Search

ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms and supply chains. However, the empirical literature reporting the benefits, challenges, and outlook of these nascent technologies in operations and supply chain management (OSCM) is limited. This study surveys current projects and perceptions of these technologies in US (n = 119) and UK (n = 181) supply chains. We found that projects range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, and process efficiencies. The main challenges concern data, technological and organisational issues. Expected benefits are dominated by cost savings and enhanced customer experience, but also include increased automation and sustainability. Industries were found to cluster around six groups according to perceived benefits and implementation challenges. Our findings contribute to the emerging literature on Gen-AI use in OSCM, and to management practice by mapping the benefits, challenges, outlook, and maturity level of Gen-AI projects in supply chains. [ABSTRACT FROM AUTHOR]

In this article, we provide a brief overview of generative artificial intelligence (GenAI) and large language models (LLMs). We then propose eight critical questions that libraries should ask when exploring this technology and its implications for their communities. We argue that libraries have a unique role in facilitating informed and responsible use of GenAI, as well as safeguarding and promoting the values of access, privacy, and intellectual freedom. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI. [ABSTRACT FROM AUTHOR]

Background Artificial intelligence chatbots have demonstrated potential to enhance clinical decision-making and streamline health care workflows, potentially alleviating administrative burdens. However, the contribution of AI chatbots to radiologic decision-making for clinical scenarios remains insufficiently explored. This study evaluates the accuracy and reliability of four prominent Large Language Models (LLMs)—Microsoft Bing, Claude, ChatGPT 3.5, and Perplexity—in offering clinical decision support for initial imaging for suspected pulmonary embolism (PE). Methods Open-ended (OE) and select-all-that-apply (SATA) questions were crafted, covering four variants of case scenarios of PE in-line with the American College of Radiology Appropriateness Criteria. These questions were presented to the LLMs by three radiologists from diverse geographical regions and setups. The responses were evaluated based on established scoring criteria, with a maximum achievable score of 2 points for OE responses and 1 point for each correct answer in SATA questions. To enable comparative analysis, scores were normalized (score divided by the maximum achievable score). Result In OE questions, Perplexity achieved the highest accuracy (0.83), while Claude had the lowest (0.58), with Bing and ChatGPT each scoring 0.75. For SATA questions, Bing led with an accuracy of 0.96, Perplexity was the lowest at 0.56, and both Claude and ChatGPT scored 0.6. Overall, OE questions saw higher scores (0.73) compared to SATA (0.68). There is poor agreement among radiologists' scores for OE (Intraclass Correlation Coefficient [ICC] = −0.067, p = 0.54), while there is strong agreement for SATA (ICC = 0.875, p < 0.001). Conclusion The study revealed variations in accuracy across LLMs for both OE and SATA questions. Perplexity showed superior performance in OE questions, while Bing excelled in SATA questions. OE queries yielded better overall results. The current inconsistencies in LLM accuracy highlight the importance of further refinement before these tools can be reliably integrated into clinical practice, with a need for additional LLM fine-tuning and judicious selection by radiologists to achieve consistent and reliable support for decision-making. [ABSTRACT FROM AUTHOR]

Conversation AI such as ChatGPT is a disruptive technology that has the potential to influence the field of counseling. The present article examines how AI based chatbot ChatGPT can be used by counselor educators in helping counselors-in-training practice counseling skills through role play. The author developed a model, ChatGPT Client Simulation (CCS), to support counselors-in-training learn and practice counseling skills. The CCS model can be used both in synchronous online as well as in-person counseling courses. [ABSTRACT FROM AUTHOR]

ChatGPT and other LLMs are at the forefront of pedagogical considerations in classrooms across the academy. Many studies have spoken to the technology's capacity to generate one-off texts in a variety of genres. This study complements those by inquiring into its capacity to generate compelling texts at scale. In this study, we quantitatively and qualitatively analyze a small corpus of generated texts in two genres and gauge it against novice and published academic writers along known dimensions of linguistic variation. Theoretically, we position and historicize ChatGPT as a writing technology and consider the ways in which generated text may not be congruent with established trajectories of writing development in higher education. Our study found that generated texts are more informationally dense than authored texts and often read as dialogically closed, "empty," and "fluffy." We close with a discussion of potentially explanatory linguistic features, as well as relevant pedagogical implications. [ABSTRACT FROM AUTHOR]

Since its release in late 2022, ChatGPT and subsequent generative artificial intelligence (GAI) tools have raised a wide variety of questions and concerns for the field of technical communication: How will these tools be incorporated into professional settings? How might we appropriately integrate these tools into our research and teaching? In this review, we examine research published in 2023–2024 addressing these questions (N = 28). Overall, we find preliminary evidence that GAI tools can positively impact student writing and assessment; they also have the potential to assist with some aspects of academic and medical research and writing. However, there are concerns about their reliability and the ethical conundrums raised when they are used inappropriately or when their outputs cannot be distinguished from humans. More research is needed for evidence-based teaching and research strategies as well as policies guiding ethical use. We offer suggestions for new research avenues and methods. [ABSTRACT FROM AUTHOR]

This introductory article examines the evolving landscape of generative artificial intelligence (GAI) tools, contextualizing their impact through historical tropes of automation as both helper and threat. The authors argue that GAI tools are neither sentient helpers nor existential threats but complex systems that require careful integration into educational and research settings. The article underscores the importance of nuanced, evidence-based approaches, advocating for a balanced understanding of GAI's potential and limitations. It emphasizes ethical considerations and promotes reflective adoption over reactionary measures. [ABSTRACT FROM AUTHOR]

