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Purpose: To compare the performance of licensed dentists and two software versions (3.5 legacy and 4.0) of an artificial intelligence (AI)-based chatbot (ChatGPT) answering the exam for the 2022 Certification in Implant Dentistry of the European Association for Osseointegration (EAO). Materials and Methods: The 50-question, multiple-choice exam of the EAO for the 2022 Certification in Implant Dentistry was obtained. Three groups were created based on the individual or program answering the exam: licensed dentists (D group) and two software versions of an artificial intelligence (AI)-based chatbot (ChatGPT)--3.5 legacy (ChatGPT-3.5 group) and the 4.0 version (ChatGPT-4.0 group). The EAO provided the results of the 2022 examinees (D group). For the ChatGPT groups, the 50 multiple-choice questions were introduced into both ChatGBT versions, and the answers were recorded. Pearson correlation matrix was used to analyze the linear relationship among the subgroups. The inter- and intraoperator reliability was calculated using Cronbach's alpha coefficient. One-way ANOVA and Tukey post-hoc tests were used to examine the data (α = .05). Results: ChatGPT was able to pass the exam for the 2022 Certification in Implant Dentistry of the EAO. Additionally, the software version of ChatGPT impacted the score obtained. The 4.0 version not only pass the exam but also obtained a significantly higher score than the 3.5 version and licensed dentists completing the same exam. Conclusions: The AI-based chatbot tested not only passed the exam but performed better than licensed dentists. [ABSTRACT FROM AUTHOR]

Firms are beginning to use chatbots to collect information from users (e.g., online reviews), a task traditionally done through forms. We used two experiments to investigate how collecting reviews using chatbots in place of forms might impact user experience and, in turn, alter the nature of the reviews submitted by users. Study 1 compares chatbots to web forms, finding that chatbots may improve perceived efficiency but otherwise have no discernible effect on satisfaction and usage intention. At the same time, chatbot usage results in shorter, lower-quality reviews. While structured templates enhance form-based reviews, introducing structure to chatbot interactions does not positively impact satisfaction and usage intent, potentially making the process less efficient. Nonetheless, a structured chatbot approach yields longer reviews and mitigates declines in quality. Adding structure to a chatbot takes the chat out of the chatbot, turning the interaction from casual conversation to a formal process, as demonstrated in Study 2. Hence, while this structured approach for chatbots improves review quality without harming satisfaction and usage intent, it may not be the most effective method for enhancing the reviewer experience. Our research shows that the chatbot and structure pulled users towards contradicting genre rules (the social structures that guide technology use) and triggered users to subconsciously enact distinctly different thought patterns as they composed reviews. [ABSTRACT FROM AUTHOR]

The Journal of Management Information Systems (JMIS) is celebrating its fifth decade of publication and is committed to publishing research papers that make significant contributions to the field of information systems. The journal's editors have an inclusive understanding of the field and aim to reflect and shape its growth and evolution. Over the years, JMIS has published papers on various topics, including the economics of IS, strategic deployment of IT, design research, neuroscience in IS research, behavioral cybersecurity, health informatics, and AI-human collaboration. The journal also recognizes its responsibility to support the well-being of society and explores the societal impacts of IT-driven change. The current issue features papers on topics such as the use of body-worn cameras in policing, technological innovation, crowdfunding, chatbots, sentiment analysis, cloud storage and security breaches, cybersecurity risk management, software architecture, and turnover of IT professionals. [Extracted from the article]

Many firms are beginning to replace customer service employees with bots, from humanoid service robots to digital chatbots. Using real human–bot interactions in lab and field settings, we study consumers' evaluations of bot-provided service. We find that service evaluations are more negative when the service provider is a bot versus a human—even when the provided service is identical. This effect is explained by consumers' belief that service automation is motivated by firm benefits (i.e. cutting costs) at the expense of customer benefits (such as service quality). The effect is eliminated when firms share the economic surplus derived from automation with consumers through price discounts. The effect is reversed when service bots provide unambiguously superior service to human employees—a scenario that may soon become reality. Consumers' default reactions to service bots are therefore largely negative but can be equal to or better than reactions to human service providers if firms can demonstrate how automation benefits consumers. [ABSTRACT FROM AUTHOR]

