control and quality of life, especially for follow-up in patients with stable rheumatic disease, The clinical results presented here are consistent with this considerations.AcknowledgementsGrupo INNOBIDE.Disclosure of InterestsNone Declared.
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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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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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Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.
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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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In this chapter the use of chatbots is advocated as a technological channel to facilitate learning communities to minimize feelings of isolation and falling academic engagement, owing to COVID-19 mitigation strategies. These facets are reported to be common challenges faced by students transitioning from school to university (Hone and El Said, 2016). Chatbots facilitate communication between students via a chat interface powered by artificial intelligence (Desaulniers, 2016), and make use of pattern matching to provide personalised experiences (Gill, 2019). Chatbots offer an innovative approach toward improving establishing learning communities by "tapping-in" to the popularity of mobile phone use (Chaudhuri, 2008). Although there has been a rise in the adoption of chatbots across the Higher Education landscape (Studente, Ellis and Garivaldis, 2020), research in the area is still relatively new (Sandoval, 2018). This chapter reports upon a study conducted at a London University, with a largely international student base. During the physical closure of the university owing to lockdown measures, a chatbot called Differ was used to not only provide a channel of social support for students, but to also facilitate collaborative study support across a number of modules during online delivery of classes. © 2022 Nova Science Publishers, Inc. All rights reserved.
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The coronavirus outbreak has far-reaching ramifications for civilizations all around the world. People are worried and have a lot of requests. A research department from Covid19 Awareness was our recommendation. We supplemented it with AI-based chatbot models to aid hospitals, patients, medical facilities, and congested areas such as airports. We propose to develop this chatbot to support current scenarios and enable hospitals or governments to achieve more to solve the objective, given the two primary factors that inexpensive and fast production is now necessary. It is an immediate necessity in this epidemic circumstance. We built this bot from the ground up to be open source, so that anybody or any institution can use it to fight Corona, and commercialization is strictly prohibited. This bot isn't for sale;instead, we'd like to devote it to the country to help with current pandemic situations. The design of advanced artificial intelligence is presented in this paper (AI). If patients are exposed to COVID-19, the chatbot assesses the severity of the illness and consults with registered clinicians if the symptoms are severe, evaluating the diagnosis and recommending prompt action. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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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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Orientation: The taxability of e-commerce transactions have been the subject of many studies to protect governments from Value-Added Tax (VAT) erosion, illegal recovery and fraud. Research purpose: This study critically analyses the challenges posed by e-commerce transactions in South Africa's VAT Act . Recommendations are made for amendments to the VAT Act to improve rules to effectively tax e-commerce transactions occurring in South Africa. Motivation for the study: Globally, including in South Africa, enforcing relevant VAT legislation to target output tax collections and input tax credits from e-commerce transactions aptly remains a challenge. Research approach/design and method: By integrating qualitative literature reviews and comparative synthesis, this study employed a comparative legal methodology. VAT levied on e-commerce transactions in South Africa is compared to the Organisation for Economic Co-operation and Development's guidelines as well as New Zealand's and Australia's Goods and Services Tax legislations. Main Findings: While the South African VAT Act aligns with international best practices on the use of intermediaries, there are some differences as detailed in the study. Practical/managerial implications: To align with international trade counterparts, the South African VAT Act should differentiate between business-to-business and business-to-consumer sales. A provision concerning the place of consumption for bundled goods should be included in the VAT Act . The VAT Act should contain a provision that allows bad debts to be claimed on cash sales made instead of total sales made. Contribution/value-add: This study harmonises South African VAT legislation with international best practices within the context of continual advancement of e-commerce transactions.
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This article aims to answer frequently asked questions about the Covid-19 pandemic using ChatGPT and contribute to the spread of accurate information about the pandemic. The article provides general information about the ways Covid-19 is spread, symptoms, diagnosis, treatment, vaccines and pandemic management. It also provides advice on infection control, vaccination campaigns and emergency preparedness.
Subject(s)
COVID-19 , Humans , Pandemics/prevention & control , Infection ControlABSTRACT
The COVID-19 pandemic has marked a considerable event in the history of all countries, causing a high degree of mortality rate in older adults, in Peru health care for this group of users has become relevant. In this sense, the EsSalud Center for the Elderly (Social Health Insurance) in the town of Sicuani (CAM - Sicuani) needs to protect its population. This research consisted of proposing a reference method for health consultations based on chatbot, avoiding face-to-face consultations;for which a reference method was developed to design and develop the chatbot, in order to allow it to answer user queries online and immediately. For this purpose, information was obtained from CAM - Sicuani regarding the daily queries made by users. Information was also collected on the topics, questions and answers related to COVID-19, this from the websites of the World Health Organization (WHO), EsSalud, Ministry of Health (MINSA). On the other hand, there are not many research and projects related to the chatbot. A chatbot development method was developed based on Amir Shevat's book consisting of 4 phases;The first phase called use case definition and exploration, the second conversation scripts, the third design and testing and finally the development of the chatbot. Regarding the results, through the analysis of the metrics it is shown that the chatbot is efficient in response time with an average of 0.8 ms in the confusion rate, the chatbot responds in real time;for the performance of the chatbot, it was verified that it is flexible and optimal, since the capacity of the chatbot is adaptable;for the last metric, which is the rating of the chatbot, it was evaluated based on star rating;through a visual scale, where an average of users was randomly selected for the chatbot tests in this first initial version, who were satisfied with a rating of 3 - 5 stars;With the chatbot, it contributes to having a significant improvement in the process of attending to queries, generating a positive attitude in users;In addition, CAM - Sicuani and its users will benefit from the chatbot in relation to minimizing costs and optimizing time, which is not subject to any alteration. We conclude that the chatbot in its initial version works efficiently, in addition this innovation will prevent people from attending these health consultation services in person, on the other hand the digital transformation is promoted and with this a competitive advantage is obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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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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Purpose: The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots. Design/methodology/approach: This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering. Findings: Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students' perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education. Research limitations/implications: The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective. Originality/value: This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts. © 2023, Emerald Publishing Limited.