Three within-subject experiments were conducted by providing students with answers to content questions across different subject matters (a definition, explanation, and example) offered by a human professor (subject-matter expert) versus generative artificial intelligence (ChatGPT). In a randomized order, students read both the expert and ChatGPT’s responses (both were de-identified and declared to be “professors,” so students were not aware one was artificial intelligence), rated both explanations on teaching clarity and competence, and then reported on their affect toward the content and situational interest. Study 1 (interpersonal communication content) revealed no significant differences in repeated measure ratings comparing the expert versus ChatGPT. However, in Study 2 (business communication content) and Study 3 (instructional communication content), compared with the expert, ChatGPT (impersonating a professor) was rated by the same students as higher in teaching clarity and competence, and it generated more student affect and situational interest. In Study 2 and Study 3, a within-subjects mediation analysis revealed that ChatGPT generated more student affect toward the content through the clarity in responses it provided to students. [ABSTRACT FROM AUTHOR]

ChatGPT has revolutionized the travel industry. This study employs the stimulus-organism-response (SOR) model to develop and validate a conceptual model for ChatGPT acceptance. Social influence and perceived value emerge as key determinants of user cognitive appraisals of ChatGPT's expertise, trustworthiness, and emotional connections through parasocial interaction. These factors subsequently influence traveler acceptance of ChatGPT for travel-related services. Findings reveal that social influence is the most potent predictor of ChatGPT acceptance, while perceived trust directly impacts user acceptance during the cognitive process. These insights advance research on parasocial interaction, while providing valuable guidance for implementing ChatGPT in tourism services. [ABSTRACT FROM AUTHOR]

This article explores the application of generative artificial intelligence (AI), such as ChatGPT, in the development of customized textual materials for communication teaching. It underscores the potential of generative AI to address the scarcity of suitable learning materials and enhance teaching effectiveness. A case study of text generation for a communication theory class highlights the process and efficiency of employing generative AI to produce contextually rich and pedagogically relevant teaching resources. [ABSTRACT FROM AUTHOR]

This essay explores the nuanced impact of generative AI technologies on management and business education, framed through three paradoxes: the Expertise Paradox suggests that AI's adequate performance at lower-level tasks may weaken students' development of higher-level thinking; the Innovation Paradox states that AI's creativity aid could stifle original thinking; and the Equity Paradox highlights AI's potential to provide immense gains to experts but disproportionately harm novices. We take the position that without "sensible" AI use guidelines in management education, AI is likely to have a net-negative effect on learning. This stance is based on our trials with ChatGPT on various cognitive tasks organized around the revised Bloom's Taxonomy of learning. We identify areas where AI tools can enhance learning, such as comprehending established subject domains, as well as areas where they exhibit significant limitations, such as logical reasoning and critical thinking. We caution against the potential deskilling in critical thinking due to students' overreliance on AI for basic tasks. To alleviate these challenges, we recommend sensible AI uses by students that support skill development without fostering overreliance. We also suggest how faculty, administrators, and employers may support students in getting the most out of this new tool. [ABSTRACT FROM AUTHOR]

As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT's applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its comprehensive analysis of the existing literature, addressing a critical gap in understanding ChatGPT's adaptability, limitations, and optimal application. In this paper, we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to direct our search process and seek relevant studies. Our review reveals ChatGPT's significant potential in enhancing various NLP tasks. Its adaptability in information extraction tasks, sentiment analysis, and text classification showcases its ability to comprehend diverse contexts and extract meaningful details. Additionally, ChatGPT's flexibility in annotation tasks reduces manual efforts and accelerates the annotation process, making it a valuable asset in NLP development and research. Furthermore, GPT-4 and prompt engineering emerge as a complementary mechanism, empowering users to guide the model and enhance overall accuracy. Despite its promising potential, challenges persist. The performance of ChatGPT needs to be tested using more extensive datasets and diverse data structures. Subsequently, its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues. [ABSTRACT FROM AUTHOR]

Claims that GPT-4 can outperform more than 90% of human test-takers in the US Uniform Bar Examination have sparked heated debates about the impact of Generative AI (GenAI) on legal education, academic integrity, and the future of legal practice. Yet GenAI’s capabilities in broader legal examination contexts – including in jurisdictions outside the US – are unclear. This study addresses this gap by evaluating GenAI’s performance against students who took the ‘Criminal Law’ final exam at an Australian law school in Spring 2023. Various AI models and prompt engineering techniques were used to generate 10 distinct answers to the exam question. Five criminal law tutors, unaware of AI involvement, graded a mix of AI-generated and student responses. Then, the tutors were briefed on the AI-generated papers they marked and engaged in reflective semi-structured interviews. The study found that GenAI performed below the student average in questions that required detailed legal and critical analysis. However, all GenAI papers performed better than students in open-ended questions and essay writing tasks. These results provide a benchmark for the capabilities and limitations of GenAI in higher education and provide insights into the potential implications of its application to legal assessments and education, curriculum development, and the future workforce. [ABSTRACT FROM AUTHOR]

