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Artificially intelligent physical activity digital assistants that use the full spectrum of machine learning capabilities have not yet been developed and examined. This study aimed to explore potential users' perceptions and expectations of using such a digital assistant. Six 90-min online focus group meetings (n = 45 adults) were conducted. Meetings were recorded, transcribed and thematically analysed. Participants embraced the idea of a 'digital assistant' providing physical activity support. Participants indicated they would like to receive notifications from the digital assistant, but did not agree on the number, timing, tone and content of notifications. Likewise, they indicated that the digital assistant's personality and appearance should be customisable. Participants understood the need to provide information to the digital assistant to allow for personalisation, but varied greatly in the extent of information that they were willing to provide. Privacy issues aside, participants embraced the idea of using artificial intelligence or machine learning in return for a more functional and personal digital assistant. In sum, participants were ready for an artificially intelligent physical activity digital assistant but emphasised a need to personalise or customise nearly every feature of the application. This poses challenges in terms of cost and complexity of developing the application., (© 2024 The Author(s). Applied Psychology: Health and Well‐Being published by John Wiley & Sons Ltd on behalf of International Association of Applied Psychology.)

This article contends that the responsible artificial intelligence (AI) approach-which is the dominant ethics approach ruling most regulatory and ethical guidance-falls short because it overlooks the impact of AI on human relationships. Focusing only on responsible AI principles reinforces a narrow concept of accountability and responsibility of companies developing AI. This article proposes that applying the ethics of care approach to AI regulation can offer a more comprehensive regulatory and ethical framework that addresses AI's impact on human relationships. This dual approach is essential for the effective regulation of AI in the domain of mental health care. The article delves into the emergence of the new "therapeutic" area facilitated by AI-based bots, which operate without a therapist. The article highlights the difficulties involved, mainly the absence of a defined duty of care toward users, and shows how implementing ethics of care can establish clear responsibilities for developers. It also sheds light on the potential for emotional manipulation and the risks involved. In conclusion, the article proposes a series of considerations grounded in the ethics of care for the developmental process of AI-powered therapeutic tools., (©Tamar Tavory. Originally published in JMIR Mental Health (https://mental.jmir.org), 19.09.2024.)

Purpose/objectives: This study proposes the utilization of a Natural Language Processing tool to create a semantic search engine for dental education while addressing the increasing concerns of accuracy, bias, and hallucination in outputs generated by AI tools. The paper focuses on developing and evaluating DentQA, a specialized question-answering tool that makes it easy for students to seek information to access information located in handouts or study material distributed by an institution., Methods: DentQA is structured upon the GPT3.5 language model, utilizing prompt engineering to extract information from external dental documents that experts have verified. Evaluation involves non-human metrics (BLEU scores) and human metrics for the tool's performance, relevance, accuracy, and functionality., Results: Non-human metrics confirm DentQA's linguistic proficiency, achieving a Unigram BLEU score of 0.85. Human metrics reveal DentQA's superiority over GPT3.5 in terms of accuracy (p = 0.00004) and absence of hallucination (p = 0.026). Additional metrics confirmed consistent performance across different question types (X 2  (4, N = 200) = 13.0378, p = 0.012). User satisfaction and performance metrics support DentQA's usability and effectiveness, with a response time of 3.5 s and over 70% satisfaction across all evaluated parameters., Conclusions: The study advocates using a semantic search engine in dental education, mitigating concerns of misinformation and hallucination. By outlining the workflow and the utilization of open-source tools and methods, the study encourages the utilization of similar tools for dental education while underscoring the importance of customizing AI models for dentistry. Further optimizations, testing, and utilization of recent advances can contribute to dental education significantly., (© 2024 American Dental Education Association.)

Background: Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse., Objective: This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses., Methods: We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio., Results: GPT-4 and human experts displayed comparable efficacy in medical accuracy ("GPT-4 is better" at 132/251, 52.6% vs "Human expert is better" at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience., Conclusions: GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions., (© Eunbeen Jo, Sanghoun Song, Jong-Ho Kim, Subin Lim, Ju Hyeon Kim, Jung-Joon Cha, Young-Min Kim, Hyung Joon Joo. Originally published in JMIR Medical Education (https://mededu.jmir.org).)

Background: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses., Objective: This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort., Methods: A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation., Results: The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m 2 , and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251)., Conclusions: eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity., Trial Registration: ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803., (©Han Shi Jocelyn Chew, Nicholas WS Chew, Shaun Seh Ern Loong, Su Lin Lim, Wai San Wilson Tam, Yip Han Chin, Ariana M Chao, Georgios K Dimitriadish, Yujia Gao, Jimmy Bok Yan So, Asim Shabbir, Kee Yuan Ngiam. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.05.2024.)

Objectives: Conversational agents (CAs) with emerging artificial intelligence present new opportunities to assist in health interventions but are difficult to evaluate, deterring their applications in the real world. We aimed to synthesize existing evidence and knowledge and outline an evaluation framework for CA interventions., Materials and Methods: We conducted a systematic scoping review to investigate designs and outcome measures used in the studies that evaluated CAs for health interventions. We then nested the results into an overarching digital health framework proposed by the World Health Organization (WHO)., Results: The review included 81 studies evaluating CAs in experimental (n = 59), observational (n = 15) trials, and other research designs (n = 7). Most studies (n = 72, 89%) were published in the past 5 years. The proposed CA-evaluation framework includes 4 evaluation stages: (1) feasibility/usability, (2) efficacy, (3) effectiveness, and (4) implementation, aligning with WHO's stepwise evaluation strategy. Across these stages, this article presents the essential evidence of different study designs (n = 8), sample sizes, and main evaluation categories (n = 7) with subcategories (n = 40). The main evaluation categories included (1) functionality, (2) safety and information quality, (3) user experience, (4) clinical and health outcomes, (5) costs and cost benefits, (6) usage, adherence, and uptake, and (7) user characteristics for implementation research. Furthermore, the framework highlighted the essential evaluation areas (potential primary outcomes) and gaps across the evaluation stages., Discussion and Conclusion: This review presents a new framework with practical design details to support the evaluation of CA interventions in healthcare research., Protocol Registration: The Open Science Framework (https://osf.io/9hq2v) on March 22, 2021., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Large language models (LLMs) are exhibiting remarkable performance in clinical contexts, with exemplar results ranging from expert-level attainment in medical examination questions to superior accuracy and relevance when responding to patient queries compared to real doctors replying to queries on social media. The deployment of LLMs in conventional health care settings is yet to be reported, and there remains an open question as to what evidence should be required before such deployment is warranted. Early validation studies use unvalidated surrogate variables to represent clinical aptitude, and it may be necessary to conduct prospective randomized controlled trials to justify the use of an LLM for clinical advice or assistance, as potential pitfalls and pain points cannot be exhaustively predicted. This viewpoint states that as LLMs continue to revolutionize the field, there is an opportunity to improve the rigor of artificial intelligence (AI) research to reward innovation, conferring real benefits to real patients., (©Arun James Thirunavukarasu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.12.2023.)

