Search

With the advancement of artificial intelligence, conversational agents are now capable of displaying intelligent and emotionally empathetic responses, which are essential for the continued use of AI-based agents. However, there is a scarcity of formal measures that can comprehensively evaluate how well they react to the user's emotional needs. The objective of this research is to develop and validate a set of new measures, collectively called the agent empathic reactivity index (AERI), an adaptation of the interpersonal reactivity index (IRI) developed for the human-human relationship evaluation to the human-agent interaction context. By rigorously following the measure development procedures suggested by prior research, four dimensions of AERI measures of empathic concern, perspective-taking, fantasy, and personal distress were developed. Multiple pilot tests and surveys involving various conversational agents were conducted to validate the four AERI measures. The study results show that the new measures have strong psychometric properties and nomological validity. [ABSTRACT FROM AUTHOR]

ABSTRACT Chatbots have emerged as ubiquitous tools for enhancing user interaction across various platforms, from customer service to personal assistance. They are computer programs that simulate and process human conversation, either written, spoken or both. However, developing efficient chatbots remains a challenge, primarily due to the intricate nature of critical components of chatbots like natural language understanding (NLU) requiring a subscription from intent recognition providers like Dialogflow and Amazon Lex. This makes chatbots closely linked to NLP services and can be locked in. Recently, various research studies have provided solutions to reduce the workload of developers and designers. These approaches have proposed model‐driven development via domain‐specific languages (DSLs), which make the chatbot development process more accessible and more automated. This advancement aims to enhance effectiveness in chatbot development by leveraging DSLs. This study aims to provide a comprehensive overview of DSLs for developing chatbots, with the first contribution comprising various research topics, tools, approaches, and technologies employed to implement DSLs. Second, this work aims to assess and contrast the primary DSLs currently available for chatbot development, focusing on presenting the key elements used in constructing these DSLs. Third, this study identifies the challenges and limitations of using DSLs in chatbot development. [ABSTRACT FROM AUTHOR]

Background: Digital innovations can reduce the global burden of depression by facilitating timely and scalable interventions. In recent years, the number of commercial Digital Health Interventions for Depression (DHIDs) has been on the rise. However, there is limited knowledge on their content and underpinning scientific evidence. This study aimed to: (i) identify the top-funded companies offering DHIDs and (ii) provide an overview of their interventions, including scientific evidence, psychotherapeutic approaches and use of novel technologies. Methods: A systematic search was conducted using two venture capital databases to identify the top-30 funded companies offering DHIDs. In addition, studies related to the DHIDs' were identified via academic databases and hand-searching. The methodological quality of the publications was evaluated using the Mixed Methods Appraisal Tool. Results: The top-30 funded companies offering DHIDs received a total funding of 2,592 million USD. Less than half of the companies produced any scientific research associated with their DHIDs, with a total of 83 publications identified. Twenty-five publications were randomised control trials, of which 15 reported moderate-to-large effects in reducing depression symptoms. Regarding novel technologies, few DHIDs incorporated the use of conversational agents or low-burden sensing technologies. Conclusions: Funding received by top-funded companies was not related to the amount of scientific evidence provided on their DHIDs. There was a strong variation in the quantity of evidence produced and an overall need for more rigorous effectiveness trials. Few DHIDs used automated approaches such as conversational agents, limiting their scalability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Few individuals with eating disorders (EDs) receive treatment. Innovations are needed to identify individuals with EDs and address care barriers. We developed a chatbot for promoting services uptake that could be paired with online screening. However, it is not yet known which components drive effects. This study estimated individual and combined contributions of four chatbot components on mental health services use (primary), chatbot helpfulness, and attitudes toward changing eating/shape/weight concerns ("change attitudes," with higher scores indicating greater importance/readiness). Method s : Two hundred five individuals screening with an ED but not in treatment were randomized in an optimization randomized controlled trial to receive up to four chatbot components: psychoeducation, motivational interviewing, personalized service recommendations, and repeated administration (follow‐up check‐ins/reminders). Assessments were at baseline and 2, 6, and 14 weeks. Results: Participants who received repeated administration were more likely to report mental health services use, with no significant effects of other components on services use. Repeated administration slowed the decline in change attitudes participants experienced over time. Participants who received motivational interviewing found the chatbot more helpful, but this component was also associated with larger declines in change attitudes. Participants who received personalized recommendations found the chatbot more helpful, and receiving this component on its own was associated with the most favorable change attitude time trend. Psychoeducation showed no effects. Discussion: Results indicated important effects of components on outcomes; findings will be used to finalize decision making about the optimized intervention package. The chatbot shows high potential for addressing the treatment gap for EDs. [ABSTRACT FROM AUTHOR]