This research shows that AI-based conversational interfaces can have a profound impact on consumer–brand relationships. We develop a conceptual model of verbal embodiment in technology-mediated communication that integrates three key properties of human-to-human dialogue—(1) turn-taking (i.e. alternating contributions by the two parties), (2) turn initiation (i.e. the act of initiating the next turn in a sequence), and (3) grounding between turns (i.e. acknowledging the other party's contribution by restating or rephrasing it). These fundamental conversational properties systematically shape consumers' perception of an AI-based conversational interface, their perception of the brand that the interface represents, and their behavior in connection with that brand. Converging evidence from four studies shows that these dialogue properties enhance the perceived humanness of the interface, which in turn promotes more intimate consumer–brand relationships and more favorable behavioral brand outcomes (greater recommendation acceptance, willingness to pay a price premium, brand advocacy, and brand loyalty). Moreover, we show that these effects are reduced in contexts requiring less mutual understanding between the consumer and the brand. This research highlights how fundamental principles of human-to-human communication can be harnessed to design more intimate consumer–brand interactions in an increasingly AI-driven marketplace. [ABSTRACT FROM AUTHOR]

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]

In this article, the authors examine the effect of regulatory focus and privacy concerns on consumers' responses to highly personalized chatbot advertising. Findings from two experimental studies indicate that predominantly promotion-focused consumers are more receptive and respond more favorably to highly personalized chatbot advertising because they attend more to benefits they may gain from disclosing personal information. In contrast, consumers who are predominantly prevention focused are more attentive to the risks involved and feel unfavorably toward highly personalized chatbot advertising. In addition, consumers who are highly concerned about privacy disdain highly personalized ads, regardless of regulatory focus. Risk-benefit perceptions are shown to mediate interactions of ad personalization, regulatory focus, and privacy concerns. [ABSTRACT FROM AUTHOR]

This research note suggests the emergence of Artificial Intelligence-powered chatbots like ChatGPT pose challenges to the future of higher education. We as a field should pay attention to issues and opportunities associated with this technology across learning, teaching, and research spaces. We propose ignoring, or being indifferent to, predictions about what technologies like Artificial Intelligence-powered chatbots can do can cause us to do "dumb things." All health and physical education teacher education faculty members should make efforts to learn about these tools to facilitate informed, solution-focused decisions about whether and where to leverage them. We highlight the importance of maintaining sociocritical perspectives when considering use of digital technologies to understand and address digital (in)equity and promote equitable practices. We conclude by emphasizing the need for field-specific consensus statements to guide ethical and appropriate use of Artificial Intelligence-powered chatbots, to ensure the value of these tools is harnessed for the good of the society. [Output by ChatGPT-3] [ABSTRACT FROM AUTHOR]

This paper critically explores the implications of generative artificial “intelligence” (GAI) technologies for literacy theory and practice through a case study of the author's use of OpenAI's ChatGPT. The study opens with an overview of recent literature surrounding the pedagogical implications of using GAI with a focus on issues of racial justice, outlining an abolitionist political ecology approach to literacy that extends relational theories of mediation to machine‐aided writing. The framework is then applied to data from a cognitive autoethnography of GAI use over a 6‐month period, which included a digital ethnography of ChatGPT and an extended semistructured “interview” with the GAI chatbot. Racial justice issues were found, especially linguistic and other biases. As such, soon‐to‐be ubiquitous artificial intelligence (AI) technologies require profound reconsideration of the productive value of literacy exploited by GAI, which will inevitably be pursued through an acquiescence or fundamental rupture with the dystopian visions of the technology's creators. [ABSTRACT FROM AUTHOR]