Background: Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security)., Objective: This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework., Methods: We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis., Results: Following participants' input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence-based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure safety and reliability in health care settings. Finally, aligned with participants' feedback, we present an updated iteration of the conceptual framework for health care conversational agents (CHAT) with key considerations for CA design, development, and evaluation and implementation, complemented by 3 cross-cutting themes: ethics, user involvement, and data privacy and security., Conclusions: We present an expanded, validated CHAT and aim at guiding researchers from a variety of backgrounds and with different levels of expertise in the design, development, and evaluation and implementation of rule-based CAs in health care settings., (©Laura Martinengo, Xiaowen Lin, Ahmad Ishqi Jabir, Tobias Kowatsch, Rifat Atun, Josip Car, Lorainne Tudor Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.11.2023.)

Background: Artificial intelligence chatbot research has focused on technical advances in natural language processing and validating the effectiveness of human-machine conversations in specific settings. However, real-world chat data remain proprietary and unexplored despite their growing popularity, and new analyses of chatbot uses and their effects on mitigating negative moods are urgently needed., Objective: In this study, we investigated whether and how artificial intelligence chatbots facilitate the expression of user emotions, specifically sadness and depression. We also examined cultural differences in the expression of depressive moods among users in Western and Eastern countries., Methods: This study used SimSimi, a global open-domain social chatbot, to analyze 152,783 conversation utterances containing the terms "depress" and "sad" in 3 Western countries (Canada, the United Kingdom, and the United States) and 5 Eastern countries (Indonesia, India, Malaysia, the Philippines, and Thailand). Study 1 reports new findings on the cultural differences in how people talk about depression and sadness to chatbots based on Linguistic Inquiry and Word Count and n-gram analyses. In study 2, we classified chat conversations into predefined topics using semisupervised classification techniques to better understand the types of depressive moods prevalent in chats. We then identified the distinguishing features of chat-based depressive discourse data and the disparity between Eastern and Western users., Results: Our data revealed intriguing cultural differences. Chatbot users in Eastern countries indicated stronger emotions about depression than users in Western countries (positive: P<.001; negative: P=.01); for example, Eastern users used more words associated with sadness (P=.01). However, Western users were more likely to share vulnerable topics such as mental health (P<.001), and this group also had a greater tendency to discuss sensitive topics such as swear words (P<.001) and death (P<.001). In addition, when talking to chatbots, people expressed their depressive moods differently than on other platforms. Users were more open to expressing emotional vulnerability related to depressive or sad moods to chatbots (74,045/148,590, 49.83%) than on social media (149/1978, 7.53%). Chatbot conversations tended not to broach topics that require social support from others, such as seeking advice on daily life difficulties, unlike on social media. However, chatbot users acted in anticipation of conversational agents that exhibit active listening skills and foster a safe space where they can openly share emotional states such as sadness or depression., Conclusions: The findings highlight the potential of chatbot-assisted mental health support, emphasizing the importance of continued technical and policy-wise efforts to improve chatbot interactions for those in need of emotional assistance. Our data indicate the possibility of chatbots providing helpful information about depressive moods, especially for users who have difficulty communicating emotions to other humans., (©Hyojin Chin, Hyeonho Song, Gumhee Baek, Mingi Shin, Chani Jung, Meeyoung Cha, Junghoi Choi, Chiyoung Cha. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2023.)

Prompt engineering is a relatively new field of research that refers to the practice of designing, refining, and implementing prompts or instructions that guide the output of large language models (LLMs) to help in various tasks. With the emergence of LLMs, the most popular one being ChatGPT that has attracted the attention of over a 100 million users in only 2 months, artificial intelligence (AI), especially generative AI, has become accessible for the masses. This is an unprecedented paradigm shift not only because of the use of AI becoming more widespread but also due to the possible implications of LLMs in health care. As more patients and medical professionals use AI-based tools, LLMs being the most popular representatives of that group, it seems inevitable to address the challenge to improve this skill. This paper summarizes the current state of research about prompt engineering and, at the same time, aims at providing practical recommendations for the wide range of health care professionals to improve their interactions with LLMs., (©Bertalan Meskó. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.10.2023.)

Clinical Significance: Optometrists are well-placed to provide helpful advice and guidance to patients with visual impairment but may not know how best to do this. The availability of a reliable and comprehensive conversational agent to which patients could be directed would be a valuable supplement to clinical intervention., Background: The Artificial Intelligence in Visual Impairment (AIVI) Study is a proof-of-concept study to investigate whether ongoing information support for people with visual impairment (VI) can be provided by a dialogue-based digital assistant. The phase of the AIVI Study reported here explored the different dimensions of the information-seeking behaviour of individuals with VI: in particular, their need for information, the methods for obtaining it at present, and their views on the use of a digital assistant., Methods: Qualitative data were collected from 120 UK-resident adults who responded to an online survey who were either visually impaired (86.7%), a carer or family member of someone with VI (5.8%), or a professional involved in the support of those with VI (7.5%). In addition, 10 in-depth 1:1 semi-structured interviews explored opinions in more detail. Thematic analysis was used to analyse the findings., Results: Analysis of information needs identified 7 major themes: ocular condition; equipment, technology and adaptations; daily activities; registration; finance/employment; emotional support; and support for the carer. Participants used a wide variety of methods to access information from many sources and explained the barriers to access. Participants accepted the merit of a dialogue system aiding in a goal-directed search for specific information, but expressed reservations about its abilities in other areas, such as providing emotional support., Conclusions: Participants highlighted potential benefits, limitations, and requirements in using a digital assistant to access information about VI. These findings will inform the design of dialogue systems for populations with VI.