Backgrounds: The use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making. Methods: We collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease's clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated 'good' responses, 6 ≤ TS ≤ 9 indicated 'moderate' responses, and TS < 6 indicated 'poor' responses. Results: In response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before. Conclusions: Our study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini. [ABSTRACT FROM AUTHOR]

This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three Master's‐level exams were scored using ChatGPT 3.5, and the results were compared with the teachers' scoring and the grading teachers were interviewed. In total, 463 exam responses were graded. With each response being graded at least three times, a total of 1389 gradings were conducted. For the final exam scores, 70% of ChatGPT's gradings were within 10% of the teachers' gradings and 31% within 5%. ChatGPT tended to give marginally higher scores. The agreement on grades is 30%, but 45% of the exams received an adjacent grade. On individual questions, ChatGPT is more inclined to avoid very high or very low scores. ChatGPT struggles to correctly score questions closely related to the course lectures but performs better on more general questions. The AI can generate plausible scores on university exams that, at first glance, look similar to a human grader. There are differences but it is not unlikely that two different human graders could result in similar discrepancies. During the interviews, teachers expressed their surprise at how well ChatGPT's grading matched their own. Increased use of AI can lead to ethical challenges as exams are entrusted to a machine whose decision‐making criteria are not fully understood, especially concerning potential bias in training data. [ABSTRACT FROM AUTHOR]

AbstractChatGPT, a type of generative artificial intelligence, recently became the fastest-growing app of all time, with over 100 million users. The increasing use of this chatbot has led educators to fear that its use will cause various problems, including cheating. However, using ChatGPT can lead to desirable outcomes. Teachers can use this technology to make their tasks easier to complete and to encourage students to think critically. This article focuses on how educators can implement ChatGPT so that students benefit. Three strategies are included to achieve this goal. The first is to prohibit the use of ChatGPT when its use will likely impede learning. The second focuses on using this chatbot to encourage students to learn and think critically, and the third involves the importance of implementing an ethical approach. [ABSTRACT FROM AUTHOR]

A national curriculum cannot be uniformly applied in all classrooms. Educators frequently adapt the official curriculum to suit their particular circumstances. In exploring the interplay between artificial intelligence (AI) technologies and curriculum adaptation in education, this study bridges a significant gap in the literature by exploring how AI tools influence teachers' strategies for adapting curricula. Employing an explanatory sequential design, the research analyses both qualitative and quantitative data from 440 teachers, using the Curriculum Adaptation Patterns Scale and focus group semi‐structured interviews. Results indicate a balanced approach among teachers towards extending and revising the curriculum, with less emphasis on omission. Notably, curriculum adaptation practices evolve positively with increased professional experience, differ across disciplines, but remain constant across school levels and educational levels. Qualitatively, teachers reported positive experiences using AI, particularly ChatGPT, to make their lessons better fit students' needs. They've used it to omit parts that aren't needed, add more relevant and personalised content, and revise or replace the content. The findings highlight AI's transformative potential in curriculum adaptation, making education more engaging, relevant and personalised. The study contributes to understanding how AI can support effective curriculum implementation and enhance learning experiences in the digital age. [ABSTRACT FROM AUTHOR]

AbstractArtificial intelligence (AI) tools are becoming increasingly prevalent in various fields, including foreign language education. Understanding foreign language students’ and teachers’ perceptions, experiences, ideas, and suggestions is crucial for effectively integrating these tools into language learning. To this end, this case study examines the perceptions of 14 English as a Foreign Language instructors and 13 students regarding the integration of ChatGPT in a foreign language school over a year. Data were collected through focus groups and one-to-one interviews and analyzed using reflexive thematic analysis. The findings suggest that generative AI chatbots can enhance language skills, offer diverse learning materials, and foster inclusiveness and equity in foreign language learning. However, concerns were raised about plagiarism and potential negative impacts on students’ cognitive skills. [ABSTRACT FROM AUTHOR]

Chat Generative Pretrained Transformer (ChatGPT; OpenAI) is a state-of-the-art large language model that can simulate human-like conversations based on user input. We evaluated the performance of GPT-4 V in the Japanese National Clinical Engineer Licensing Examination using 2,155 questions from 2012 to 2023. The average correct answer rate for all questions was 86.0%. In particular, clinical medicine, basic medicine, medical materials, biological properties, and mechanical engineering achieved a correct response rate of ≥ 90%. Conversely, medical device safety management, electrical and electronic engineering, and extracorporeal circulation obtained low correct answer rates ranging from 64.8% to 76.5%. The correct answer rates for questions that included figures/tables, required numerical calculation, figure/table ∩ calculation, and knowledge of Japanese Industrial Standards were 55.2%, 85.8%, 64.2% and 31.0%, respectively. The reason for the low correct answer rates is that ChatGPT lacked recognition of the images and knowledge of standards and laws. This study concludes that careful attention is required when using ChatGPT because several of its explanations lack the correct description. [ABSTRACT FROM AUTHOR]

AbstractSince the launch of ChatGPT in November 2022, the number of services providing generative AI has been steadily increasing. As different services enter the market, the generative AI interfaces users experience become more diverse. However, none of the services has yet established itself as the dominant tool, and which interface component affects user preferences the most has yet to be identified. This study investigates user preferences based on the interface of generative AI services currently on the market. We investigated users’ preferred interface components by setting the output data type, generative style, output variation, reference style, and generation history provided by the current generative AI service as properties. We collected data from 500 users through a survey and conducted conjoint analysis. Users preferred the provision of 10 generation histories the most, and the second most preferred the provision of reference style in footnote format. In addition, it was found that there was no preference for the creative generative style, which can be interpreted as users being aware of the problem of hallucination in generative AI. The results of this study will help future generative AI services design interfaces that consider user experience. [ABSTRACT FROM AUTHOR]