This article explores the use of artificial intelligence (AI) in mental healthcare, specifically focusing on its impact on the therapist-patient relationship. AI technologies, such as chatbots, have the potential to provide personalized support and improve access to mental health services. However, there are concerns about transference, where patients may project their feelings onto the AI. It is important to carefully manage transference dynamics to avoid harm and maintain ethical standards. The development of AI systems that can recognize and appropriately respond to transference is necessary, and collaboration between AI developers and mental health professionals is crucial. Additionally, regulatory frameworks and ethical guidelines should be implemented to ensure responsible and effective use of AI in mental healthcare. Interdisciplinary collaboration, cultural sensitivity, and continuous monitoring and evaluation of AI interactions are also emphasized. [Extracted from the article]

Purpose: The aim of this study was to define the capability of ChatGPT-4 and Google Gemini in analyzing detailed glaucoma case descriptions and suggesting an accurate surgical plan. Methods: Retrospective analysis of 60 medical records of surgical glaucoma was divided into "ordinary" (n = 40) and "challenging" (n = 20) scenarios. Case descriptions were entered into ChatGPT and Bard's interfaces with the question "What kind of surgery would you perform?" and repeated three times to analyze the answers' consistency. After collecting the answers, we assessed the level of agreement with the unified opinion of three glaucoma surgeons. Moreover, we graded the quality of the responses with scores from 1 (poor quality) to 5 (excellent quality), according to the Global Quality Score (GQS) and compared the results. Results: ChatGPT surgical choice was consistent with those of glaucoma specialists in 35/60 cases (58%), compared to 19/60 (32%) of Gemini (p = 0.0001). Gemini was not able to complete the task in 16 cases (27%). Trabeculectomy was the most frequent choice for both chatbots (53% and 50% for ChatGPT and Gemini, respectively). In "challenging" cases, ChatGPT agreed with specialists in 9/20 choices (45%), outperforming Google Gemini performances (4/20, 20%). Overall, GQS scores were 3.5 ± 1.2 and 2.1 ± 1.5 for ChatGPT and Gemini (p = 0.002). This difference was even more marked if focusing only on "challenging" cases (1.5 ± 1.4 vs. 3.0 ± 1.5, p = 0.001). Conclusion: ChatGPT-4 showed a good analysis performance for glaucoma surgical cases, either ordinary or challenging. On the other side, Google Gemini showed strong limitations in this setting, presenting high rates of unprecise or missed answers. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) applications have been emerging in these past years and affecting multiple dimensions of the public sector. The government utilizes AI to transform policy implementation and service delivery, but AI can also threaten citizens' privacy and social equity due to its potential biases. These concerns increase citizens' perceived uncertainty concerning AI. In an uncertain environment, trust transfer serves as a way to improve citizens' trust in AI-enabled government systems. However, little research has explored trust transfer between the public sector and the system. This study examines whether a context-based trust transfer mechanism can explain the trust-building of the AI-enabled government system. The study conducted a survey and analyzed the collected data using factor-score-based regression analysis. The research results indicate that trust transfer occurs for the AI-enabled government system. Trust in an administrative process, local government, and political leaders can be transferred to trust in governmental AI systems. The findings can advance the theoretical development of trust transfer theory and be used to develop recommendations for the public sector. [ABSTRACT FROM AUTHOR]

Background Chat Generative Pre-trained Transformer (ChatGPT) and other ChatBots have emerged as tools for interacting with information in manners resembling natural human speech. Consequently, the technology is used across various disciplines, including business, education, and even in biomedical sciences. There is a need to better understand how ChatGPT can be used to advance gerontology research. Therefore, we evaluated ChatGPT responses to questions on specific topics in gerontology research, and brainstormed recommendations for its use in the field. Methods We conducted semistructured brainstorming sessions to identify uses of ChatGPT in gerontology research. We divided a team of multidisciplinary researchers into 4 topical groups: (a) gero-clinical science, (b) basic geroscience, (c) informatics as it relates to electronic health records, and (d) gero-technology. Each group prompted ChatGPT on a theory-, methods-, and interpretation-based question and rated responses for accuracy and completeness based on standardized scales. Results ChatGPT responses were rated by all groups as generally accurate. However, the completeness of responses was rated lower, except by members of the informatics group, who rated responses as highly comprehensive. Conclusions ChatGPT accurately depicts some major concepts in gerontological research. However, researchers have an important role in critically appraising the completeness of its responses. Having a single generalized resource like ChatGPT may help summarize the preponderance of evidence in the field to identify gaps in knowledge and promote cross-disciplinary collaboration. [ABSTRACT FROM AUTHOR]

