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A definitive resource for exploring conversational AI, ChatGPT, and large language models. This book introduces the fundamentals of ChatGPT and conversational AI automation. You'll explore the application of ChatGPT in conversation design, the use of ChatGPT as a tool to create conversational experiences, and a range of other practical applications. As you progress, you'll delve into LangChain, a dynamic framework for LLMs, covering topics such as prompt engineering, chatbot memory, using vector stores, and validating responses. Additionally, you'll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem.

PurposeThe application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.Design/methodology/approachThis study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.FindingsEducational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.Research limitations/implicationsThe authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.Originality/valueThis study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

ChatGPT is an artificial intelligence (AI) chatbot, a software application that aims to mimic human conversation through text or voice interactions, launched as a prototype on November 30, 2022, garnering attention for its detailed responses and articulate answers across many domains of knowledge. ChatGPT can write and debug computer programs, mimic the style of celebrity CEOs and write business pitches, compose music, teleplays, fairy tales and student essays, answer test questions (sometimes, depending on the test, at a level above the average human test-taker), write poetry and song lyrics, translate and summarize text, emulate a Linux system; simulate entire chat rooms, play games like tic-tac-toe and simulate an ATM. This is a collection of questions and answers from ChatGPT.

Recent developments in natural language understanding have sparked a great amount of interest in the large language models such as ChatGPT that contain billions of parameters and are trained for thousands of hours on all the textual data of the internet. ChatGPT has received immense attention because it has widespread applications, which it is able to do out-of-the-box, with no prior training or fine-tuning. These models show emergent skill and can perform virtually any textual task and provide glimmers, or “sparks”, of artificial general intelligence, in the form of a general problem solver as envisioned by Newell and Simon in the early days of artificial intelligence research. Researchers are now exploring the opportunities of ChatGPT in education. Yet, the factors influencing and driving users’ acceptance of ChatGPT remains largely unexplored. This study investigates users’ (n=138) acceptance of ChatGPT. We test a structural model developed using Unified Theory of Acceptance and Use of Technology model. The study reveals that performance expectancy is related to behavioral intention, which in turn is related to ChatGPT use. Findings are discussed within the context of mass adoption and the challenges and opportunities for teaching and learning. The findings provide empirical grounding to support understanding of technology acceptance decisions through the lens of students’ use of ChatGPT and further document the influence of situational factors on technology acceptance more broadly. This research contributes to body of knowledge and facilitates future research on digital innovation acceptance and use.

ChatGPT has experienced unprecedented acceptance and use, capturing popular and academic attention. With this growth in use comes the need to focus on the determinants of ChatGPT use as the success of a technology or service depends largely on users’ continuance intentions. Modeling what influences students’ intention to continue using ChatGPT is important to better understand how students search for information and their decision-making process. Using a sample of 106 students, we test a structural model developed using the unified extended-confirmation model. The research model included the following elements: subjective norm, perceived usefulness of continued use, disconfirmation of their expectations from prior use, satisfaction with prior use, and continuance intention. The findings demonstrate support for the proposed research model as the research model explains 60.5% of the variance in continuance intention. In terms of the direct influence on continuance intention, the role of perceived usefulness and satisfaction were documented. The present study has the potential to serve as a starting point for improving our understanding of antecedents of continuance intentions in the context of ChatGPT.

\"Learn how to harness the power of AI in your classroom! This professional resource includes 50 easy-to-implement strategies for using AI technology as a tool for teaching. This book saves teachers valuable time with activities that boost literacy, content knowledge, and student engagement using AI tools.Written by a teacher who specializes in using technology in his own classroom, this professional book introduces artificial intelligence and the many ways it can help educators. These quick, accessible strategies for using AI academically and creatively can be used in any K-12 classroom. From sample prompts for AI to activities that support community building and fun, help teachers save valuable time and implement creative strategies with the tools in this resource!\"-- Provided by publisher.

With the rapid adoption of generative artificial intelligence (GenAI) chatbots on e-commerce platforms, users’ expectations for anthropomorphic service experiences have risen significantly. Despite the growing presence of GenAI, little is known about how different types of anthropomorphic users’ self-efficacy and the intention to adopt as a decision aid through distinct cognitive pathways. Addressing this research gap, this study draws on the elaboration likelihood model (ELM) to develop a comprehensive framework that integrates central and peripheral cues. Using large-scale survey data from e-commerce users and structural equation modeling, the research empirically examines the mediating role of self-efficacy and the moderating effect of information overload. Results indicate that human-like empathy and perceived warmth (peripheral cues) and perceived competence (central cue) all significantly enhance self-efficacy, which in turn positively influences the intention to adopt as a decision aid. Moreover, information overload intensifies the effect of peripheral cues on self-efficacy but has a limited impact on central cues. These findings advance the theoretical understanding of GenAI–human interaction by clarifying the mechanisms through which anthropomorphic features operate, and provide actionable insights for designing user-centric GenAI recommendation services to optimize user experience and encourage the intention to adopt as a decision aid in e-commerce.