The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used generative AI tools, ChatGPT, Bing and Bard. Each AI tool was prompted with an essay question from undergraduate (UG) level 4 (year 1), level 5 (year 2), level 6 (year 3) and postgraduate (PG) level 7 biomedical sciences courses. Anonymised AI generated output was then evaluated by four independent markers, according to specifed marking criteria and matched to the Frameworks for Higher Education Qualifcations (FHEQ) of UK level descriptors. Percentage scores and ordinal grades were given for each marking criteria across AI generated papers, inter-rater reliability was calculated using Kendall's coefcient of concordance and generative AI performance ranked. Across all UG and PG levels, ChatGPT performed better than Bing or Bard in areas of scientifc accuracy, scientifc detail and context. All AI tools performed consistently well at PG level compared to UG level, although only ChatGPT consistently met levels of high attainment at all UG levels. ChatGPT and Bing did not provide adequate references, while Bing falsifed references. In conclusion, generative AI tools are useful for providing scientifc information consistent with the academic standards required of students in written assignments. These fndings have broad implications for the design, implementation and grading of written assessments in higher education. [ABSTRACT FROM AUTHOR]

AbstractThis article reports on three digital information literacy initiatives created by instruction librarians to support students’ use of generative AI technologies, namely ChatGPT, in academic library research. The cumulative and formative data gathered from the initiatives reveals a continuing need for academic libraries to provide information literacy instruction that guides students toward the ethical use of information and awareness of using generative AI tools in library research. Moreover, analysis of the cumulative data gathered from Mentimeter anonymous surveys has led to the further reshaping of our existing information literacy program as students’ AI literacy develops in their use of AI tools at California State University, Los Angeles. [ABSTRACT FROM AUTHOR]

Objective: The integration of artificial intelligence (AI) in healthcare education is inevitable. Understanding the proficiency of generative AI in different languages to answer complex questions is crucial for educational purposes. The study objective was to compare the performance ChatGPT-4 and Gemini in answering Virology multiple-choice questions (MCQs) in English and Arabic, while assessing the quality of the generated content. Both AI models' responses to 40 Virology MCQs were assessed for correctness and quality based on the CLEAR tool designed for evaluation of AI-generated content. The MCQs were classified into lower and higher cognitive categories based on the revised Bloom's taxonomy. The study design considered the METRICS checklist for the design and reporting of generative AI-based studies in healthcare. Results: ChatGPT-4 and Gemini performed better in English compared to Arabic, with ChatGPT-4 consistently surpassing Gemini in correctness and CLEAR scores. ChatGPT-4 led Gemini with 80% vs. 62.5% correctness in English compared to 65% vs. 55% in Arabic. For both AI models, superior performance in lower cognitive domains was reported. Both ChatGPT-4 and Gemini exhibited potential in educational applications; nevertheless, their performance varied across languages highlighting the importance of continued development to ensure the effective AI integration in healthcare education globally. [ABSTRACT FROM AUTHOR]

In the evolving landscape of teacher education, where generative AI poses both opportunities and challenges, this article investigates the resurgence of orality through podcasting as a pedagogical tool. Situated within a teacher education program, the study focuses on teacher candidates who are navigating the complexities of educational technologies. It examines the role of student created podcasts for enhancing learning experiences, fostering collaborative communities, and developing essential teaching skills. Drawing on Walter Ong's theory of orality and literacy, this phenomenological research explores how a digital return to orality can effectively counterbalance the impersonal nature of AI-generated content in education. The study argues that podcasting embodies human craftsmanship, revitalizes oral traditions in learning, and equips future educators with innovative pedagogical strategies in an increasingly digital academic environment. [ABSTRACT FROM AUTHOR]

Large language models like Chat Generative Pre-Trained Transformer (ChatGPT) are increasingly used in academic writing. Faculty may consider use of artificial intelligence (AI)–generated responses a form of cheating. We sought to determine whether general surgery residency faculty could detect AI versus human-written responses to a text prompt; hypothesizing that faculty would not be able to reliably differentiate AI versus human-written responses. Ten essays were generated using a text prompt, "Tell us in 1-2 paragraphs why you are considering the University of Rochester for General Surgery residency" (Current trainees: n = 5, ChatGPT: n = 5). Ten blinded faculty reviewers rated essays (ten-point Likert scale) on the following criteria: desire to interview, relevance to the general surgery residency, overall impression, and AI- or human-generated; with scores and identification error rates compared between the groups. There were no differences between groups for %total points (ChatGPT 66.0 ± 13.5%, human 70.0 ± 23.0%, P = 0.508) or identification error rates (ChatGPT 40.0 ± 35.0%, human 20.0 ± 30.0%, P = 0.175). Except for one, all essays were identified incorrectly by at least two reviewers. Essays identified as human-generated received higher overall impression scores (area under the curve: 0.82 ± 0.04, P < 0.01). Whether use of AI tools for academic purposes should constitute academic dishonesty is controversial. We demonstrate that human and AI-generated essays are similar in quality, but there is bias against presumed AI-generated essays. Faculty are not able to reliably differentiate human from AI-generated essays, thus bias may be misdirected. AI-tools are becoming ubiquitous and their use is not easily detected. Faculty must expect these tools to play increasing roles in medical education. [ABSTRACT FROM AUTHOR]