Background: ChatGPT-4 is the latest release of a novel artificial intelligence (AI) chatbot able to answer freely formulated and complex questions. In the near future, ChatGPT could become the new standard for health care professionals and patients to access medical information. However, little is known about the quality of medical information provided by the AI., Objective: We aimed to assess the reliability of medical information provided by ChatGPT., Methods: Medical information provided by ChatGPT-4 on the 5 hepato-pancreatico-biliary (HPB) conditions with the highest global disease burden was measured with the Ensuring Quality Information for Patients (EQIP) tool. The EQIP tool is used to measure the quality of internet-available information and consists of 36 items that are divided into 3 subsections. In addition, 5 guideline recommendations per analyzed condition were rephrased as questions and input to ChatGPT, and agreement between the guidelines and the AI answer was measured by 2 authors independently. All queries were repeated 3 times to measure the internal consistency of ChatGPT., Results: Five conditions were identified (gallstone disease, pancreatitis, liver cirrhosis, pancreatic cancer, and hepatocellular carcinoma). The median EQIP score across all conditions was 16 (IQR 14.5-18) for the total of 36 items. Divided by subsection, median scores for content, identification, and structure data were 10 (IQR 9.5-12.5), 1 (IQR 1-1), and 4 (IQR 4-5), respectively. Agreement between guideline recommendations and answers provided by ChatGPT was 60% (15/25). Interrater agreement as measured by the Fleiss κ was 0.78 (P<.001), indicating substantial agreement. Internal consistency of the answers provided by ChatGPT was 100%., Conclusions: ChatGPT provides medical information of comparable quality to available static internet information. Although currently of limited quality, large language models could become the future standard for patients and health care professionals to gather medical information., (©Harriet Louise Walker, Shahi Ghani, Christoph Kuemmerli, Christian Andreas Nebiker, Beat Peter Müller, Dimitri Aristotle Raptis, Sebastian Manuel Staubli. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.06.2023.)

In November 2022, OpenAI publicly launched its large language model (LLM), ChatGPT, and reached the milestone of having over 100 million users in only 2 months. LLMs have been shown to be useful in a myriad of health care-related tasks and processes. In this paper, I argue that attention to, public access to, and debate about LLMs have initiated a wave of products and services using generative artificial intelligence (AI), which had previously found it hard to attract physicians. This paper describes what AI tools have become available since the beginning of the ChatGPT revolution and contemplates how it they might change physicians' perceptions about this breakthrough technology., (©Bertalan Mesko. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.06.2023.)

Background: This systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were effective in changing physical activity, healthy eating, weight management behaviors, and other related health outcomes., Methods: In collaboration with a medical librarian, six electronic bibliographic databases (PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, and IEEE) were searched to identify relevant studies. Only randomized controlled trials or quasi-experimental studies were included. Studies were screened by two independent reviewers, and any discrepancy was resolved by a third reviewer. The National Institutes of Health quality assessment tools were used to assess risk of bias in individual studies. We applied the AI Chatbot Behavior Change Model to characterize components of chatbot interventions, including chatbot characteristics, persuasive and relational capacity, and evaluation of outcomes., Results: The database search retrieved 1692 citations, and 9 studies met the inclusion criteria. Of the 9 studies, 4 were randomized controlled trials and 5 were quasi-experimental studies. Five out of the seven studies suggest chatbot interventions are promising strategies in increasing physical activity. In contrast, the number of studies focusing on changing diet and weight status was limited. Outcome assessments, however, were reported inconsistently across the studies. Eighty-nine and thirty-three percent of the studies specified a name and gender (i.e., woman) of the chatbot, respectively. Over half (56%) of the studies used a constrained chatbot (i.e., rule-based), while the remaining studies used unconstrained chatbots that resemble human-to-human communication., Conclusion: Chatbots may improve physical activity, but we were not able to make definitive conclusions regarding the efficacy of chatbot interventions on physical activity, diet, and weight management/loss. Application of AI chatbots is an emerging field of research in lifestyle modification programs and is expected to grow exponentially. Thus, standardization of designing and reporting chatbot interventions is warranted in the near future., Systematic Review Registration: International Prospective Register of Systematic Reviews (PROSPERO): CRD42020216761 ., (© 2021. The Author(s).)

Background: Artificial intelligence (AI)-driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including why-explanations and how-explanations. Social theories suggest that why-explanations are better at communicating knowledge and cultivating trust among laypeople., Objective: The aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease., Methods: A cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants' responses followed by comparison-of-means tests were used to evaluate group differences in trust., Results: Depending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (P=.65) and temporal arteritis, marginally significant (P=.09). Varying disease by explanation type resulted in statistical significance for input influence (P=.001), social proof (P=.049), and no explanation (P=.006), with counterfactual explanation (P=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user's specific question and discount the diseases that they may also be aware of., Conclusions: System builders developing explanations for symptom-checking apps should consider the recipient's knowledge of a disease and tailor explanations to each user's specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap., (©Claire Woodcock, Brent Mittelstadt, Dan Busbridge, Grant Blank. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.11.2021.)

Introduction: Clinicians rely on pharmacologic knowledge bases to answer medication questions and avoid potential adverse drug events. In late 2018, an artificial intelligence-based conversational agent, Watson Assistant (WA), was made available to online subscribers to the pharmacologic knowledge base, Micromedex®. WA allows users to ask medication-related questions in natural language. This study evaluated search method-dependent differences in the frequency of information accessed by traditional methods (keyword search and heading navigation) vs conversational agent search., Materials and Methods: We compared the proportion of information types accessed through the conversational agent to the proportion of analogous information types accessed by traditional methods during the first 6 months of 2020., Results: Addition of the conversational agent allowed early adopters to access 22 different information types contained in the 'quick answers' portion of the knowledge base. These information types were accessed 117,550 times with WA during the study period, compared to 33,649,651 times using traditional search methods. The distribution across information types differed by method employed (c 2 test, P < .0001). Single drug/dosing, FDA/non-FDA uses, adverse effects, and drug administration emerged as 4 of the top 5 information types accessed by either method. Intravenous compatibility was accessed more frequently using the conversational agent (7.7% vs. 0.6% for traditional methods), whereas dose adjustments were accessed more frequently via traditional methods (4.8% vs. 1.4% for WA)., Conclusion: In a widely used pharmacologic knowledge base, information accessed through conversational agents versus traditional methods differed. User-centered studies are needed to understand these differences., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Artificial intelligence-driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care-leveraging innovative telehealth solutions during the COVID-19 pandemic. They allow for automatic acute care triaging and chronic disease management, including remote monitoring, preventive care, patient intake, and referral assistance. This paper focuses on the current clinical needs and applications of artificial intelligence-driven voice chatbots to drive operational effectiveness and improve patient experience and outcomes., (©Tomasz Jadczyk, Wojciech Wojakowski, Michal Tendera, Timothy D Henry, Gregory Egnaczyk, Satya Shreenivas. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.05.2021.)

