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This discussion article examines the potential integration of Generative Artificial Intelligence (Gen-AI), including advanced Large-Language Models like the popular platform ChatGPT into subject English education. Following the significant public and academic attention in response to these technologies through 2023, this paper considers the transformative potential and challenges posed by Gen-AI in educational settings. Central to the discussion is the exploration of how English teachers can leverage Gen-AI to enrich student learning beyond the obvious domain of writing skills. Instead, the article foregrounds the necessity for students' understanding of Gen-AI as an essential component of digital literacy. While acknowledging ethical concerns such as plagiarism, equity, and access, the paper presents an argument for the productive use of Gen-AI in the classroom to augment reading, viewing, and interpretation lessons. Avoiding an evangelical or dystopian view of AI, this discussion piece explores the time-critical and urgent issue of how, when, and why English can engage with the technology. [ABSTRACT FROM AUTHOR]

The recent popularity of ChatGPT and artificial intelligence chatbots presents both challenges and opportunities for incorporating this modern technology in the classroom. This paper introduces an activity that uses ChatGPT to help students practice their role playing sales skills. The benefits of using this AI chatbot for role play training include allowing students to practice when it's convenient and have the ability to react to a chatbot taking on personas of different buyer types. Survey results demonstrate the effectiveness of this training method for both role play training and general familiarity with ChatGPT. [ABSTRACT FROM AUTHOR]

This paper examines the venture capital-driven process of making intangible assets in platform start-up firms. By examining the case study of the rise and fall of a venture capital-backed 'unicorn' firm developing a digital health platform, this paper argues that the process of real valorization of capital invested in platform start-up firms involves the making of algorithmic systems and data as intangible assets as well as the experimentation with strategies of exploitation and appropriation, which are inherently linked to the future-oriented financial valorization process of equity shares since unprofitable start-up firms continuously require outside capital to expand operations. While the fetish of 'artificial intelligence' posing the firm's chatbot for self-diagnosis as an intelligent 'doctor in your pocket' plays an important role in financial valorization, it is the failed real valorization process in making profits that ultimately leads to the platform start-up's financial collapse. The conceptual contribution of the paper centres on the contradictory nature of assetization processes which sheds light on how class domination operates in and through venture capital-driven accumulation. [ABSTRACT FROM AUTHOR]

In view of the emerging spread of AI in computing systems of everyday use, it is becoming apparent that AI and HCI can mutually benefit from each other in fruitful cooperation. Overall, the article provides guidance for dealing with human-AI interaction during the decision-making process and sheds light on how explainable AI can increase users' acceptance of such systems. Explainable AI: The effect of contradictory decisions and explanations on users' acceptance of AI systems. The discussion around the rapid evolution and omnipresence of Artificial Intelligence (AI) is already rich, and AI is expected to be integrated into most aspects of everyday life. [Extracted from the article]

The paper explores how Generative AI intersects with Competition Law, focusing on Foundation Models (FMs) and Large Language Models (LLMs). It examines industry dynamics and identifies key competition issues like entry barriers, tying, leveraging, and acquisitions. It highlights the supply chain's importance and looks at how FMs are integrated into search software, chatbots, and productivity tools, particularly noting entry barriers such as computing power and data collection. It suggests that FMs might require new approaches to market delineation, possibly creating a separate relevant market for data. The paper also discusses various cases pertaining to tying and leveraging and highlights the difficulty in proving tying due to the blurred lines between traditional search engines and AI chatbots. It illustrates how competition assessments for acquisitions may require changes due to data being a highly flexible commodity for the industry. The paper concludes by calling for increased scrutiny and regulation for the industry. [ABSTRACT FROM AUTHOR]