Introduction: In light of the growing interest in utilizing AI for information retrieval, assessing the accuracy and reliability of tools such as chatbots is essential. This study aims to evaluate the efficacy of chatbots in providing accurate information about mobile applications (apps) in the field of audiology. Material and methods: The responses of the Gemini and ChatGPT chatbots to eight open-ended questions posed in Polish and English were compared. Each answer was assessed for correctness. Results: Gemini_ENG achieved the highest correctness with a score of 5 points (62.5%), while ChatGPT_PL scored 2 points (25%), and both Gemini_PL and ChatGPT_ENG scored 1 point (12.5%). Chatbots were most effective in recommending apps for older adults, with three of the four chatbots providing accurate recommendations. However, they struggled when asked to recommend apps for non-English speakers, to describe apps, or to provide direct links, with none of them scoring points in these areas. Conclusions: Chatbots are currently unreliable sources of information about audiology apps. Depending on the language, there is significant variability in response accuracy. A good example is that Gemini_ENG performed far better than Gemini_PL. A major issue for all of them was the frequent fabrication of data, including the creation of nonexistent app names and incorrect links. [ABSTRACT FROM AUTHOR]

This study explored the perceived impact of integrating Generative Artificial Intelligence Systems (GenAI) such as ChatGPT into a postgraduate Master of Education program, drawing on the dual perspectives of participants who were students and educators teaching students at different levels of education in England. Using the Situated Expectancy‐Value Theory and Activity‐Centered Analysis and Design theoretical frameworks, the findings revealed opportunities, benefits, and challenges. Benefits included personalized learning and efficiencies in course design while the challenges consisted of ethical concerns, holistic skill development and problem solving. The surprising outcome of this study was the strong emotive anxiety around using AI and GenAI in participants' academic and personal lives. A balanced, critically reflective approach can help realize ChatGPT's educational potential while navigating implementation barriers and associated challenges. Participants insights can add to the dialogue negotiating ways of effectively integrating GenAI into the higher education curriculum. Practical Takeaways: Foster collaboration: Educators and students should work together to develop critical evaluation skills, ethical frameworks, and adaptive strategies for responsible GenAI use.Prioritize ethical considerations: Promote ongoing reflection on the personal and academic implications of GenAI, including potential biases and impacts on equity.Balance innovation with traditional skills: Emphasize that GenAI augments, rather than replaces, core academic skills like critical thinking, source verification, and effective communication.Align with student values: To sustain engagement, connect GenAI's use with students' immediate goals, future aspirations, and the potential to improve their learning experiences and outcomes. [ABSTRACT FROM AUTHOR]

The adoption of large language models (LLMs) in education holds much promise. However, like many technological innovations before them, adoption and access can often be inequitable from the outset, creating more divides than they bridge. In this paper, we explore the magnitude of the country and language divide in the leading open‐source and proprietary LLMs with respect to knowledge of K‐12 taxonomies in a variety of countries and their performance on tagging problem content with the appropriate skill from a taxonomy, an important task for aligning open educational resources and tutoring content with state curricula. We also experiment with approaches to narrowing the performance divide by enhancing LLM skill tagging performance across four countries (the USA, Ireland, South Korea and India–Maharashtra) for more equitable outcomes. We observe considerable performance disparities not only with non‐English languages but with English and non‐US taxonomies. Our findings demonstrate that fine‐tuning GPT‐3.5 with a few labelled examples can improve its proficiency in tagging problems with relevant skills or standards, even for countries and languages that are underrepresented during training. Furthermore, the fine‐tuning results show the potential viability of GPT as a multilingual skill classifier. Using both an open‐source model, Llama2‐13B, and a closed‐source model, GPT‐3.5, we also observe large disparities in tagging performance between the two and find that fine‐tuning and skill information in the prompt improve both, but the closed‐source model improves to a much greater extent. Our study contributes to the first empirical results on mitigating disparities across countries and languages with LLMs in an educational context. Practitioner notesWhat is already known about this topic Recent advances in generative AI have led to increased applications of LLMs in education, offering diverse opportunities.LLMs excel predominantly in English and exhibit a bias towards the US context.Automated content tagging has been studied using English‐language content and taxonomies.What this paper adds Investigates the country and language disparities in LLMs concerning knowledge of educational taxonomies and their performance in tagging content.Presents the first empirical findings on addressing disparities in LLM performance across countries and languages within an educational context.Improves GPT‐3.5's tagging accuracy through fine‐tuning, even for non‐US countries, starting from zero accuracy.Extends automated content tagging to non‐English languages using both open‐source and closed‐source LLMs.Implications for practice and/or policy Underscores the importance of considering the performance generalizability of LLMs to languages other than English.Highlights the potential viability of ChatGPT as a skill tagging classifier across countries. [ABSTRACT FROM AUTHOR]