Background: Artificial intelligence (AI)-driven chatbots are increasingly being used in health care, but most chatbots are designed for a specific population and evaluated in controlled settings. There is little research documenting how health consumers (eg, patients and caregivers) use chatbots for self-diagnosis purposes in real-world scenarios., Objective: The aim of this research was to understand how health chatbots are used in a real-world context, what issues and barriers exist in their usage, and how the user experience of this novel technology can be improved., Methods: We employed a data-driven approach to analyze the system log of a widely deployed self-diagnosis chatbot in China. Our data set consisted of 47,684 consultation sessions initiated by 16,519 users over 6 months. The log data included a variety of information, including users' nonidentifiable demographic information, consultation details, diagnostic reports, and user feedback. We conducted both statistical analysis and content analysis on this heterogeneous data set., Results: The chatbot users spanned all age groups, including middle-aged and older adults. Users consulted the chatbot on a wide range of medical conditions, including those that often entail considerable privacy and social stigma issues. Furthermore, we distilled 2 prominent issues in the use of the chatbot: (1) a considerable number of users dropped out in the middle of their consultation sessions, and (2) some users pretended to have health concerns and used the chatbot for nontherapeutic purposes. Finally, we identified a set of user concerns regarding the use of the chatbot, including insufficient actionable information and perceived inaccurate diagnostic suggestions., Conclusions: Although health chatbots are considered to be convenient tools for enhancing patient-centered care, there are issues and barriers impeding the optimal use of this novel technology. Designers and developers should employ user-centered approaches to address the issues and user concerns to achieve the best uptake and utilization. We conclude the paper by discussing several design implications, including making the chatbots more informative, easy-to-use, and trustworthy, as well as improving the onboarding experience to enhance user engagement., (©Xiangmin Fan, Daren Chao, Zhan Zhang, Dakuo Wang, Xiaohua Li, Feng Tian. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.01.2021.)

Objective: To determine whether smart conversational agents can be used for detection of neuropsychiatric disorders. Therefore, we reviewed the technologies used, targeted mental disorders and validation procedures of relevant proposals in this field., Methods: We searched Scopus, PubMed, Pro-Quest, IEEE Xplore, Web of Science, CINAHL and the Cochrane Library using a predefined search strategy. Studies were included if they focused on neuropsychiatric disorders and involved conversational data for detection and diagnosis. They were assessed for eligibility by independent reviewers and ultimately included if a consensus was reached about their relevance., Results: 2356 references were initially retrieved. Eventually, 17 articles - referring 9 smart conversational agents - met the inclusion criteria. Out of the selected studies, 7 are targeted at neurocognitive disorders, 7 at depression and 3 at other conditions. They apply diverse technological solutions and analysis techniques (82.4% use Artificial Intelligence), and they usually rely on gold standard tests for criterion validity assessment. Acceptability, reliability and other aspects of validity were rarely addressed., Conclusion: The use of smart conversational agents for the detection of neuropsychiatric disorders is an emerging and promising field of research, with a broad coverage of mental disorders and extended use of AI. However, the few published studies did not undergo robust psychometric validation processes. Future research in this field would benefit from more rigorous validation mechanisms and standardized software and hardware platforms., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Artificial intelligence virtual health assistants are a promising emerging technology. This study is a process evaluation of a 12-week pilot physical activity and diet program delivered by virtual assistant "Paola". This single-arm repeated measures study ( n = 28, aged 45-75 years) was evaluated on technical performance (accuracy of conversational exchanges), engagement (number of weekly check-ins completed), adherence (percentage of step goal and recommended food servings), and user feedback. Paola correctly asked scripted questions and responded to participants during the check-ins 97% and 96% of the time, respectively, but correctly responded to spontaneous exchanges only 21% of the time. Participants completed 63% of weekly check-ins and conducted a total of 3648 exchanges. Mean dietary adherence was 91% and was lowest for discretionary foods, grains, red meat, and vegetables. Participants met their step goal 59% of the time. Participants enjoyed the program and found Paola useful during check-ins but not for spontaneous exchanges. More in-depth knowledge, personalized advice and spontaneity were identified as important improvements. Virtual health assistants should ensure an adequate knowledge base and ability to recognize intents and entities, include personality and spontaneity, and provide ongoing technical troubleshooting of the virtual assistant to ensure the assistant remains effective.

Background: The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption., Objective: This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents., Methods: PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another., Results: A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback., Conclusions: The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents., International Registered Report Identifier (irrid): RR2-10.2196/16934., (©Madison Milne-Ives, Caroline de Cock, Ernest Lim, Melissa Harper Shehadeh, Nick de Pennington, Guy Mole, Eduardo Normando, Edward Meinert. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.10.2020.)

Background: Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet., Objective: The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations., Methods: We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model., Results: Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper., Conclusions: As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot's relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles., (©Jingwen Zhang, Yoo Jung Oh, Patrick Lange, Zhou Yu, Yoshimi Fukuoka. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.09.2020.)