This article examines 'feminist chatbots' as tools for activism through automation. Such bots aim to engage users in automated communication on feminist concerns. The article starts from the assumption that chatbots, like all technologies, have politics and that automation, including the automated communication of chatbots, is a feminist issue. We investigate how feminist chatbots mobilise automation to address societal inequalities and bias. Conceptually, the article draws on technofeminism and intersectionality as lenses for understanding the potential of chatbots to reflect activist concerns. Three different chatbots are analysed, using a cultural (case) studies approach: F'xa, Gender Pay Gap Bot and Betânia. The analysis suggests that feminist chatbots oppose mainstream automation by engaging users in communication about its sociotechnical risks and using automation to inspire feminist (data) activism. Yet challenges remain in designing such bots, partly because of platform dependencies and the limits of automating complex intersectional issues. [ABSTRACT FROM AUTHOR]

Recently, alternate reality games (ARGs) have been applied in museum learning to encourage learners' active engagement through playful problem‐solving activities. However, most learners have insufficient prior knowledge and metacognitive skills to complete the learning tasks in such games. To support learning with ARGs, there is a need to provide proper feedback so that the learners are capable of self‐regulated learning and solving the problems encountered during the learning process. Considering the influences of individual differences, this research developed a chatbot as a learning partner in ARG‐based learning to support students' learning with adaptive feedback in a museum. A quasi‐experimental study was conducted to evaluate the effectiveness with and without the proposed approach. The results showed that the AI‐based chatbot approach could significantly improve learners' metacognition awareness, emotional engagement and behavioural engagement. Besides, it is helpful to facilitate students' double‐loop learning.Practitioner notesWhat is already known about this topicAlternate reality games (ARGs) enable students to interact with real‐world contexts.The complexities of real contexts and ambiguities of the ARGs could frustrate students and demotivate their engagement in problem‐solving activities.Conventional corrected feedback may fail to match individual students' requirements in solving encountered problems.What this paper addsAn AI‐based chatbot was designed as a smart learning partner to support the students in solving encountered problems.The experimental results showed that the proposed approach improved students' metacognitive awareness and engagement.A drawing analysis and a behaviour sequential analysis were applied to further examine the students' perceptions and behavioural patterns.Implications for practice and/or policyIt is potentially worth integrating AI‐based chatbots into game‐based learning to promote students' metacognitive awareness and engagement.AI‐based chatbots provide adaptive feedback to effectively facilitate students' self‐regulated learning and double‐loop learning. [ABSTRACT FROM AUTHOR]

• Artificial Intelligence (AI) is transforming medical writing, raising ethical and quality concerns. • Med-PaLM 2 achieved 86.5 % on the USMLE but had low scientific consensus about adherence. • While AI tools can undoubtedly enhance literature review, statistical analysis, and translation processes, they faces several criticisms. • Threats include increased research waste, low-quality papers, and fraudulent publications. • Regulation and ethical frameworks are essential for managing AI's impact on science. The debate surrounding the integration of artificial intelligence (AI) into scientific writing has already attracted significant interest in medical and life sciences. While AI can undoubtedly expedite the process of manuscript creation and correction, it raises several criticisms. The crossover between AI and health sciences is relatively recent, but the use of AI tools among physicians and other scientists who work in the life sciences is growing very fast. Within this whirlwind, it is becoming essential to realize where we are heading and what the limits are, including an ethical perspective. Modern conversational AIs exhibit a context awareness that enables them to understand and remember any conversation beyond any predefined script. Even more impressively, they can learn and adapt as they engage with a growing volume of human language input. They all share neural networks as background mathematical models and differ from old chatbots for their use of a specific network architecture called transformer model [ 1 ]. Some of them exceed 100 terabytes (TB) (e.g., Bloom, LaMDA) or even 500 TB (e.g., Megatron-Turing NLG) of text data, the 4.0 version of ChatGPT (GPT-4) was trained with nearly 45 TB, but stays updated by the internet connection and may integrate with different plugins that enhance its functionality, making it multimodal. [ABSTRACT FROM AUTHOR]