ChatGPT is a language model that was trained on a large dataset including medical literature. Several studies have described the performance of ChatGPT on medical exams. In this study, we examine its performance in answering factual knowledge questions regarding clinical pharmacy. Questions were obtained from a Dutch application that features multiple‐choice questions to maintain a basic knowledge level for clinical pharmacists. In total, 264 clinical pharmacy‐related questions were presented to ChatGPT and responses were evaluated for accuracy, concordance, quality of the substantiation, and reproducibility. Accuracy was defined as the correctness of the answer, and results were compared to the overall score by pharmacists over 2022. Responses were marked concordant if no contradictions were present. The quality of the substantiation was graded by two independent pharmacists using a 4‐point scale. Reproducibility was established by presenting questions multiple times and on various days. ChatGPT yielded accurate responses for 79% of the questions, surpassing pharmacists' accuracy of 66%. Concordance was 95%, and the quality of the substantiation was deemed good or excellent for 73% of the questions. Reproducibility was consistently high, both within day and between days (>92%), as well as across different users. ChatGPT demonstrated a higher accuracy and reproducibility to factual knowledge questions related to clinical pharmacy practice than pharmacists. Consequently, we posit that ChatGPT could serve as a valuable resource to pharmacists. We hope the technology will further improve, which may lead to enhanced future performance. [ABSTRACT FROM AUTHOR]

Health sciences instructors hold a wide range of opinions about generative artificial intelligence (GenAI) such as ChatGPT, Bing, and Bard; however, many are uncertain about guiding students on how to use technology for assigned writing. Our survey of 62 public health instructors at a single institution highlighted their perceived benefits, limitations, and concerns about student use of GenAI for assigned writing. Perceived benefits included the completion of tasks unrelated to relevant learning such as spellchecking and reference formatting, as well as for certain writing activities such as brainstorming. Several identified the preparation for future workplace activities as a meaningful benefit. Important limitations and concerns included the worry that GenAI would inhibit learning, as well as ethical and equity-related concerns. Nearly half of instructors expressed concerns about whether using GenAI tools constitutes plagiarism or violates academic integrity. Nearly half of instructors also indicated concern about being able to detect whether a student completed an assignment with GenAI tools. Developing thoughtful guidance on technology use for assigned writing is important as it sets standards for academic integrity and supports learning. We used the survey data and applied backward design principles to develop the Brave New Words framework and three-step process described in this paper. This framework is intended to help instructors think through and ultimately develop guidelines for students on whether and how they should use technology for assigned writing. An example assignment and activity are used to demonstrate the framework. [ABSTRACT FROM AUTHOR]

Despite increased recognition of the importance and need for pedagogical training for public health and health promotion instructors in best-practices and inclusivity, formal training is often overlooked. This disregard for pedagogical training necessitates exploration of alternative and innovative approaches to enhance teaching and learning such as generative AI. This paper describes applied uses of generative AI, specifically ChatGPT, to enhance pedagogy in public health and health promotion education in the areas of curriculum design, instructional strategies, assessment and feedback, and diversity, equity, and inclusion. Generative AI as a supplemental tool shows immense promise for improving teaching and learning, however, inherent limitations and ethical considerations require caution and continued scrutiny. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (genAI) technology is used among students, yet it remains unclear how public health students and instructors perceive it to be effective in a learning environment. We described how and why public health students and instructors are using genAI technology along with their perceived benefits and limitations of using genAI, noting where perceptions overlap. We surveyed public health students and instructors at a higher education institution in the United States. Student survey questions covered which genAI technologies they used, which activities they used genAI for, and perceived benefits and limitations of using genAI. Questions for instructors covered which genAI technology they used, course activities genAI was integrated, and perceived benefits and limitations of using genAI. Student respondents (n = 300) indicated using genAI technology for writing or clarifying concepts. Students and instructors (n = 62) agreed genAI technology could save time on tedious tasks and will be part of our future workforce. They agreed that appropriate use in the classroom will better prepare future professionals. Alternatively, students and instructors indicated genAI may impede learning, produce inaccurate information, and pose opportunities for unethical behavior. While students and instructors agree on many aspects of genAI technology, instructors should be explicit about their expectations and rationale for use of genAI technology in classrooms. [ABSTRACT FROM AUTHOR]

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a "grand challenge," thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a "weapon of mass deception" (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of hu mans in ensuring the proper use of ChatGPT for individual and social wellbeing. [ABSTRACT FROM AUTHOR]

In addition to the popular search engines on the Internet, ChatGPT may provide accurate and reliable health information. The aim of this study was to examine whether ChatGPT's responses to frequently asked questions concerning cerebral palsy (CP) by families were reliable and useful. Google trends were used to find the most frequently searched keywords for CP. Five independent physiatrists assessed ChatGPT responses to 10 questions. Seven-point Likert-type scales were used to rate information reliability and usefulness based on whether the answer can be validated and is understandable. The median ratings for reliability of information for each question varied from 2 (very unsafe) to 5 (relatively very reliable). The median rating was 4 (reliable) for four questions. The median ratings for usefulness of information varied from 2 (very little useful) to 5 (moderately useful). The median rating was 4 (partly useful) for seven questions. Although ChatGPT appears promising as an additional tool for informing family members of individuals with CP about medical information, it should be emphasized that both consumers and health care providers should be aware of the limitations of artificial intelligence-generated information. [ABSTRACT FROM AUTHOR]