There is considerable uncertainty about the role that Artificial Intelligence (AI) might play in Environmental Impact Assessment (EIA), including into research. AI large language model (LLM) chatbots have the potential to increase the efficiency of EIA research, but their outputs can create concerns. This paper investigates the potential time savings achievable using LLM chatbots to undertake a critical review of literature focussing on the use of AI in EIA. Using a combination of ChatGPT and Elicit, literature was reviewed to identify 12 key issues associated with the use of AI in EIA and this paper was prepared in three and a half days from initial conception. A protocol is developed to assist researchers in fact checking evidence delivered through Elicit (or other machine learning tools) which serves as a novel outcome of this research. Using comments from three peer reviewers allowed some more objective reflection on the credibility of the LLM chatbot-derived output, on the appropriateness of the time savings, and on the future research needed on the application of LLM chatbots in this context. [ABSTRACT FROM AUTHOR]

AbstractThis column considers challenges and opportunities AI generative chatbots pose to proprietary library databases. It briefly reviews evolution from paper library indices to Web-based databases then reasons why current chatbots pose greater challenges to database relevancy than when Web engines first emerged. Library databases and free chatbots are compared in terms of identifying information needs, and locating, evaluating, and using information. Q/A with EBSCO are collected on challenges and opportunities chatbots provide them. Discussion centering around questions intended for database vendors ensues, along with a conclusion on potential information literacy benefits if library databases adopt chatbot search platforms. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) poses new challenges and opportunities for hospitality and tourism (H&T) education. This study explores how ChatGPT, a chatbot powered by a generative pre-trained transformer model, can affect H&T curriculum design and delivery. Using secondary data from academic articles, news reports, and official documents, the paper applies curriculum theory and stakeholder theory to examine the competencies, expectations, and interests of H&T students, educators, and industry practitioners regarding ChatGPT. This study reveals that ChatGPT can enhance learning outcomes and experiences, improve educational equity and efficiency, and raise ethical, technical, and pedagogical issues for H&T education. This research note also offers insights and recommendations for H&T educators to integrate ChatGPT into their teaching practices and prepare students for research work and careers that incorporate generative AI. Moreover, the study contributes to the literature on H&T education by examining the role and impact of ChatGPT as an innovative and emerging technology. [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]

The introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns and issues, especially related to the ethics of using these tools in scientific writing and publications. In addition, there has been discussions about whether ChatGPT is trustworthy, effective, and useful in increasing researchers' productivity. Therefore, in this paper, we evaluate ChatGPT's performance on tasks related to bibliometric analysis, by comparing the output provided by the chatbot with a recently conducted bibliometric study on the same topic. The findings show that there are large discrepancies and ChatGPT's trustworthiness is low in this particular area. Therefore, researchers should exercise caution when using ChatGPT as a tool in bibliometric studies. [ABSTRACT FROM AUTHOR]

Today, with the rapid advancement of technology, Artificial Intelligence (AI)-powered chatbots have become omnipresent across sectors like e-commerce, travel, hospitality, banking, EdTech, etc.; but they still haven't been predominantly adopted by academic libraries. This paper reports on a qualitative study aimed to explore the perceived risks and challenges behind the adoption of chatbots in libraries and how chatbots could help deliver superior experience to all library users as well as stakeholders. We conducted interviews with multiple stakeholders of academic libraries: the library staff, doctoral students and the faculty, to understand their perspectives on the adoption of Chatbots. We used CAQDAS NVIVO 12 content analysis software to analyse the interview transcripts. Our findings suggest that the majority of the stakeholders we interviewed favour the adoption of chatbots, as they believe that integrating chatbot technology with an existing library information system could deliver diverse services, which in turn would help in research and scholarly communication. However, 'perceived risk' with respect to employing chatbots among stakeholders was high. Also, the stakeholders raised some serious concerns with respect to privacy intrusion by chatbots, and with respect to chatbots' comprehension of task complexity, which are issues that must be addressed by chatbot developers while designing them, specifically for libraries. [ABSTRACT FROM AUTHOR]