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]

The rising surge of work-related stress is particularly severe among small-to-medium-sized enterprise (SME) owner-managers, and has been linked to an array of deleterious consequences, such as burnout, venture failure, and suicide. Sadly, the majority of available stress management interventions appear to be ill-suited to owner-managers. Prior work, however, suggests that mobile phone-based messaging conversational agents (CAs) may hold promise for delivering mental health interventions to underserved groups such as the self-employed. Furthermore, recent research with SMEs finds that altering one's stress mindset – beliefs about the extent to which stress might be enhancing or debilitating can change one's responses to stress. Against this backdrop, the present study assessed the effectiveness and acceptability of a first-of-its-kind mobile messaging-based conversational agent-led stress mindset intervention (mCASMI) for Aotearoa New Zealand SME owner-managers. The mCASMI was delivered over 4-days via WhatsApp Messenger. The results confirmed that the mCASMI was successful in altering participants' mindsets about stress, in conjunction with self-reported improvements in productivity and work performance. The owner-managers who received the intervention were engaged, adherent, and reported a high degree of rapport with the CA. Though preliminary, these findings extend the state-of-the-art and suggest progressing with a larger-scale feasibility study. [ABSTRACT FROM AUTHOR]

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 (X2 (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. [ABSTRACT FROM AUTHOR]

Chatbots offer customers access to personalised services and reduce costs for organisations. While some customers initially resisted interacting with chatbots, the COVID‐19 outbreak caused them to reconsider. Motivated by this observation, we explore how disruptive situations, such as the COVID‐19 outbreak, stimulate customers' willingness to interact with chatbots. Drawing on the theory of consumption values, we employed interviews to identify emotional, epistemic, functional, and social values that potentially shape willingness to interact with chatbots. Findings point to six values and suggest that disruptive situations stimulate how the values influence WTI with chatbots. Following theoretical insights that values collectively contribute to behaviour, we set up a scenario‐based study and employed a fuzzy set qualitative comparative analysis. We show that customers who experience all values are willing to interact with chatbots, and those who experience none are not, irrespective of disruptive situations. We show that disruptive situations stimulate the willingness to interact with chatbots among customers with configurations of values that would otherwise not have been sufficient. We complement the picture of relevant values for technology interaction by highlighting the epistemic value of curiosity as an important driver of willingness to interact with chatbots. In doing so, we offer a configurational perspective that explains how disruptive situations stimulate technology interaction. [ABSTRACT FROM AUTHOR]

Shared Automated Vehicles (SAVs) promise to make automated mobility accessible to a wide range of people while reducing air pollution and improving traffic flow. In the future, these vehicles will operate with no human driver on board, which poses several challenges that might differ depending on the cultural context and make one-fits-all solutions demanding. A promising substitute for the driver could be Digital Companions (DCs), i.e. conversational agents presented on a screen inside the vehicles. We conducted interviews with Colombian participants and workshops with German and Korean participants and derived two design concepts of DCs as an alternative for the human driver on SAVs: a human-like and a robot-like. We compared these two concepts to a baseline without companion using a scenario-based online questionnaire with participants from Colombia (N = 57), Germany (N = 50), and Korea (N = 29) measuring anxiety, security, trust, risk, control, threat, and user experience. In comparison with the baseline, both DCs are statistically significantly perceived as more positively. While we found a preference for the human-like DC among all participants, this preference is higher among Colombians while Koreans show the highest openness towards the robot-like DC. [ABSTRACT FROM AUTHOR]