The advent of Chat Generative Pre-Trained Transformer (ChatGPT), a conversational artificial intelligence application, has sparked considerable interest and debate within the educational sphere. This narrative article explores the potential implications of ChatGPT on dental education, including its applications, challenges, and ethical considerations. In the realm of dental school admissions, the use of ChatGPT raises questions about academic integrity and the equitable access to resources for applicants. While it may offer benefits in aiding students with writer's block, concerns arise regarding plagiarism and the authenticity of personal statements. Furthermore, its impact on the learning experiences of dental students presents both opportunities and challenges. While ChatGPT can serve as a helpful learning tool, its use may also hinder the development of critical thinking skills and lead to issues of academic dishonesty. In the realm of research, ChatGPT holds promise for facilitating literature analysis, question-answering, and information retrieval. However, its limitations, such as the lack of domain-specific knowledge and potential for bias, must be considered. Ethical concerns surrounding AI-generated content, including misinformation and transparency, necessitate careful regulation and oversight. Looking ahead, the integration of generative AI models into dental education requires collaborative efforts to establish best practices and ethical guidelines. Future research should focus on developing tailored AI models specific to dental education and investigating their long-term effects on learning outcomes and patient care. Ultimately, as educators, it is imperative to embrace technological advancements while ensuring responsible and equitable integration into dental education curricula. [ABSTRACT FROM AUTHOR]

Numerous techniques, including problem-solving, seeking clarification, and creating questions, have been employed to utilize generative Artificial Intelligence (AI) in education. This study investigates the possibility of using Generate AI (GenAI) to grade Structured Query Language (SQL) queries automatically. Three models were used which are ChatGPT, Gemini, and Copilot. The study uses an experimental approach to assess how well the models perform in evaluating student responses by comparing the models' accuracy with those of human experts. The results showed that despite some inconsistencies, GenAI holds great promise for streamlining. Thus, further research is required in light of inconsistent GenAI performance. If these issues were resolved, GenAI can be utilized in education. However, human oversight and ethical issues must always come first. [ABSTRACT FROM AUTHOR]

Since the 1990s, Singapore has sought to project itself as innovative and technologically cutting-edge through narratives such as 'intelligent island', digital ecosystem', and more recently, 'smart nation'. The Smart Nation initiative is aimed at digitising and, 'datafying' as many public services as possible. To this end, the government launched a National Artificial Intelligence Strategy in 2019 that envisions Singapore as a global hub for developing, test-bedding, deploying, and scaling AI solutions. In late 2023, the same year Generative AI entered public consciousness thanks to ChatGPT, Version 2.0 of Singapore's AI strategy was released. Providing a commentary of Singapore's digital journey and its governing approaches, this paper contends that AI is more than just a new digital tool for Singaporeans; it is also a proxy for Singapore's digital and 'smart nation' global reputation. [ABSTRACT FROM AUTHOR]

The rise of generative AI programmes like ChatGPT, Gemini, and Midjourney has generated both fascination and apprehension in society. While the possibilities of generative AI seem boundless, concerns about ethical violations, disinformation, and job displacement have ignited anxieties. The Singapore government established the AI Verify Foundation in June 2023 to address these issues in collaboration with major tech companies like Aicadium, Google, IBM, IMDA, Microsoft, Red Hat, and Salesforce, alongside numerous general members. This public-private partnership aims to promote the development and adoption of an open-source testing tool for fostering responsible AI usage through engaging the global community. The foundation also seeks to promote AI testing via education, outreach, and as a neutral platform for collaboration. This initiative reflects a potential governance model for AI that balances public interests with commercial agendas. This article analyses the foundation's efforts and the AI Verify testing framework and toolkit to identify strengths, potential and limitations to distil key takeaways for establishing practicable solutions for AI governance. [ABSTRACT FROM AUTHOR]

Generative AI (GenAI) is a technology that, since the launch of ChatGPT in November 2022, has taken the world by storm. While a lot of the conversation around GenAI is hype, there are some real applications of this technology that can bring real value to businesses. There are, however, risks in applying this technology blindly that sometimes can outweigh the value it brings. This paper discusses the potential applicability of GenAI to the processes in post-trade and what impact it could have on financial institutions and their ability to meet challenges in the market, such as T+1. We also discuss the risks of implementing this technology and how these can be mitigated, as well as ensuring that all the objectives are met not only from a business perspective, but also technology and compliance. [ABSTRACT FROM AUTHOR]

Since ChatGPT exploded onto the scene in October 2022, the media has been breathless with their discussion about how it is going to impact many aspects of life and work. In order to bring this new reality of generative AI into the Introduction to Management Information Systems classroom at both the undergraduate and graduate levels, we have developed two short cases that can be completed in or out of the classroom. These cases allow students to use generative AI for an interesting and useful purpose and develop prompts to deliver the output. Students can begin to learn what generative AI can do for them, and how it can shape their future careers. We tested the perceived effectiveness of the project on students using a pre/post survey. The results are analyzed using a paired t-test and demonstrate that students increased their understanding of generative AI and prompt engineering. [ABSTRACT FROM AUTHOR]