In this paper, I examine the historiographical capabilities of ChatGPT, a state-of-the-art artificial intelligence (AI) chatbot released on 30 November 2022, by OpenAI corporation. I begin by outlining a concise history of AI, highlighting the key innovations that have culminated in the development of advanced applications such as ChatGPT. Subsequently, I present an analysis of my interactions with the chatbot between February and May 2023, focusing on a long 'interview' conducted on the theory and methodology of historical science. My preliminary findings suggest that while ChatGPT holds significant potential for aiding historians in their research, the prospect of AI-driven history production remains distant and unlikely to materialize soon. [ABSTRACT FROM AUTHOR]

A library's website is the virtual front door for many of our users. Libraries offer online assistance in several ways, including a variety of options to request help. Live chat and email services are not new, but the recent development of chatbots provides opportunities to rethink and enhance online support. While some chatbots or virtual assistants generate automatic and custom responses to user input, this paper explores the implementation of a rules-based chatbot, where all responses have been input and configured by library staff. Details are provided to describe the process before, during, and after the initial implementation with data and observations from the pilot in mid-2023. [ABSTRACT FROM AUTHOR]

Copyright of Service Industries Journal is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explores the trends of chatbots in education studies by conducting a literature review to analyze relevant papers published in the Social Science Citation Index (SSCI) journals by searching the Web of Science (WoS) database. From the analysis results, it was found that the United States, Taiwan and Hong Kong are the top three contributing countries or regions. In addition, most studies adopted quantitative methods in their research design, such as ANOVA (Analysis of variance), descriptive statistics, t test, and correlation analysis. ANCOVA (Analysis of covariance) was the most frequently adopted approach for comparing the performances or perceptions of different groups of students. From the analysis results, the greatest proportion of studies adopted guided learning, followed by no learning activities. It was determined that the studies related to chatbots in education are still in an early stage since there are few empirical studies investigating the use of effective learning designs or learning strategies with chatbots. This implies much room for conducting relevant research to drive innovative teaching in terms of improving the learning process and learning outcomes. Finally, we highlight the research gaps and suggest several directions for future research based on the findings in the present study. [ABSTRACT FROM AUTHOR]

This document is a correction notice for a set of articles published in the journal Public Management Review. The articles were written for a special issue titled "Implementing AI in the Public Sector" and were mistakenly published in regular issues. The correction provides the correct order of the articles and updates the references in the introduction for clarity. The complete set of papers can be found on the journal's collection page. [Extracted from the article]

Chatbots and other artificial-intelligence applications that mimic human conversation or writing have rapidly become some of the most popular tech applications of all time. Expert analysis and media coverage of the risks and benefits of AI have paid scant attention to how chatbots might affect public health at a time when depression, suicide, anxiety, and mental illness are epidemic in the United States, particularly among children and young adults. Many experts have pointed to a correlation between declining mental health and increasing online engagement. Generative AI's potential to transform education, the job market, and social interactions could come at the expense of humanity's own mental faculties, creativity, and social skills. Chatbots—which are prone to errors and fabrications—could also make it more difficult for humans to tell fact from fiction. But to the extent that mental health experts and the healthcare industry are interested in AI, it's mostly viewed as a promising tool for identifying and treating mental health issues, rather than a potential threat to mental health. [ABSTRACT FROM AUTHOR]

Since ChatGPT's release in November 2022, artificial intelligence has come into the spotlight. Inspiring both fascination and fear, chatbots have stirred debates among researchers, developers, and policy makers. The concerns range from concrete and tangible ones—which include replication of existing biases and discrimination at scale, harvesting personal data, and spreading misinformation—to more existential fears that their development will lead to machines with human-like cognitive abilities. Understanding how chatbots work and the human labor and data involved can better help evaluate the validity of concerns surrounding these systems, which although innovative, are hardly the stuff of science fiction. [ABSTRACT FROM AUTHOR]