In the past ten years, chatbot development has matured to become one of the most well-distinguished outcomes of artificial intelligence. Despite some criticism, Bing AI, ChatGPT and other natural language processing (NLP) products of similar nature are becoming more popular. The creation of chatbots can close several gaps in geographic information retrieval as well. This research introduces and successfully implements, for the first time, a model for integrating task-oriented chatbots into geoportals, with the goal of easing user requests, improving access to geospatial services, and fostering human-computer interactions (HCI). Additionally, it presents a novel recommendation solution for matching the most appropriate volunteer to the user’s geospatial needs based on expertise similarity, semantic similarity, and community feedback. The three categories of finding map services, discovering geoprocessing services, and volunteer expert recommendations were shown to be the most significant geoportal bot intents. Depending on the requirement, each intent additionally includes various entities such as time, place, description, skill, etc. The notion of deep transfer learning (DTL) was then put into practice by customizing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for our particular aim and creating a task-oriented conversational agent. According to the results, effective intent classification and entity recognition in the geospatial domain could arise from this approach. We performed the training process with 200 sample data, 20% of which were utilized in a stratified manner for testing, and we obtained f1-scores of at least 0.75. Finally, a pilot Geoportal Chabot that combines crowdsourcing and conversational agents’ approaches was put into use and tested with success. In keeping with SDI technical purposes, the system might direct users to common geospatial web services, namely WMS and WPS, in addition to including natural language understanding (NLU) and natural language generation (NLG) capabilities. Result of user-centered evaluation indicated that the integration of a chatbot significantly reduces the average time required to access geospatial data and processing services by more than 50%. Notably, this effect is even more pronounced when locating an expert, with a fivefold decrease in the time required. Finally, overall user satisfaction rose from 86% to 94%.

Conversational Agents (CAs) are becoming part of our everyday lives. About 10 percent of users display aggressive behavior toward CAs, such as swearing at them when they produce errors. We conducted two online experiments to understand user aggression toward CAs better. In the first experiment, 175 participants used either a humanlike CA or a non-humanlike CA. Both CAs worked without errors, and we observed no increased frustration or user aggression. The second experiment (with 201 participants) was the focus of this study; in it, both CAs produce a series of errors. The results show that frustration with errors drives aggression, and users with higher impulsivity are more likely to become aggressive when frustrated. The results also suggest that there are three pathways by which perceived humanness influences users' aggression to CAs. First, perceived humanness directly increases the frustration with the CA when it produces errors. Second, perceived humanness increases service satisfaction which in turn reduces frustration. Third, perceived humanness influences the nature of aggression when users become frustrated (i.e., users are less likely to use highly offensive words with a more humanlike CA). Our research contributes to our theoretical understanding of the role of anthropomorphism in the interaction with machines, showing that designing a CA to be more humanlike is a double-edged sword—both increasing and decreasing the frustration that leads to aggression—and also a means to reduce the most severe aggression. [ABSTRACT FROM AUTHOR]

AbstractOver the past three decades the field of pedagogical agents (PAs) has seen significant growth, but no review has specifically focused on the design and use of PAs for K-12 students, despite the fact that an early meta-analysis showed that they receive the most benefits from learning from or with PAs. Our systematic search revealed 112 studies that met the inclusion criteria and were analyzed. Our findings revealed a plethora of studies investigating the use of PAs with K-12 populations and a considerable number of longitudinal studies, both of which the field has long stated did not exist in significant numbers. Our findings contrast long-held findings in the field, further support others, and highlight areas where further experimentation and research synthesis are needed. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technologies have increasingly become vital in our everyday lives. Education is one of the most visible domains in which these technologies are being used. Conversational Agents (CAs) are among the most prominent AI systems for assisting teaching and learning processes. Their integration into an e-learning system can provide replies suited to each learner's specific needs, allowing them to study at their own pace. In this paper, based on recent advancements in Natural Language Processing (NLP) and deep learning techniques, we present an experimental implementation of an educational chatbot intended to instruct secondary school learners Logo, an educational programming language. The related chatbot was implemented and evaluated in Moroccan public schools with the support of teachers from the Regional Center for Education and Training Professions of Souss Massa. The experiments included 109 students grouped into three separate classes. One is a control class group that uses a traditional approach, while the other two are experimental groups that employ digital content and the chatbot-based method. Preliminary findings indicate that employing chatbots can greatly enhance student learning experiences by allowing them to study at their own speed with less stress, saving them time, and keeping them motivated. Furthermore, integrating these AI systems into a smart classroom will not only create a supportive environment by encouraging good interactions with students, it will also allow learners to be more engaged and achieve better academic objectives. [ABSTRACT FROM AUTHOR]