This study presents a corpus analysis of academic integrity policies from Higher Education Institutions (HEIs) worldwide, exploring how they address the issues posed by technological threats, such as Automated Paraphrasing Tools and generative-artificial intelligence tools, such as ChatGPT. The analysis of 142 policies conducted in November and December 2022, and May 2023 reveals a gap regarding the mention of AI and associated technologies in the available academic integrity policies. Despite the growing prevalence of these tools in the 6-month period since the release of ChatGPT, no HEIs had produced revised academic integrity policies. Content analysis of 53 guidance documents produced by HEIs suggests an overall positive focus of Gen AI tools, yet advises caution. This study suggests a modification to Bretag et al.'s (Int J Educ Integr 7, 2011) exemplary academic integrity model, introducing "Technological Explicitness" — emphasizing the need to include explicit guidelines about new technologies in academic integrity policies. These results underscore the urgent need for HEIs to revise their academic integrity policies, considering the evolving landscape of AI and its implications for academic integrity. This paper argues for a multifaceted approach to deal with the issues of integrating technology, education, policy reform, and assessment restructuring to navigate these challenges while upholding academic integrity. [ABSTRACT FROM AUTHOR]

We investigate how bots influence human-to-human interaction in online communities. In doing so, we distinguish between reflexive and supervisory bots delegated by community participants and moderators, respectively. We hypothesize that reflexive bot activity will reduce direct reciprocity and increase generalized reciprocity and that supervisory bot activity will reduce preferential attachment among human participants. Through an analysis of almost 70 million posts on the discussion communities on Reddit, a popular platform for online discussions, we found support for the hypotheses. [ABSTRACT FROM AUTHOR]

Conversational robots (chatbots) are currently an extended Insurtech that is widely used to enable policyholders' communication with insurance firms. This paper analyses customers' acceptance of chatbots in procedures with regard to in-force policies. We analysed a semistructured survey answered by workers of a public Spanish university. Structured questions have been grounded in the well-known technology acceptance model (TAM), which is based on two explanatory variables: perceived usefulness (PU) and perceived ease of use (PEOU). We have also considered two additional explanatory factors: social influence (SI), whose impact on behavioral intention (BI) is mediated by PU, and trust (TRUST), whose influence on BI is supposed to be made throughout PU and PEOU. Likewise, we asked two open questions about the advantages and disadvantages of chatbot use to make procedures linked with in-force insurance contracts. We performed our analysis by using quantitative and qualitative methods. The quantitative analysis tests the suitability of TAM on our data and has been performed by using structural equation modelling with partial least squares (PLS-SEM). Subsequently, to reach a deeper understanding of the reasons that explain the respondents' behavioral intention, we provide a systematic overview of the answers to open questions with the help of the groundwork provided by TAM. We have checked that the basic TAM along with social influence and trust provide a satisfactory explanation for behavioral intention toward bots. Likewise, we have observed a general reluctance toward the use of chatbots. The qualitative analysis showed that arguments explaining resistance come from all explanatory factors considered in the paper. Therefore, mainstream responses have outlined that interaction with chatbots is difficult, and many times, the procedure must be finished with the assistance of a human operator. Likewise, many responses point out as a relevant drawback that they provide a dehumanized service without empathy. Consequently, interactions with chatbots are perceived as cumbersome, ineffective, and a loss of time. Although some people perceive that the faster service provided by chatbots in concrete circumstances is an advantage, other theoretical consequences that may add value, such as temporal flexibility and the possibility of proving better services with the same cost and/or reducing insurance prices because of the reduction of firms' administrative costs, are generally not perceived. Our findings have theoretical and practical implications. We have shown that TAM provides a reliable theoretical model to understand policyhoders' acceptance of chatbot technology in an insurance setting. Perceived usefulness, reliability, social opinion about bot adoption, and usability must be improved to avoid generalized policyholders' reluctance. That resistance is because of issues such as conversational skills, the capability to provide an interaction closer to being human, and users' perception that chatbots actually add value to policyholders. [ABSTRACT FROM AUTHOR]