This study explores the polarization of news content shared on Facebook compared with email using data from the New York Times' Most Emailed and Most Shared lists over 2.5 years. Employing latent Dirichlet allocation and large language models (LLMs), we find that highly polarized articles are more likely to be shared on Facebook (versus email), even after accounting for factors like topics, emotion, and article age. Additionally, distinct topic preferences emerge, with social issues dominating Facebook shares and lifestyle topics prevalent in emails. Contrary to expectations, political polarization of articles shared on Facebook did not escalate post-2020 election. We introduce a novel approach to measuring polarization of text content that leverages generative artificial intelligence models, like ChatGPT, and it is both scalable and cost effective. This research contributes to the evolving intersection of LLMs, social media, and polarization studies, shedding light on descriptive patterns of content dissemination across different digital channels. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04134. [ABSTRACT FROM AUTHOR]

The significant advancements in the capabilities, reasoning, and efficiency of artificial intelligence (AI)-based tools and systems are evident. Some noteworthy examples of such tools include generative AI-based large language models (LLMs) such as generative pretrained transformer 3.5 (GPT 3.5), generative pretrained transformer 4 (GPT-4), and Bard. LLMs are versatile and effective for various tasks such as composing poetry, writing codes, generating essays, and solving puzzles. Thus far, LLMs can only effectively process text-based input. However, recent advancements have enabled them to handle multimodal inputs, such as text, images, and audio, making them highly general-purpose tools. LLMs have achieved decent performance in pattern recognition tasks (such as classification), therefore, there is a curiosity about whether general-purpose LLMs can perform comparable or even superior to specialized deep learning models (DLMs) trained specifically for a given task. In this study, we compared the performances of fine-tuned DLMs with those of general-purpose LLMs for image-based emotion recognition. We trained DLMs, namely, a convolutional neural network (CNN) (two CNN models were used: C N N 1 and C N N 2 ), ResNet50, and VGG-16 models, using an image dataset for emotion recognition, and then tested their performance on another dataset. Subsequently, we subjected the same testing dataset to two vision-enabled LLMs (LLaVa and GPT-4). The C N N 2 was found to be the superior model with an accuracy of 62% while VGG16 produced the lowest accuracy with 31%. In the category of LLMs, GPT-4 performed the best, with an accuracy of 55.81%. LLava LLM had a higher accuracy than C N N 1 and VGG16 models. The other performance metrics such as precision, recall, and F1-score followed similar trends. However, GPT-4 performed the best with small datasets. The poor results observed in LLMs can be attributed to their general-purpose nature, which, despite extensive pretraining, may not fully capture the features required for specific tasks like emotion recognition in images as effectively as models fine-tuned for those tasks. The LLMs did not surpass specialized models but achieved comparable performance, making them a viable option for specific tasks without additional training. In addition, LLMs can be considered a good alternative when the available dataset is small. [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]

Copyright of Tourism Review is the property of Emerald Publishing Limited 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.)

One of the most common questions that students ask statistics advisors is 'What test should I do?' This paper explores the use of generative AI chatbots, specifically ChatGPT, as a tool to assist students, in particular those with limited experience in statistics, in selecting appropriate statistical tests for their analyses. Traditional methods, such as flowcharts and online test selectors, require at least a basic understanding of measurement scales and research design, which can be an issue for many students who have limited exposure to statistics on their courses. This research focuses on developing and refining prompts to guide ChatGPT in providing accurate and relevant statistical test recommendations. A hypothetical scenario was used to test the effectiveness of various prompts, ranging from simple, naïve questions to more sophisticated ones utilising specific prompt patterns, such as the 'context manager' and 'flipped interaction.' These patterns were selected to enhance the chatbot's responses and ensure the relevance and accuracy of the test suggestions. The findings suggest that while AI chatbots like ChatGPT can be a valuable resource for students, their effectiveness is highly dependent on the quality of the prompts used. The paper concludes with a discussion on the potential of these AI tools in educational settings, acknowledging the limitations of current technology and suggesting directions for future research and development. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions This study evaluated the accuracy, clinical concordance, and readability of the chatbot interface generative pretrained transformer (ChatGPT) 3.5 as a source of breast cancer information for patients.Twenty questions that patients are likely to ask ChatGPT were identified by breast cancer advocates. These were posed to ChatGPT 3.5 in July 2023 and were repeated three times. Responses were graded in two domains: accuracy (4‐point Likert scale, 4 = worst) and clinical concordance (information is clinically similar to physician response; 5‐point Likert scale, 5 = not similar at all). The concordance of responses with repetition was estimated using intraclass correlation coefficient (ICC) of word counts. Response readability was calculated using the Flesch Kincaid readability scale. References were requested and verified.The overall average accuracy was 1.88 (range 1.0–3.0; 95% confidence interval [CI], 1.42–1.94), and clinical concordance was 2.79 (range 1.0–5.0; 95% CI, 1.94–3.64). The average word count was 310 words per response (range, 146–441 words per response) with high concordance (ICC, 0.75; 95% CI, 0.59–0.91; p < .001). The average readability was poor at 37.9 (range, 18.0–60.5) with high concordance (ICC, 0.73; 95% CI, 0.57–0.90; p < .001). There was a weak correlation between ease of readability and better clinical concordance (−0.15; p = .025). Accuracy did not correlate with readability (0.05; p = .079). The average number of references was 1.97 (range, 1–4; total, 119). ChatGPT cited peer‐reviewed articles only once and often referenced nonexistent websites (41%).Because ChatGPT 3.5 responses were incorrect 24% of the time and did not provide real references 41% of the time, patients should be cautioned about using ChatGPT for medical information. [ABSTRACT FROM AUTHOR]