AbstractThere is a rising interest in chatbots dedicated to enhancing sexual health. However, there is limited research on the effectiveness of these chatbots, and the current literature lacks sufficient exploration of gaps and patterns in this field. In this review, we provided an overview of the state-of-the-art research conducted on sexual health chatbots, with the goal of identifying prevalent trends, design patterns, and features. In addition, we investigated existing research gaps, challenges, and shortcomings in the landscape of sexual health chatbots. Further, we proposed potential enhancements and directions for future research and development to create more effective chatbots in this field. A systematic search and screening of the literature from the past decade (2013–2023), extracted from seven databases, yielded a total of 1040 studies, out of which 29 articles were included in the final review following screening. The findings suggest that chatbots are usable and effective tools in sexual health education, persuasion, and assistance that are appreciated for their confidentiality, efficiency, and 24/7 availability. However, their performance is hindered by limitations such as restricted scope of knowledge and challenges in understanding user inputs. Additionally, constraints such as text-only input/output modalities and a predominant reliance on the English language limit their accessibility and acceptability. There is also a crucial need for more research in low-income or lower-middle-income countries, where individuals require increased sexual health education and support. [ABSTRACT FROM AUTHOR]

Existing work on task-oriented dialog systems generally assumes that the interaction of users with the system is restricted to the information stored in a closed data schema. However, in practice users may ask 'out-of-schema' questions, that is, questions that the system cannot answer, because the information does not exist in the schema. Failure to answer these questions may lead the users to drop out of the chat before reaching the success state (e.g. reserving a restaurant). A key challenge is that the number of these questions may be too high for a domain expert to answer them all. We formulate the problem of out-of-schema question detection and selection that identifies the most critical out-of-schema questions to answer, in order to maximize the expected success rate of the system. We propose a two-stage pipeline to solve the problem. In the first stage, we propose a novel in-context learning (ICL) approach to detect out-of-schema questions. In the second stage, we propose two algorithms for out-of-schema question selection (OQS): a naive approach that chooses a question based on its frequency in the dropped-out conversations, and a probabilistic approach that represents each conversation as a Markov chain and a question is picked based on its overall benefit. We propose and publish two new datasets for the problem, as existing datasets do not contain out-of-schema questions or user drop-outs. Our quantitative and simulation-based experimental analyses on these datasets measure how our methods can effectively identify out-of-schema questions and positively impact the success rate of the system. [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 use of conversational agents (e.g., chatbots) to simplify or aid consumers' purchase decisions is on the rise. In designing those conversational agents, a key question for companies is whether and when it is advisable to enable voice-based rather than text-based interactions. Addressing this question, this study finds that matching consumers' communication modality with product type (speaking about hedonic products; writing about utilitarian products) shapes consumers' choice and increases choice satisfaction. Specifically, speaking fosters a feeling-based verbalizing focus, while writing triggers a reason-based focus. When this focus matches consumers' mindset in evaluating the product type, preference fluency increases, thereby enhancing choice satisfaction. Accordingly, the authors provide insights into managing interactions with conversational agents more effectively to aid decision-making processes and increase choice satisfaction. Finally, they show that communication modality can serve as a strategic tool for low-equity brands to better compete with high-equity brands. [ABSTRACT FROM AUTHOR]

Enabling patients to actively document their health information significantly improves understanding of how therapies work, disease progression, and overall life quality affects for those living with chronic disorders such as hematologic malignancies. Advancements in artificial intelligence, particularly in areas such as natural language processing and speech recognition, have resulted in the development of interactive tools tailored for healthcare. This paper introduces an innovative conversational agent tailored to the Greek language. The design and deployment of this tool, which incorporates sentiment analysis, aims at gathering detailed family histories and symptom data from individuals diagnosed with hematologic malignancies. Furthermore, we discuss the preliminary findings from a feasibility study assessing the tool's effectiveness. Initial feedback on the user experience suggests a positive reception towards the agent's usability, highlighting its potential to enhance patient engagement in a clinical setting. [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]

Individualized learning support is an essential part of formal educational learning processes. However, in typical large-scale educational settings, resource constraints result in limited interaction among students, teaching assistants, and lecturers. Due to this, learning success in those settings may suffer. Inspired by current technological advances, we transfer the concept of chatbots to formal educational settings to support not only a single task but a full lecture period. Grounded on an expert workshop and prior research, we design a natural language-based digital tutor acting as an intermediary among students, teaching assistants, and lecturers. The aim of the digital tutor is to support learners automated during the lecture period in natural language-based chat conversations. We implement a digital tutor in an iterative design process and evaluate it extensively in a large-scale field setting. The results demonstrate the applicability and beneficial support of introducing digital tutors as intermediaries in formal education. Our study proposes the concept of using digital tutors as intermediaries and documents the development and underlying principles. [ABSTRACT FROM AUTHOR]