Driven by the need to provide continuous, timely, and efficient customer service, firms are constantly experimenting with emerging technological solutions. In recent times firms have shown an increased interest in designing and implementing artificial intelligence (AI)-based interactional technologies, such as conversational AI agents and chatbots, that obviate the need for having human service agents for the provision of customer service. However, the business impact of conversational AI is contingent on customers using and adequately engaging with these tools. This engagement depends, in turn, on conversational AI's similarity, or likeness to the human beings it is intended to replace. Businesses therefore need to understand what human-like characteristics and competencies should be embedded in customer-facing conversational AI agents to facilitate smooth user interaction. This focus on "human-likeness" for facilitating user engagement in the case of conversational AI agents is in sharp contrast to most prior information systems (IS) user engagement research, which is predicated on the "instrumental value" of information technology (IT). Grounding our work in the individual human competency and media naturalness literatures, we theorize the key role of human-like interactional competencies in conversational AI agents—specifically, cognitive, relational, and emotional competencies—in facilitating user engagement. We also hypothesize the mediating role of user trust in these relationships. Following a sequential mixed methods approach, we use a quantitative two-wave, survey-based study to test our model. We then examine the results in light of findings from qualitative follow-up interviews with a sampled set of conversational AI users. Together, the results offer a nuanced understanding of desirable human-like competencies in conversational AI agents and the salient role of user trust in fostering user engagement with them. We also discuss the implications of our study for research and practice. [ABSTRACT FROM AUTHOR]

Historically, the majority of digital assistants, including chatbots, have been assigned names, voices, visual representations, and even "personalities" that are stereotypically feminine and reflect patriarchal ideology. This cross-sectional descriptive study of chatbots associated with large academic libraries in the United States found that there are few extant library chatbots, and in a major departure from trends, there are even fewer that are gendered. This is promising, in that it signals—whether intentionally or not—that the practices of creators and adopters are countering entrenched tendencies to typecast digital assistants as women, which may signal more feminist and gender-inclusive technology design to come. [ABSTRACT FROM AUTHOR]

Omni-channel marketing is an enhanced cross-channel business model involving shared data that allows enterprises to enhance and facilitate customer experience. Omni-channel opportunities shape retail business and shopper behaviours by coordinating data across all channel platforms while enabling their simultaneous use. Artificial intelligence (AI) has played an increasingly critical role in marketing analysis. With the proper training, AI can predict consumer preferences and provide recommendations based on historical data to achieve precision marketing in e-commerce. At present, however, the existent chatbots on many product-ordering platforms lack AI refinement, resulting in the need to ask customers multiple questions before generating a reliable suggestion, yet an effective way to incorporate AI in an omni-channel platform has remained vague. Hence, the aim of this study was to develop an omni-channel chatbot that incorporates iOS, Android, and web components. The chatbot was designed to achieve personalised service and precision marketing using convolutional neural networks (CNNs). A shared kitchen case study demonstrates the advantages of the proposed method, which is transferable to other consumer applications such as clothing selection or personalised services. The number of food offerings and the quality of image classifiers set the research limitations, pointing toward the direction of future research. [ABSTRACT FROM AUTHOR]

Consumers in the United States (n = 290) and Japan (n = 221) participated in 2 × 2 experiments designed to test effects of agent type (online human or chatbot) and communication strategy (targeted or tailored) on attitudes toward messages and agents. In both national samples, tailored messages were more effective in positively affecting attitudes. In addition, perceived humanness was found to mediate the relationships between agent type and attitudes such that when human or artificial agents are perceived as more human in text exchanges, perceived humanness in turn increases attitudes toward both the agents and messages. Hypothesized effects based on individualism-collectivism were not found. Theoretical and practical implications for designing interactive message strategy via conversational chatbots in an era of artificial intelligence (AI) and global communication are discussed. [ABSTRACT FROM AUTHOR]