With the growth of contactless shopping, artificial intelligence (AI) chatbot services have become an effective tool for brands to interact with consumers. We investigate the effects of AI chatbots' humanlike characteristics on consumers' acceptance of chatbot services by mediating competence, warmth, and empathy based on the AI device use acceptance (AIDUA) model. Using structural equation modeling, we tested how the relationship between these variables differs depending on one's need for human interaction. Findings suggest that AI chatbots' personalization and anthropomorphism significantly increase perceived competence and warmth, leading to empathy. This positively affects consumers' willingness to use AI chatbots. Consumers' need for interaction moderates the relationship between anthropomorphism and competence. We applied a valid theoretical model to explore how the humanlike characteristics of AI chatbots affect consumers' cognitive and emotional reactions, determining their service acceptance. The findings have practical value for online retailers who use AI chatbots as marketing communication tools. [ABSTRACT FROM AUTHOR]

ChatGPT can revolutionize education by enhancing student engagement and making learning more personalized. Drawing on UTAUT2, this study investigated determinants of intention to use ChatGPT for educational purposes. Data were gathered from 406 Malaysian students and analyzed using a hybrid approach including "partial least squares" (PLS) and "fuzzy-set qualitative comparative analysis" (fsQCA). PLS showed that performance expectancy, effort expectancy, hedonic motivation, and learning value significantly influence the intention to use ChatGPT. Furthermore, we found that personal innovativeness and information accuracy negatively moderate the associations between ChatGPT use and its determinants. While PLS demonstrated that social influence, facilitating conditions, and habit do not affect ChatGPT use, fsQCA revealed that all factors might affect the intention to use ChatGPT. fsQCA suggested that eight combinations of factors may lead to high ChatGPT use. The results hold various implications for ChatGPT developers, instructors, and universities and provide insights for accelerating ChatGPT adoption. [ABSTRACT FROM AUTHOR]

Consumers' resistance towards chatbot agency questions the quality of human-computer interaction, and thus, invites further investigation on factors determining chatbot efficiency. Current study addresses the above challenges by providing understanding on functionality and enjoyment, emerging as crucial drivers of satisfaction. Consumers who have used chatbots at least once in their daily life were invited to complete a survey, evaluating the interaction with the bot. The results are clear in showing that information quality, accuracy and competence are pivotal for chatbot functionality. Personal care and social presence enhanced interactivity perception and the enjoyment of the chatbot use. These factors might however turn into barriers when not met, as reported by consumers in low satisfaction group. Current findings provide valuable insights for developing and implementing chatbot applications to satisfy the user demand for enjoyable and functional interaction, through enhanced information quality, competence, personal care and social presence. [ABSTRACT FROM AUTHOR]

Chatbots can effect large-scale behaviour change because they are accessible through social media, flexible, scalable, and gather data automatically. Yet research on the feasibility and effectiveness of chatbot-administered behaviour change interventions is sparse. The effectiveness of established behaviour change interventions when implemented in chatbots is not guaranteed, given the unique human–machine interaction dynamics. We pilot-tested chatbot-based behaviour change through information provision and embedded animations. We evaluated whether the chatbot could increase understanding and intentions to adopt protective behaviours during the pandemic. Fifty-nine culturally and linguistically diverse participants received a compassion intervention, an exponential growth intervention, or no intervention. We measured participants' COVID-19 testing intentions and measured their staying-home attitudes before and after their chatbot interaction. We found reduced uncertainty about protective behaviours. The exponential growth intervention increased participants' testing intentions. This study provides preliminary evidence that chatbots can spark behaviour change, with applications in diverse and underrepresented groups. Key message: Chatbots can be equipped with animations to deliver standardised information and behaviour change interventions. We found preliminary evidence that using chatbots for behaviour change may be effective when users seek information independently. [ABSTRACT FROM AUTHOR]