Conversational agents (CAs) have come a long way from their first appearance in the 1960s to today's generative models. Continuous technological advancements such as statistical computing and large language models allow for an increasingly natural and effortless interaction, as well as domain-agnostic deployment opportunities. Ultimately, this evolution begs multiple questions: How have technical capabilities developed? How is the nature of work changed through humans' interaction with conversational agents? How has research framed dominant perceptions and depictions of such agents? And what is the path forward? To address these questions, we conducted a bibliometric study including over 5000 research articles on CAs. Based on a systematic analysis of keywords, topics, and author networks, we derive "five waves of CA research" that describe the past, present, and potential future of research on CAs. Our results highlight fundamental technical evolutions and theoretical paradigms in CA research. Therefore, we discuss the moderating role of big technologies, and novel technological advancements like OpenAI GPT or BLOOM NLU that mark the next frontier of CA research. We contribute to theory by laying out central research streams in CA research, and offer practical implications by highlighting the design and deployment opportunities of CAs. [ABSTRACT FROM AUTHOR]

Objective: Rehabilitation for adults with traumatic brain injury (TBI) incorporates client-centred goal-setting and motivational support to achieve goals. However, face-to-face rehabilitation is time-limited. New therapy approaches which leverage care are warranted. Conversational agents (CAs) offer a human–computer interface with which a person can converse. This study tested the feasibility, usability and acceptability of using a novel CA – RehabChat – alongside brain injury rehabilitation. Design: Mixed methods, single case design, feasibility pilot trial. Setting: Ambulatory and community brain injury rehabilitation. Participants: Adults with TBI receiving brain injury rehabilitation and clinicians providing this care. Intervention: Following 1:1 training, client–clinician dyads used RehabChat for two weeks alongside usual care. Main measures: Pre-post clinical measures (Motivation for Traumatic Brain Injury Rehabilitation Questionnaire, Rehabilitation Therapy Engagement Scale, Brain Injury Rehabilitation Trust Motivation Questionnaire-Relative, Brain Injury Rehabilitation Trust Motivation Questionnaire-Self) repeated measures (Hospital Anxiety and Depression Scale, researcher-developed wellbeing screening questions); and post-intervention (System Usability Scale (SUS), semi-structured 1:1 interview). Results: Six participants (two clients and four clinicians) completed training. Two client–clinician dyads completed the intervention. Two other clinicians used RehabChat in a mock client–clinician session. SUS scores indicated good usability. Client well-being did not deteriorate. No adverse events were experienced. Interviews indicated RehabChat was feasible, acceptable and easy to use; and supported motivation, goal-setting and completing practice activities. Conclusions: RehabChat was feasible and acceptable to use alongside usual ambulatory and community brain injury rehabilitation, had good usability and supported client needs. Further testing of RehabChat with a larger cohort for longer duration is warranted. [ABSTRACT FROM AUTHOR]

The innovations in smart cities are crucial to integrating green technologies, including in the tourism sector, which can signif-cantly impact the sustainability of destinations. Conversational agents (CAs) are considered green technologies because they ofer solutions to reduce the negative impact of human activity on the environment while enhancing the customer experience through automated interactions. By combining these advances, sustainable tourism destinations can effectively leverage green technologies such as CAs to ofer quality attractions while preserving the natural environment. In this work, we present an analysis study and a comprehensive comparison of CA development tools. As a case study, we explore the power of Bot Framework Composer, a visual development tool from Microsoft, to create a CA that displays attractions in the Deraa Taflalet region as part of smart tourism. However, our goal goes beyond the creation of a simple CA. We also introduce a metamodel for the CA, identifying the fundamental concepts of Bot Framework Composer. This metamodel aims to establish a unified basis for creating a Domain-Specific Language (DSL) for designing CA in a consistent and portable way. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