This study investigated the impact of ChatGPT's recommendation quality and ethical concerns on travelers' acceptance, satisfaction, and perceived trustworthiness. Results showed that when quality and ethical concerns were prominent, acceptance of and satisfaction with ChatGPT's recommendations decreased significantly, and the negative effects were mediated by perceived trustworthiness. This study also identified that message framing containing ChatGPT's errors, and the information types delivered by ChatGPT, acted as moderators of the positive effect of its recommendations. These findings underscore the significance of addressing ethical and quality concerns in using AI (Artificial intelligence)-powered chatbots, with implications for AI acceptance and satisfaction. [ABSTRACT FROM AUTHOR]

Background: Pharmaceutical companies are increasingly leveraging machine learning techniques to optimize healthcare research, drug development, and medical affairs activities. AI (artificial intelligence) tools such as chatbots, virtual digital assistants, and research tools have been explored to varying degrees of maturity in industries such as consumer goods or software technology. However, there continues to be untapped opportunities within the pharmaceutical industry to employ these technologies for enhanced engagement and education with healthcare professionals (HCPs). Pharmacists, situated at the crossroads of clinical sciences and innovation, have the potential to elevate their role and significance within the pharmaceutical industry by developing and leveraging such technologies. Methods: To address this, the python-coded tool, Medical Information (MI) Data Uses For AI Semantic Analysis (MUFASA), utilizes state-of-the-art Sentence Transformer library, clustering, and visualization techniques. MUFASA harnesses unsolicited MI data with AI technology, improving efficiency and providing actionable medical affairs intelligence for targeted content delivery to HCPs. Results: MUFASA optimizes medical affairs activities through its distinctive features: semantic search, cluster analysis, and visualization. Its proficiency in understanding inquiries, as demonstrated through 3D vector mapping and clustering tests, enhances the efficiency of MI and Medical Science Liaison (MSL) case handling. It proves invaluable in training new staff, bolstering response uniformity, and mitigating compliance risks. Leveraging the HDBSCAN algorithm, MUFASA's cluster analysis uncovers deep insights and discerns actionable themes from large inquiry data sets. The visualization graphs, generated from semantic searches, support evidence-based decisions by tracking the effectiveness of initiatives and monitoring trend shifts. Collectively, MUFASA enriches strategic decision-making, cultivates actionable insights, and bolsters healthcare professional engagement. Conclusion: There are numerous opportunities for innovation within the intersection of healthcare and data science. Pharmaceutical manufacturers, with one of their medical affairs responsibilities being the collection of unsolicited inquiries, particularly from HCPs, stand poised to leverage machine learning capabilities to optimize its processes. The abundance of data generated by the growing effort to use it in meaningful ways presents an opportunity for pharmaceutical companies to harness machine learning techniques. [ABSTRACT FROM AUTHOR]

Expungement, which traditionally permitted reintegration into society and the turning over of a new leaf – thus giving a second chance to people who had had the misfortune to fall foul of criminal justice systems – is a legal institution that is currently being undermined. As the cyber-vetting of potential employees is a growing trend, employers readily turn to the internet in order to seek information about job candidates. In this article, the role of expungement in legal systems and society will be examined, as well as the changing reality that is undermining expungement, in order to determine how the issue of expungement should be addressed. In particular, the article discusses the possibility of expungement being incorporated in search engines (including AI chatbots) and in the actual design of AI recruitment tools in order to facilitate the re-entry of ex-offenders into the employment marketplace. [ABSTRACT FROM AUTHOR]

Since the release of ChatGPT for public use in November 2022, the academic world has been debating the use and merits of generative artificial intelligence (AI) in academia and academic libraries. Is ChatGPT something that librarians should adopt, or should they shun the new technology altogether? The following articles examine the possibilities of using generative AI and ChatGPT in academic libraries. Overall, the authors see positive applications for the technologies in libraries and encourage librarians to study and learn the implications of using generative AI models in academic library services. [ABSTRACT FROM AUTHOR]