Chatbots have several features that may stimulate self-disclosure, such as accessibility, anonymity, convenience and their perceived non-judgmental nature. The aim of this study is to investigate if people disclose (more) intimate information to a chatbot, compared to a human, and to what extent this enhances their emotional well-being through feelings of relief. An experiment with a 2 (human vs. chatbot) by 2 (low empathetic vs. high empathetic) design was conducted (N  = 286). Results showed that there was no difference in the self-reported intimacy of self-disclosure between the human and chatbot conditions. Furthermore, people perceived less fear of judgment in the chatbot condition, but more trust in the human interactant compared to the chatbot interactant. Perceived anonymity was the only variable to directly impact self-disclosure intimacy. The finding that humans disclose equally intimate information to chatbots and humans is in line with the CASA paradigm, which states that people can react in a social manner to both computers and humans. [ABSTRACT FROM AUTHOR]

The widespread availability of large language models (LLMs) has encouraged many individuals to explore chatbot development for their business needs. However, creating chatbots for handling sensitive information, like in healthcare, can be challenging. Mistakes made by these bots when extracting information or providing health recommendations can have serious consequences. When developing a chatbot for pharmacy recommendations, it is essential for the bot to effectively extract symptom-related information and other relevant patient data and then offer recommendations for actions or medications based on that information. In this study, we proposed a straight forward and effective approach that combines regular expression templates for information extraction with forward chaining for inference to create a pharmacy recommendation chatbot called SmartFarma. In scenarios like pharmacy recommendations, we will demonstrate that the use of regular expression templates is sufficient and produces better results than some machine learning methods. Additionally, by using regular expressions, SmartFarma can be developed with transparent data handling, allowing experts to trace, monitor, and evaluate its recommendations. This research primarily focuses on the extraction of patient information. Our model, as proposed, achieved an impressive score of 81.54%, outperforming both the Biomedical Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) models. [ABSTRACT FROM AUTHOR]

This study assesses the effectiveness of ChatGPT versus the Longman Dictionary of Contemporary English (LDOCE) in supporting English language learners in lexically challenging receptive and productive lexical tasks. With a sample of 223 university students at B2 to C1 proficiency levels, this research investigates whether a leading AI-driven chatbot or a high-quality learners' dictionary better assists learners in accurately understanding and producing English. The results reveal ChatGPT's superior performance in both task types. Efficiency, in terms of consultation speed, also favoured ChatGPT, though only in the production task. This study advocates for an integrated approach that leverages both AI, with its interactive and immediate feedback, and more traditional lexicographic tools that may foster learner autonomy and linguistic proficiency. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI) allows the brand to co-create value with young customers through mobile apps. However, as many brands claim that their mobile apps are using the most updated AI technology, young customers face app fatigue and start questioning the authenticity of this touchpoint. This paper aims to study the mediating effect of authenticity for the value co-creation of AI-powered branded applications. Design/methodology/approach: Drawing from regulatory engagement theory, this study conceptualize authenticity as the key construct in customers' value experience process, which triggers customer value co-creation. Two scenario-based online experiments are conducted to collect data from 444 young customers. Data analysis is performed using ANOVA and Process Hayes. Findings: The results reveal that perceived authenticity is an important mediator between media richness (chatbot vs AI text vs augmented reality) and value co-creation. There is no interaction effect of co-brand fit (high vs low) and source endorsement (doctor vs government) on the relationship between media richness and perceived authenticity, whereas injunctive norms (high vs low) strengthen this relationship. Practical implications: The finding provides insights for marketing managers on engaging young customers suffering from app fatigue. Authenticity holds the key to young customers' technological perceptions. Originality/value: This research highlights the importance of perceived authenticity in encouraging young customers to co-create value. Young customers consider authenticity as a motivational force experience that involves customers through the app's attributes (e.g. media richness) and social standards (e.g. norms), rather than brand factors (e.g. co-brand fit, source endorsement). [ABSTRACT FROM AUTHOR]