Over 96% of adult women face health issues, with 70% experiencing conditions like infections. Mobile health education is increasingly popular but faces challenges in personalization and readability. Artificial intelligence (AI) chatbots provide tailored support, and a discrete choice experiment can help in understanding user preferences to improve chatbot design. This study aims at exploring the preferences of women toward AI chatbots to improve health education communication and user experience. A discrete choice experiment was conducted, identifying 6 main attributes of AI chatbots: response accuracy, legibility, service cost, background information collection, information utility, and content provision. A total of 957 female participants from a hospital in Hebei Province participated, choosing between 2 hypothetical chatbots or opting for neither (a no-choice option). The conditional logit model was used to estimate user preferences. A total of 957 participants were included in the analysis. The results showed that participants preferred a chatbot with 100% response accuracy (β=0.940, P<.001; 95% CI 0.624 to 1.255), very easy to understand information (β=0.907, P<.001; 95% CI 0.634 to 1.180), a service fee of CN ¥0/month (β=-0.095, P<.001; 95% CI -0.108 to -0.082; a currency exchange rate of US $1=CN ¥7.09 was applicable), practical information utility (β=1.085, P<.001; 95% CI 0.832 to 1.338), and provision of disease-related knowledge (β=0.752, P<.001; 95% CI 0.485 to 1.018). Whether or not to allow the collection of background information (only question and answer information) has no significant impact on women's choice preferences. Additionally, participants were willing to pay an additional CN ¥9.916 (95% CI 6.843 to 12.292) for 100% response accuracy, CN ¥9.567 (95% CI 6.843 to 12.292) for \"very easy to understand\" information, and CN ¥11.451 (95% CI 8.704 to 14.198) for the \"very practical\" information utility. Additionally, they were willing to pay CN ¥7.931 (95% CI 4.975 to 10.886) for \"knowledge of diseases\" compared to \"gender knowledge\" (CN ¥2.602, 95% CI -0.551 to 5.756). The relative importance of the chatbot attributes indicated that information utility (1.085/3.858, 28.12%) and response accuracy (0.940/3.858, 24.37%) were the most influential factors in participants' preferences. AI chatbots designed for female users should focus on high response accuracy, clear content, free access, privacy protection, practical information, and disease knowledge to attract users and enhance health education.

Background and Aim: Many people seek health information online, but misinformation can lead to incorrect medical decisions. The aim of the study was to assess patients’ medical internet use and their intention to discuss online findings with doctors. Materials and Methods: The observational and cross-sectional study included a sample of patients with chronic non-communicable diseases who voluntarily participated in a health education and screening campaign, conducted in four cities in Romania during March-November 2024. Socio-demographic data and answers to seven specific questions were collected using a face-to-face questionnaire developed by the authors. The study was conducted with the approval of the ethics committee of George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures. Results: The study sample included 495 respondents. The mean age of respondents was 48.7 years; 72.9% were females, and 73.4% resided in urban areas. Approximately half of the respondents (51.2%) had a high school education or less, while 48.7% had post-secondary or university education. Over 65% of respondents searched for health information online frequently or sometimes, while 34.4% did so rarely or never. Most respondents (90.9%) searched for medical information using a mobile device, while fewer used laptops (19.9%), tablets (11.3%), or desktops (10.8%). The vast majority (94.1%) used Google to find medical information, while fewer relied on websites (21.2%), social media (15.0%), forums (9.6%), or artificial intelligence chatbots (3.9%). The vast majority (81.6%) believed that it would be useful to discuss online health information with doctors. More than half (57.4%) had asked a doctor to explain medical information found online, while 35.2% had not. Most respondents (84.8%) received a response from their doctor: 50.8% obtained a detailed answer, while 34.0% received a brief one. However, 10.9% felt that their questions were avoided or unwelcome. Conclusions: Most respondents frequently search for health information online, mainly using mobile devices and Google. Many patients appreciate discussing the health information they find online with doctors, highlighting the need for professional guidance. While most doctors provided answers, some patients perceived reluctance or avoidance, suggesting room for better communication.