This article focuses on the development of a custom chatbot for Zayed University Library (United Arab Emirates) using Python and the ChatGPT API. The chatbot, named Aisha, was designed to provide quick and efficient reference and support services to students and faculty outside the library's regular operating hours. The article also discusses the benefits of chatbots in academic libraries, and reviews the early literature on ChatGPT's applicability in this field. The article describes the development process, perceived capabilities and limitations of the bot, and plans for further development. This project represents the first fully reported attempt to explore the potential of a ChatGPT-based bot in academic libraries, and provides insights into the future of AI-based chatbot technology in this context. [ABSTRACT FROM AUTHOR]

This study investigated the effect of Chatbot-Assisted Dynamic Assessment (CA-DA) on vocabulary learning and provided insights into learner abilities drawn from its implementation. Through the use of mediating chatbots, this study implemented DA to multiple learners simultaneously and provided each learner with human-like interaction. The chatbots were created using Google's Dialogflow. Fifty-three Korean EFL primary school learners who were confirmed to demonstrate the same range in vocabulary size participated in this study. They were randomly assigned to three groups: CA-DA, Chatbot-Assisted Non-Dynamic Assessment (CA-NDA), and a control. For two treatment sessions, the learners were asked to read texts and identify the meaning of underlined target words. The chatbots provided graduated assistance to learners in the CA-DA group and only target word definitions to learners in the CA-NDA group. The control group did not utilize chatbots. Two posttests (receptive and productive) were administered both immediately after and two weeks after the second treatment session. Interaction records between the chatbots and learners across the two treatment sessions were also collected. Posttest results showed that vocabulary gains in the CA-DA group were significantly higher than in the other groups. Analysis of interactions between the chatbots and learners for the CA-DA sessions provided detailed evidence of learner development. The findings suggest that CA-DA could not only promote vocabulary acquisition but could also offer diagnostic information about individual learners concerning vocabulary learning. This study also demonstrated the potential of chatbot technology to support language learners. [ABSTRACT FROM AUTHOR]

Gemini Consulting & Services is a global IT services company providing innovative solutions to our clients around the world. Furthermore, we are deeply invested in Artificial Intelligence and Machine Learning (AI/ML), offering services that range from initial AI strategy and consulting to implementing AI-powered solutions. At Gemini Consulting & Services, we have a multi-faceted business model which involves not only IT consulting and services, but also project solutions, managed services, and a strong focus on ERP and Emerging Technologies. The Future of Software Development Consulting: How AI Technologies Might Impact This Industry?. [Extracted from the article]

Copyright of China Journal of Social Work is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Service Industries Journal is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ChatGPT is a new generative artificial intelligence (AI) chatbot that has been gathering significant attention since its release at the end of 2022. There are many reasons for the onslaught of discussion in the news media and academia, but principal among them is the fact that ChatGPT produces seemingly believable, coherent content with superhuman speed and efficiency, from creating computer code to penning a college-level essay. ChatGPT and similar technologies are improving by the day, and will only become more pervasive. Librarians and other information professionals need to understand this AI's uses and limitations, and, when appropriate, must adjust their practices to reflect the reality of easy-to-use AI tools that are accessible to the masses. This breakthrough technology may require educational institutions to change the way they approach information literacy, adjust teaching practices and assignments, and update honor codes. Libraries may see impacts on reference practices, collection development, and metadata creation and transformation. Those who are able to embrace and innovate with the technology may bring efficiencies and value to their institutions. [ABSTRACT FROM AUTHOR]

I will describe how our IT helps our business model in three dimensions: 1) Our technology platform offers our consumers the broadest range of data aggregation options including credit data, banking data, property information and payroll data. This interview provides insights into how a leading FinTech company - FinLocker is leveraging cloud and Artificial Intelligence (AI) technologies to offer solutions to multiple stakeholders in the mortgage industry, including mortgage originators, lenders, servicers, banks, credit unions, and credit counselors. [Extracted from the article]