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a "quasi-other"; when that happens, users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. [ABSTRACT FROM AUTHOR]

The development of e-commerce faces many problems, among which the real-time information exchange between customers and websites is the most urgent. To study the application of software robots in the real-time processing of e-commerce orders. According to the working mechanism of ALICE, JAVAPROGRAMD, AIML technology, database technology, knowledge base principle, Rule of inference, and reasoning strategies are used to design the structure, workflow, knowledge processing flow, and Inference engine supporting human-computer automatic negotiation of the software robot. Establishing a rule base is the acquisition, induction, and organization of knowledge related to order processing. It requires learning and analyzing the problems and related materials to be solved, simulating the actual process of order processing, and extracting useful processing rules and processes from it. The rules in the system are not organized in a simple list form, but have a certain hierarchical structure. This way, when using these rules for reasoning, the hierarchy is clear and it is easy to modify and add rules. The hierarchical structure of the rules referred to here is to simulate the thinking activities of the human brain in the process of order processing, and organize them in a certain way. Based on the basic rules of order processing, the process of order processing, the rational performance of the order and the possibility of necessary events, so as to realize the structure of the reasoning machine. Expands the application of Chatbot ALICE, and provides a new tool for real-time online negotiation and negotiation of e-commerce order processing. The core part of the e-commerce order processing software robot is the human-machine automatic interaction module. Database module, knowledge base module and rule base module provide the necessary data, knowledge and rule basis for its realization, while the implementation of reasoning machine provides the possibility for the intelligence and dynamic interaction of human-machine automatic interaction module. [ABSTRACT FROM AUTHOR]

The field of chatbots has witnessed a remarkable evolution in recent years, marked by a transition from simplistic rule-based structures to sophisticated systems employing advanced natural language processing (NLP) techniques. While most languages benefit from NLP support, the majority of chatbot research and development has been conducted in English, leaving a notable scarcity of comparable works in Arabic. This scarcity is attributed to the myriad challenges posed by the linguistically intricate nature of Arabic, encompassing orthographic variations and diverse dialects. This study systematically reviews articles that represent implementations of Arabic chatbots, revealing a discernible shift from rule-based frameworks to the predominant adoption of machine learning (ML) and deep learning (DL) methods. The results highlight the dynamic trajectory of chatbot technology, with a notable emphasis on the pivotal role of DL, as evidenced by a significant peak in 2023. Looking forward, the study anticipates a more sophisticated future for chatbot development, driven by ongoing advancements in artificial intelligence (AI) and NLP, offering valuable insights into the current state of Arabic chatbot research and laying the foundation for continued exploration in this evolving and dynamic field. [ABSTRACT FROM AUTHOR]

The educational recommendation system with an artificial intelligence (AI) chatbot is a system designed to operate on smartphones and is intended for use at the College of Industrial Technology in Thailand. Compatible with both iOS and Android operating systems, the educational recommendation system in this research is capable of providing educational recommendations and valuable information about the engineering degree program. It includes information about the Electronics Engineering Technology Department, relevant bodies of knowledge that can enhance awareness and public relations, and a proactive introduction to information. The main objectives of this study are to develop the capacity to analyze educational issues and provide useful and accurate information for educational recommendations, as well as to explore the possibilities for enhancing this system. The results of the study address the three research questions and demonstrate that the process and structure of the educational recommendation system with an AI chatbot can be utilized to create educational recommendation tools in higher education. These tools are accessible anywhere and at any time through small mobile devices. The system is also compatible with popular social media applications and is capable of analyzing user questions precisely and accurately to find the optimal answers that can meet the user's needs. However, there are still gaps in the research that need to be investigated in the future. These studies should include exploring a broader range of applications among the general population and educational settings to assess the effectiveness of the educational recommendation system with an AI chatbot. Additionally, they should examine the skills that impact the utilization of educational technology in various contexts. [ABSTRACT FROM AUTHOR]