IntroductionRadiologists frequently lack direct patient contact due to time constraints. Digital medical interview assistants aim to facilitate the collection of health information. In this paper, we propose leveraging conversational agents to realize a medical interview assistant to facilitate medical history taking, while at the same time offering patients the opportunity to ask questions on the examination.MethodsMIA, the digital medical interview assistant, was developed using a person-based design approach, involving patient opinions and expert knowledge during the design and development with a specific use case in collecting information before a mammography examination. MIA consists of two modules: the interview module and the question answering module (Q&A). To ensure interoperability with clinical information systems, we use HL7 FHIR to store and exchange the results collected by MIA during the patient interaction. The system was evaluated according to an existing evaluation framework that covers a broad range of aspects related to the technical quality of a conversational agent including usability, but also accessibility and security.ResultsThirty-six patients recruited from two Swiss hospitals (Lindenhof group and Inselspital, Bern) and two patient organizations conducted the usability test. MIA was favorably received by the participants, who particularly noted the clarity of communication. However, there is room for improvement in the perceived quality of the conversation, the information provided, and the protection of privacy. The Q&A module achieved a precision of 0.51, a recall of 0.87 and an F-Score of 0.64 based on 114 questions asked by the participants. Security and accessibility also require improvements.ConclusionThe applied person-based process described in this paper can provide best practices for future development of medical interview assistants. The application of a standardized evaluation framework helped in saving time and ensures comparability of results.

Background: Obesity is a global health issue affecting over 2 billion people. Mobile health apps, specifically nutrition apps, have been identified as promising solutions to combat obesity. However, research on adherence to nutrition apps is scarce, especially for publicly available apps without monetary incentives and personal onboarding. Understanding factors associated with adherence is essential to improve the efficacy of these apps. This study aims to identify such factors by analyzing a large dataset of a free and publicly available app (“MySwissFoodPyramid”) that promotes healthy eating through dietary self-monitoring and nutrition literacy delivered via a conversational agent. Methods: A retrospective analysis was conducted on 19,805 users who used the app for at least two days between November 2018 and May 2022. Adherence was defined as completing a food diary by tracking dietary intake over a suggested period of three days. Users who finished multiple diaries were considered long-term adherent. The associations between the day and time of installation, tutorial use, reminder use, and conversational agent choice were examined regarding adherence, long-term adherence, and the number of completed diaries. Results: Overall, 66.8% of included users were adherent, and 8.5% were long-term adherent. Users who started the intervention during the day (5 a.m.–7 p.m.) were more likely to be adherent and completed more diaries. Starting to use the intervention between Sunday and Wednesday was associated with better adherence and a higher number of completed diaries. Users who chose the female conversational agent were more likely to be adherent, long-term adherent, and completed more diaries. Users who skipped the tutorial were less adherent and completed fewer diaries. Users who set a follow-up reminder were more likely to be long-term adherent and completed more diaries. Conclusions: This study demonstrates the potential of digital health interventions to achieve comparably high adherence rates, even without monetary incentives or human-delivered support. It also reveals factors associated with adherence highlighting the importance of app tutorials, customizable reminders, tailored content, and the date and time of user onboarding for improving adherence to mHealth apps. Ultimately, these findings may help improve the effectiveness of digital health interventions in promoting healthy behaviors.

Artificial intelligence chatbots recently caused a stir in the world by promising to transform education systems in a multitude of ways. To analyze pertinent publications in the SSCI journals, a systematic review was conducted for this study, which investigates the trends of chatbots in education studies, with a focus on English speaking skills. Despite the increasing use of AI-powered chatbots in education, limited research has explored how to develop the merits of AI-powered chatbots in English-speaking teaching or learning. The empirical evidence of chatbot applications from 24 research studies conducted in an English-speaking learning environment and published between 2017 and 2023 was examined in this systematic review. The topic of study on chatbots in English language-speaking practice skills was determined to be in its early stages, which suggests that there is a lot of space for performing pertinent research to support creative English-speaking learning and work with chatbots to enhance learning results. The findings suggest that the AI chatbot learning approach for English-speaking proficiency was intended to speed up the English learning process and assist students in meeting the goals or results of the courses. Also, some benefits (e.g. alleviating speaking anxiety and improving speaking pronunciation) were highlighted in each area. Specifically, the study provides insights into the effectiveness of AI chatbots in enhancing students’ English-speaking learning outcomes, confidence, engagement and motivation. This study concludes with recommendations for in-service English teachers, chatbot designers and chatbot researchers in the future.