Background: Patient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human voice and select an appropriate follow-up time for quantitative, automatic, and personalized patient follow-up. Patient feedback and voice information could be collected and converted into text data automatically., Objective: The primary objective of this study was to compare the cost-effectiveness of AI-assisted follow-up to manual follow-up of patients after surgery. The secondary objective was to compare the feedback from AI-assisted follow-up to feedback from manual follow-up., Methods: The AI-assisted follow-up system was adopted in the Orthopedic Department of Peking Union Medical College Hospital in April 2019. A total of 270 patients were followed up through this system. Prior to that, 2656 patients were followed up by phone calls manually. Patient characteristics, telephone connection rate, follow-up rate, feedback collection rate, time spent, and feedback composition were compared between the two groups of patients., Results: There was no statistically significant difference in age, gender, or disease between the two groups. There was no significant difference in telephone connection rate (manual: 2478/2656, 93.3%; AI-assisted: 249/270, 92.2%; P=.50) or successful follow-up rate (manual: 2301/2478, 92.9%; AI-assisted: 231/249, 92.8%; P=.96) between the two groups. The time spent on 100 patients in the manual follow-up group was about 9.3 hours. In contrast, the time spent on the AI-assisted follow-up was close to 0 hours. The feedback rate in the AI-assisted follow-up group was higher than that in the manual follow-up group (manual: 68/2656, 2.5%; AI-assisted: 28/270, 10.3%; P<.001). The composition of feedback was different in the two groups. Feedback from the AI-assisted follow-up group mainly included nursing, health education, and hospital environment content, while feedback from the manual follow-up group mostly included medical consultation content., Conclusions: The effectiveness of AI-assisted follow-up was not inferior to that of manual follow-up. Human resource costs are saved by AI. AI can help obtain comprehensive feedback from patients, although its depth and pertinence of communication need to be improved., (©Yanyan Bian, Yongbo Xiang, Bingdu Tong, Bin Feng, Xisheng Weng. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.05.2020.)

Background Methods Results Conclusions We developed MARVIN, an artificial intelligence (AI)‐based chatbot that provides 24/7 expert‐validated information on self‐management‐related topics for people with HIV. This study assessed (1) the feasibility of using MARVIN, (2) its usability and acceptability, and (3) four usability subconstructs (perceived ease of use, perceived usefulness, attitude towards use, and behavioural intention to use).In a mixed‐methods study conducted at the McGill University Health Centre, enrolled participants were asked to have 20 conversations within 3 weeks with MARVIN on predetermined topics and to complete a usability questionnaire. Feasibility, usability, acceptability, and usability subconstructs were examined against predetermined success thresholds. Qualitatively, randomly selected participants were invited to semi‐structured focus groups/interviews to discuss their experiences with MARVIN. Barriers and facilitators were identified according to the four usability subconstructs.From March 2021 to April 2022, 28 participants were surveyed after a 3‐week testing period, and nine were interviewed. Study retention was 70% (28/40). Mean usability exceeded the threshold (69.9/68), whereas mean acceptability was very close to target (23.8/24). Ratings of attitude towards MARVIN's use were positive (+14%), with the remaining subconstructs exceeding the target (5/7). Facilitators included MARVIN's reliable and useful real‐time information support, its easy accessibility, provision of convivial conversations, confidentiality, and perception as being emotionally safe. However, MARVIN's limited comprehension and the use of Facebook as an implementation platform were identified as barriers, along with the need for more conversation topics and new features (e.g., memorization).The study demonstrated MARVIN's global usability. Our findings show its potential for HIV self‐management and provide direction for further development. [ABSTRACT FROM AUTHOR]

The chatbot has been increasingly applied and investigated in education, along with many review studies from different aspects. However, few reviews have been conducted on chatbot-assisted learning from the pedagogical and implementational aspects, which may provide implications for future application and investigation of educational chatbots. To fill in the gaps, we reviewed relevant studies from the pedagogical and implementational aspects. Forty-six articles from Web of Science and Scopus databases were screened by predefined criteria and analysed step by step following the PRISMA framework. The finding showed diversified learning activities (i.e. exercise, instructions, role-playing activities, collaborative product design, independent writing, storytelling/book-reading, digital gameplay, and open-ended debates) that chatbots could support through presenting knowledge, facilitating practices, supervising and guiding learning activities, and providing emotional support. Chabot-assisted learning was applied in 14 disciplines, mostly in-class for one session, and had overall positive outcomes from academic and affective aspects. Based on the review results, we proposed a RAISE model of effective chatbot-assisted learning: Repetitiveness, Authenticity, Interactivity, Student-centredness, and Enjoyment. We identified eght theories that might be useful in analysing and supporting chatbot-assisted learning: constructivist theories, situated/contextualised learning theories, cognitive theories of multimedia learning, self-regulated learning theories, output hypotheses, flow theory, collaborative learning theories and motivation theories. Future studies on chatbot-assisted learning may be conducted on the use of theoretical frameworks, the application of various technological-pedagogical approaches and learning activities, and the long-term, out-of-class implementations in new areas. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between second or foreign language (L2) writing learners' perception of ChatGPT, their usage behavior, and L2 writing proficiency, including complexity, accuracy, and fluency. A mixed-methods design was utilized to collect data from 215 university L2 writing learners through surveys and argumentative writing samples, and four students participated in a focus group discussion. Results indicated that performance expectancy and effort expectancy significantly influenced students' intended behaviors, while the effect of social influence was non-significant. Facilitating conditions significantly predicted actual behaviors. Furthermore, the study found that ChatGPT usage significantly predicted only complexity. During the focus group discussion, students expressed the need for support from teachers. The study highlights the importance of human agency in writing instruction, including clear principles, critical evaluation of ChatGPT feedback, and constant monitoring to reinforce human roles in writing. These findings provide insights into students' perceptions and behaviors toward AI-based writing tools and inform effective writing instruction in the AI era. [ABSTRACT FROM AUTHOR]

In today's time, the research mainly focuses on designing a Chat-bot which responds to a user's query in the most efficient manner. However, state-of-the-art works on chat-bot design are unable to emotionally connect with the user and follow-up to a conversation. In this paper, a socially and emotionally active intelligent assistant, AMIBO is proposed. It detects and recognizes the face, perceives emotion via speech and vision capabilities, provides empathetic and intelligent responses. In order to enhance the experience of AMIBO, navigation and information delivery system have been integrated in the bot. The analysis of the feature vector (on 450 subjects) is done for all the initially taken 75 distances and 15 angles reduced to 26 distances and 11 angles feature vector. An encouraging accuracy of 99% was achieved on CK+ dataset and 97% on KDEF dataset. [ABSTRACT FROM AUTHOR]

This study presents a scoping review of research on artificial intelligence (AI)- driven virtual patients (VPs) for communication skills training of healthcare students. We aimed to establish what is known about these emergent learning tools, to characterise their design and implementation into training programmes. The preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews framework was consulted. Searches occurred in six online databases to capture relevant articles from 2014 to 2024. Eight articles from five disciplines met inclusion criteria. A variety of design approaches, creation tools and VP appearances exist. Educational considerations such as consultation of educational theory, curricular integration and provision of feedback was overall lacking. Neutral to positive evaluations of satisfaction and acceptance of the VPs were provided by most students. Emerging literature suggests AI-driven VPs are increasingly being utilised for communication skills training, although their effectiveness is not established. Careful consideration of technological design features, educational theory and evidence regarding communication skill development should occur by clinical educators wishing to include AI-driven VPs in their training programmes. Further empirical research involving key stakeholders is needed to learn more about this technology. [ABSTRACT FROM AUTHOR]