As artificial intelligence (AI) agents like chatbots play larger roles in daily life, questions arise regarding how people evaluate their communication. Perspectives applying communication scripts to human-AI interactions propose that outcomes are determined by messages and the embedded cues therein. The expectancy violations perspective posits that message characteristics are less important than whether they are expected or unexpected. A pilot study established baseline expectancies about humans' and chatbots' conversational contingency and response latencies. A 2 (contingency: more/less contingent responses) × 2 (latency: fast/slow responses) × 2 (communicator identity: human/chatbot) experiment then tested predictions derived from human-human communication scripts and expectancy violations using textual variations in an e-commerce chat. Communicators showing greater conversational contingency and faster responses were most credible, whether they were human or chatbots, but chatbots were consistently less socially attractive than humans. Results show that humans and chatbots are evaluated similarly regarding the functional, but not the relational aspects of communication. There was greater support for the communication script perspective than the expectancy violations perspective regarding interactions with chatbots. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Extended Reality (XR) have been employed in several foreign language education applications to increase the availability of experiential learning methods akin to international immersion programs. However, research in multi-modal spoken dialogue in L2 combined with immersive technologies and collaborative learning is thin, limiting students' experiences to solo interactions focused mostly on vocabulary and grammar in such settings. We intend to fill this gap as we present the Cognitive Immersive Language Learning Environment (CILLE). The AI in CILLE can hear, see, and understand its users and can engage with them in non-dyadic multimodal conversations. The XR offers students a feeling of being somewhere else without the use of intrusive devices and supports multi-party, multi-modal interactions. Together, AI and XR create naturalistic conversational interactions targeted towards comprehensive foreign language acquisition. We evaluate CILLE as a Chinese-as-a-foreign-language (CFL) education tool through a seven-week, mixed-methods study with university students (N = 10). Results display statistical significance and retained improvement in CFL vocabulary, comprehension, and conversation skills. Coupled with an analysis of student feedback and researcher observations, we show how CILLE is designed and experienced by students to learn CFL. [ABSTRACT FROM AUTHOR]

As social actors, artificial intelligence (AI) devices are used in tourism. However, it's challenging for enterprises to choose recoveries for their service failures. To fill this gap, we divide these service failures from the perspective of consumers' basic psychological needs: being rejected and being ignored due to AI lacking flexibility and common sense. Based on the Need-Threat model and three experiments, we find that chatbots express gratitude (vs. apology) will be more likely to gain consumers' forgiveness if service failures of AI devices are being rejected (vs. being ignored); this main effect is mediated by relational needs (vs. efficacy needs). [ABSTRACT FROM AUTHOR]

A library's website is a virtual point of contact for interacting with its patrons. Ensuring a library's website has easily findable content is critical for providing access to library resources and highlighting services and events. One tool for assisting with content findability is a chatbot, a form of artificial intelligence software. In this case study, Lehman College's Leonard Lief Library implemented Ivy, a proprietary educational software chatbot on its website, the first of its kind for an academic library. This chatbot functioned as a new tool that assisted users seeking information and provided insight to librarians about the kinds of topics students search for via the library website. This article provides the first detailed description in the literature of an implementation of a proprietary chatbot for an academic library. [ABSTRACT FROM AUTHOR]

Increasing demand for mental health services and the expanding capabilities of artificial intelligence (AI) in recent years has driven the development of digital mental health interventions (DMHIs). To date, AI-based chatbots have been integrated into DMHIs to support diagnostics and screening, symptom management and behavior change, and content delivery. We summarize the current landscape of DMHIs, with a focus on AI-based chatbots. Happify Health's AI chatbot, Anna, serves as a case study for discussion of potential challenges and how these might be addressed, and demonstrates the promise of chatbots as effective, usable, and adoptable within DMHIs. Finally, we discuss ways in which future research can advance the field, addressing topics including perceptions of AI, the impact of individual differences, and implications for privacy and ethics. Our discussion concludes with a speculative viewpoint on the future of AI in DMHIs, including the use of chatbots, the evolution of AI, dynamic mental health systems, hyper-personalization, and human-like intervention delivery. [ABSTRACT FROM AUTHOR]