Humanity has fantasized about artificial intelligence tools able to discuss with human beings fluently for decades. Numerous efforts have been proposed ranging from ELIZA to the modern vocal assistants. Despite the large interest in this research and innovation field, there is a lack of common understanding on the concept of conversational agents and general over expectations that hide the current limitations of existing solutions. This work proposes a literature review on the subject with a focus on the most promising type of conversational agents that are powered on top of knowledge bases and that can offer the ground knowledge to hold conversation autonomously on different topics. We describe a conceptual architecture to define the knowledge-enhanced conversational agents and investigate different domains of applications. We conclude this work by listing some promising research pathways for future work. [ABSTRACT FROM AUTHOR]

The study explores factors affecting university students' behavioural intentions in adopting an academic advising chatbot. The study focuses on functional, socio-emotional, and relational factors affecting students' acceptance of an AI-driven academic advising chatbot. The research is based on a conceptual model derived from several constructs of traditional technology acceptance models, TAM, UTAUT, the latest AI-driven self-service technologies models, the Service Robot Acceptance (sRAM) model, and the intrinsic motivation Self Determination Theory (SDT) model. The proposed conceptual model has been tailored to an educational context. A questionnaire Survey of Non-purposive sampling technique was applied to collect data points from 207 university students from two major universities in the UAE. Subsequently, PLS-SEM causal modelling was applied for hypothesis testing. The results revealed that the functional elements, perceived ease of use and social influence significantly affect behavioural intention for chatbots' acceptance. However, perceived usefulness, autonomy, and trust did not show significant evidence of influence on the acceptance of an advising chatbot. The study reviews chatbot literature and presents recommendations for educational institutions to implement AI-driven chatbots effectively for academic advising. It is one of the first studies that assesses and examines factors that impact the willingness of higher education students to accept AI-driven academic advising chatbots. This study presents several theoretical contributions and practical implications for successful deployment of service-oriented chatbots for academic advising in the educational sector. [ABSTRACT FROM AUTHOR]

Chatbots increase business productivity by handling customer conversations instead of human agents. Similar rationale applies to use chatbots in the healthcare sector, especially for health coaches who converse with clients. Chatbots are nascent in healthcare. Study findings have been mixed in terms of engagement and their impact on outcomes. Questions remain as to chatbot acceptability with coaches and other providers; studies have focused on clients. To clarify perceived benefits of chatbots in HIV interventions we conducted virtual focus groups with 13 research staff, eight community advisory board members, and seven young adults who were HIV intervention trial participants (clients). Our HIV healthcare context is important. Clients represent a promising age demographic for chatbot uptake. They are a marginalized population warranting consideration to avoid technology that limits healthcare access. Focus group participants expressed the value of chatbots for HIV research staff and clients. Staff discussed how chatbot functions, such as automated appointment scheduling and service referrals, could reduce workloads while clients discussed the after-hours convenience of these functions. Participants also emphasized that chatbots should provide relatable conversation, reliable functionality, and would not be appropriate for all clients. Our findings underscore the need to further examine appropriate chatbot functionality in HIV interventions. [ABSTRACT FROM AUTHOR]

Background: Digital mental health interventions (DMHIs) may reduce treatment access issues for those experiencing depressive and/or anxiety symptoms. DMHIs that incorporate relational agents may offer unique ways to engage and respond to users and to potentially help reduce provider burden. This study tested Woebot for Mood & Anxiety (W-MA-02), a DMHI that employs Woebot, a relational agent that incorporates elements of several evidence-based psychotherapies, among those with baseline clinical levels of depressive or anxiety symptoms. Changes in self-reported depressive and anxiety symptoms over 8 weeks were measured, along with the association between each of these outcomes and demographic and clinical characteristics. Methods: This exploratory, single-arm, 8-week study of 256 adults yielded non-mutually exclusive subsamples with either clinical levels of depressive or anxiety symptoms at baseline. Week 8 Patient Health Questionnaire-8 (PHQ-8) changes were measured in the depressive subsample (PHQ-8 ≥ 10). Week 8 Generalized Anxiety Disorder-7 (GAD-7) changes were measured in the anxiety subsample (GAD-7 ≥ 10). Demographic and clinical characteristics were examined in association with symptom changes via bivariate and multiple regression models adjusted for W-MA-02 utilization. Characteristics included age, sex at birth, race/ethnicity, marital status, education, sexual orientation, employment status, health insurance, baseline levels of depressive and anxiety symptoms, and concurrent psychotherapeutic or psychotropic medication treatments during the study. Results: Both the depressive and anxiety subsamples were predominantly female, educated, non-Hispanic white, and averaged 38 and 37 years of age, respectively. The depressive subsample had significant reductions in depressive symptoms at Week 8 (mean change =—7.28, SD = 5.91, Cohen's d = -1.23, p < 0.01); the anxiety subsample had significant reductions in anxiety symptoms at Week 8 (mean change = -7.45, SD = 5.99, Cohen's d = -1.24, p < 0.01). No significant associations were found between sex at birth, age, employment status, educational background and Week 8 symptom changes. Significant associations between depressive and anxiety symptom outcomes and sexual orientation, marital status, concurrent mental health treatment, and baseline symptom severity were found. Conclusions: The present study suggests early promise for W-MA-02 as an intervention for depression and/or anxiety symptoms. Although exploratory in nature, this study revealed potential user characteristics associated with outcomes that can be investigated in future studies. Trial Registration: This study was retrospectively registered on ClinicalTrials.gov (#NCT05672745) on January 5th, 2023. [ABSTRACT FROM AUTHOR]

Recently, Artificial Intelligence (AI)-powered chatbots and virtual humans have assumed significant roles in various domains due to their ability to interact with users and perform tasks based on their intended purpose. Virtual humans have received considerable attention in various industries due to their lifelike human appearance, behaviour, and ability to convey emotions, especially in virtual reality contexts. Conversely, chatbots are finding use in a wide range of applications and represent a promising feature of human-computer interaction due to their efficient communication with humans. Therefore, this study aims to develop a real-time chatbot that can effectively convey emotions through facial expressions, thereby promoting realistic communication. To achieve this, several advanced AI models were employed to address different aspects, including speech recognition, emotion synthesis, and response generation. The methodology, models used, components, and results are explained in detail, and the results of the user study are also presented. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (AI) tools, such as ChatGPT, have received great attention from researchers, the media, and the public. They are gladly and frequently used for text production by many people. These tools have undeniable strengths but also weaknesses that must be addressed. In this squib we ask to what extent these tools can be employed by users for individual learning as well as for knowledge construction to spark a collective endeavor of developing new insights. We take a social, collective notion of knowledge as a basis and argue that users need to establish a dialog that goes beyond knowledge telling (simply writing what one knows) and stimulates knowledge transformation (converting knowledge into complex relational argumentation structures). Generative AI tools do not have any conceptual knowledge or conscious understanding, as they only use word transitions and rely on probabilities of word classes. We suggest, however, that argumentative dialogs among humans and AI tools can be achieved with appropriate prompts, where emergent processes of joint knowledge construction can take place. Based on this assumption, we inquire into the human and into the AI parts of communication and text production. For our line of argument, we borrow from research on individual and collaborative writing, group cognition, and the co-evolution of cognitive and social systems. We outline future CSCL research paths that might take the human-AI co-construction of knowledge into account in terms of terminology, theory, and methodology. [ABSTRACT FROM AUTHOR]

The present narrative review seeks to unravel where we are now, and where we need to go to delineate the active ingredients in psychotherapy delivered by conversational agents (e.g., chatbots). While psychotherapy delivered by conversational agents has shown promising effectiveness for depression, anxiety, and psychological distress across several randomized controlled trials, little emphasis has been placed on the therapeutic processes in these interventions. The theoretical framework of this narrative review is grounded in prominent perspectives on the active ingredients in psychotherapy. Based on search terms derived from various theoretical perspectives, we conducted a systematic literature search of four scientific databases and identified 17 studies. Across the selected studies, three themes emerged: relationship variables, emotional venting, and cognitive factors. While methodological issues compromise the epistemic value of this evidence base, core questions also remain to be answered. Such questions include, but are not limited to, whether humans can form affectionate bonds to inanimate beings and whether these kind of mental health treatments should be understood as psychotherapy or something else. Researchers should therefore be cautious when applying theories of psychotherapy in the realm of conversational agents. We conclude the paper by introducing recommendations for future research, which we hope will help instigate methodologically sound studies in this field.

Information that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general, useful to the rest of humanity. Conversational agents can access these PKGs to answer queries related to the user's day‐to‐day life, whereas recommender systems can harness the knowledge stored in PKGs to make personalized suggestions. Despite the immense applicability of PKGs, there has not been significant research in this area. We present an extensive review of PKGs. We categorize them according to the domains in which they are most relevant; in particular, we highlight the use of PKGs in medicine, finance, and education and research. We also categorize the different ways of constructing a PKG based on the source of data required for such constructions. Furthermore, we discuss the limitations of PKGs and suggest directions for future work. This article is categorized under:Fundamental Concepts of Data and Knowledge > Human Centricity and User InteractionFundamental Concepts of Data and Knowledge > Knowledge RepresentationTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

As the role of artificial intelligence (AI) agents in information curation has emerged with recent advancements in AI technologies, the present study explored which users would potentially be susceptible to the filter bubble phenomenon. First, a large-scale analysis of conversational agent users in South Korea (N = 2808) was conducted to investigate the relative importance of content optimization algorithms in shaping positive user experience. Five user clusters were identified based on their information technology proficiency and demographics, and a multiple-group path analysis was performed to compare the influences of content optimization algorithms across the user groups. The results indicated that the personalization algorithm generally exhibited a stronger impact on evaluations of an AI agent's usefulness than the diversity algorithm. In addition, increased user age and greater Internet usage were found to decrease the importance of objectivity in shaping trust in AI agents. This study improves the understanding of the social influence of AI technology and suggests the necessity of segmented approaches in the development of AI technology. [ABSTRACT FROM AUTHOR]

Abstract This meta-analysis aimed to comprehensively review empirical studies on the effect of chatbots on learning and quantitatively synthesize their findings to produce an overall effect size. Searching several databases yielded 28 eligible reports with 31 individual effect sizes. The results revealed a significant and medium effect (g = .48) of chatbots on learning. Further analyses indicated four significant moderators: the type of instruction in the comparison group, experimental duration, chatbot type, and chatbot tasks. The highest effect sizes emerged when the comparison group had no specific support, the experiment lasted only one session, and chatbots were task-focused and took care of frequently asked questions. These results suggest that chatbots can be more effective in certain cases within their overall contribution area to learning. [ABSTRACT FROM AUTHOR]

The rise of emotional intelligence technology and the recent debate about the possibility of a "sentient" artificial intelligence (AI) urge the need to study the role of emotion during people's interactions with AIs. In customer service, human employees are increasingly replaced by AI agents, such as chatbots, and often these AI agents are equipped with emotion-expressing capabilities to replicate the positive impact of human-expressed positive emotion. But is it indeed beneficial? This research explores how, when, and why an AI agent's expression of positive emotion affects customers' service evaluations. Through controlled experiments in which the subjects interacted with a service agent (AI or human) to resolve a hypothetical service issue, we provide answers to these questions. We show that AI-expressed positive emotion can influence customers affectively (by evoking customers' positive emotions) and cognitively (by violating customers' expectations) in opposite directions. Thus, positive emotion expressed by an AI agent (versus a human employee) is less effective in facilitating service evaluations. We further underscore that, depending on customers' expectations toward their relationship with a service agent, AI-expressed positive emotion may enhance or hurt service evaluations. Overall, our work provides useful guidance on how and when companies can best deploy emotion-expressing AI agents. Customer service employees are generally advised to express positive emotion during their interactions with customers. The rise and maturity of artificial intelligence (AI)–powered conversational agents, also known as chatbots, beg the question: should AI agents be equipped with the ability to express positive emotion during customer service interactions? This research explores how, when, and why an AI agent's expression of positive emotion affects customers' service evaluations. We argue that AI-expressed positive emotion can influence customers via dual pathways: an affective pathway of emotional contagion and a cognitive pathway of expectation–disconfirmation. We propose that positive emotion expressed by an AI agent (versus a human employee) is less effective in facilitating service evaluations because of a heightened level of expectation–disconfirmation. We further introduce a novel individual difference variable, customers' relationship norm orientation, which affects their expectations toward the AI agent and moderates the cognitive pathway. Results from three laboratory experiments substantiate our claims. By revealing a distinctive impact of positive emotion expressed by an AI agent compared with a human employee, these findings deepen our understanding of customers' reactions to emotional AIs, and they offer valuable insights for the deployment of AIs in customer service. History: Deepa Mani, Senior Editor; Pallab Sanyal, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1179. [ABSTRACT FROM AUTHOR]

BackgroundMisuse of substances is common, can be serious and costly to society, and often goes untreated due to barriers to accessing care. Woebot is a mental health digital solution informed by cognitive behavioral therapy and built upon an artificial intelligence-driven platform to deliver tailored content to users. In a previous 2-week randomized controlled trial, Woebot alleviated depressive symptoms.ObjectiveThis study aims to adapt Woebot for the treatment of substance use disorders (W-SUDs) and examine its feasibility, acceptability, and preliminary efficacy.MethodsAmerican adults (aged 18-65 years) who screened positive for substance misuse without major health contraindications were recruited from online sources and flyers and enrolled between March 27 and May 6, 2020. In a single-group pre/postdesign, all participants received W-SUDs for 8 weeks. W-SUDs provided mood, craving, and pain tracking and modules (psychoeducational lessons and psychotherapeutic tools) using elements of dialectical behavior therapy and motivational interviewing. Paired samples t tests and McNemar nonparametric tests were used to examine within-subject changes from pre- to posttreatment on measures of substance use, confidence, cravings, mood, and pain.ResultsThe sample (N=101) had a mean age of 36.8 years (SD 10.0), and 75.2% (76/101) of the participants were female, 78.2% (79/101) were non-Hispanic White, and 72.3% (73/101) were employed. Participants' W-SUDs use averaged 15.7 (SD 14.2) days, 12.1 (SD 8.3) modules, and 600.7 (SD 556.5) sent messages. About 94% (562/598) of all completed psychoeducational lessons were rated positively. From treatment start to end, in-app craving ratings were reduced by half (87/101, 86.1% reporting cravings in the app; odds ratio 0.48, 95% CI 0.32-0.73). Posttreatment assessment completion was 50.5% (51/101), with better retention among those who initially screened higher on substance misuse. From pre- to posttreatment, confidence to resist urges to use substances significantly increased (mean score change +16.9, SD 21.4; P

There are no clear guidelines or validated models for artificial intelligence (AI)-based approaches in the monitoring of coronavirus disease 2019 (COVID-19) patients who were isolated in the community, in order to identify early deterioration of their health symptoms. Developed in partnership with Curious Thing (CT), a Sydney-based AI conversational technology, a new care robot technology was introduced in South Western Sydney (SWS) in September 2021 to manage the large numbers of low-to-medium risk patients with a COVID-19 diagnosis and who were isolating at home. The CT interface made contact with patients via their mobile phone, following a locally produced script to obtain information recording physical condition, wellness and support. The care robot has engaged over 6323 patients between 2 September to 14 December 2021. The AI-assisted phone calls effectively identified the patients requiring further support, saved clinician time by monitoring less ailing patients remotely, and enabled them to spend more time on critically ill patients, thus ensuring that service and supply resources could be directed to those at greatest need. Engagement strategies had ensured stakeholders support of this technology to meet clinical and welfare needs of the identified patient group. Feedback from both the patients and healthcare staff was positive and had informed the ongoing formulation of a more patient-centred model of virtual care. Developed in partnership with a Sydney-based artificial intelligent (AI) conversational technology, a new care robot was successfully introduced to manage 6323 low-to-medium risk clients with a COVID-19 diagnosis and who were isolating at home. This initiative proved that application of AI was efficient and safe, and demonstrated benefits to support healthcare professionals and the community. [ABSTRACT FROM AUTHOR]

Recent advancements in artificial intelligence (AI) and the emergence of AI‐based social applications in the market have propelled research on the possibility of consumers developing relationships with AI. Motivated by the diversity of approaches and inconsistent findings in this emerging research stream, this systematic literature review analyzes 37 peer‐reviewed empirical studies focusing on human–AI relationships published between 2018 and 2023. We identify three major theoretical domains (social psychology, communication and media studies, and human–machine interactions) as foundations for conceptual development, and detail theories used in the reviewed papers. Given the radically new nature of social AI innovation, we recommend developing a novel theoretical approach that would synergistically utilize cross‐disciplinary literature. Analysis of the methodology indicates that quantitative studies dominate this research stream, while qualitative, longitudinal, and mixed‐method approaches are used infrequently. Examination of research models and variables used in the studies suggests the need to reconceptualize factors and processes of human–AI relationship, such as agency, autonomy, authenticity, reciprocity, and empathy, to better correspond to the social AI context. Based on our analysis, we propose an integrative conceptual framework and offer directions for future research that incorporate the need to develop a comprehensive theory of human ‐ AI relationships, explore the nomological networks of its key constructs, and implement methodological variety and triangulation. [ABSTRACT FROM AUTHOR]

Intelligent learning systems provide relevant learning materials to students based on their individual pedagogical needs and preferences. However, providing personalized learning objects based on learners’ preferences, such as learning styles which are particularly important for the recommendation of learning objects, re-mains a challenge. Recommending the most appropriate learning objects for learners has always been a challenge in the field of e-learning. This challenge has driven educators and researchers to implement new ideas to help learners improve their learning experience and knowledge. New solutions use artificial intelligence (AI) techniques such as machine learning (ML) and natural language processing (NLP). In this paper, we propose and develop a new personalization approach for recommendation that implements the adaptation of learning objects according to the learners’ learning style mainly focused on the use of a chatbot, named LearningPartnerBot, which will be integrated into the Moodle platform. We use the Felder-Silverman Learning Styles Model to determine learners’ learning styles in order to recommend learning objects, and also to overcome the cold start problem. A chatbot is an automated communication tool that attempts to imitate a conversation by detecting the intentions of its user. The proposed LearningPartnerBot should be able to answer learners’ questions in real time and provide a relevant set of suggestions according to their needs. [ABSTRACT FROM AUTHOR]