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The Thomas Mahaffey Jr. Business Library conducted a study to assess the performance of two competing generative AI products, ChatGPT and Perplexity AI, in answering business reference questions. The study used a data set consisting of a sample of anonymized reference questions submitted through the library's ServiceNow ticketing system between January 2018 and May 2022. The questions were input as prompts to each competing AI. Responses were collected and evaluated by their performance in four separate dimensions relevant to business reference: accessibility, library referral, quality, and serendipity. Each dimension was scored on a 0-5 Likert scale resulting in a final composite performance score for each AI. Results showed similar and underwhelming performance between each AI at the composite level. Analysis of scores in each individual scoring dimension showed greater variance in the score distributions between the competing AI. Through the evaluation process, key strengths, weaknesses, and trends emerged between each AI. The study provides a quantitative measure of where generative AI stands in its capabilities in a business library reference context, and it recommends, based on the results of the evaluation, making use of generative AI in its current iteration as a supplementary tool for business reference as opposed to considering it as a replacement. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence in academia is a hot topic in the education field. ChatGPT is an AI tool that offers a range of benefits, including increased student engagement, collaboration, and accessibility. However, is also raises concerns regarding academic honesty and plagiarism. This paper examines the opportunities and challenges of using ChatGPT in higher education, and discusses the potential risks and rewards of these tools. The paper also considers the difficulties of detecting and preventing academic dishonesty, and suggests strategies that universities can adopt to ensure ethical and responsible use of these tools. These strategies include developing policies and procedures, providing training and support, and using various methods to detect and prevent cheating. The paper concludes that while the use of AI in higher education presents both opportunities and challenges, universities can effectively address these concerns by taking a proactive and ethical approach to the use of these tools. [ABSTRACT FROM AUTHOR]

AbstractIn this study, an online contextualized educational game was designed to provide interactive simulated dialogues using generative AI scaffolding (using ChatPDF) in a contextualized game as scaffolding for immediate feedback, where learners can access guides and explore knowledge. This study analyzed learners’ behaviors while performing AI prompting in the interactive scaffolding, as well as learners’ psychological responses. A total of 59 students participated in this study. The results of the study showed that learners had significantly high flow and low activity anxiety in the game tasks, while game feedback and scaffolding usefulness had significant effects on learning aids. The generative AI instant feedback interactive scaffolding had a certain high percentage of direct answers or indirect suggestions, which is suitable for interactive scaffolding. [ABSTRACT FROM AUTHOR]

This paper examines the potential impact of AI-based language models on education, with a specific focus on the ChatGPT model developed by OpenAI. The study begins by providing an overview of the current state of AI in education, including the challenges and opportunities presented by these models. It then delves into an analysis of ChatGPT, including its capabilities, strengths and limitations. The paper also conducts a thorough analysis of potential implications and offers suggestions for assessment design to prevent students from utilising AI-based language models in educational environments. Finally, the paper concludes with a set of recommendations for assessment design, considering the specific characteristics of ChatGPT and the potential impact of AI-based language models on tourism education. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (AI) is a rapidly expanding field that aims to develop machines capable of performing tasks that were previously considered unique to humans, such as learning, reasoning, problem-solving, and decision-making. The recent release of several tools based on AI (e.g. ChatGPT) has sparked debates on the potential of this technology and garnered widespread attention in the mainstream media.Using a socio-psychological approach, in three studies (total N = 410), we demonstrate that when faced with Generative AI’s ability to reproduce the complexity of human cognitive capabilities, participants reported significantly higher negative emotions than those in the control group. In turn, negative emotions elicited by a specific type of AI (e.g. generative AI) were associated to the perception of threat extended to AI technologies as a whole, understood as a threat to various aspects of human life, including jobs, resources, identity, uniqueness, and value.Our findings emphasise the importance of considering emotional and societal impacts when developing and deploying advanced AI technologies and implementing responsible guidelines to minimise adverse effects. As AI technology advances, addressing public concerns and regulating its usage is crucial for the benefit of society. To achieve this goal, collaboration between experts, policymakers, and the public is necessary. [ABSTRACT FROM AUTHOR]

This paper examines ChatGPT’s capability in evaluating educational learning outcomes, investigating its effectiveness in assessing SMART criteria alignment and identifying the presence of fundamental components. The hypothesis posits that ChatGPT can proficiently accomplish these tasks, offering potential benefits to educational design and assessment processes. Through exemplar learning outcomes, the study showcases ChatGPT’s ability to discern SMART criteria alignment. Furthermore, it demonstrates ChatGPT’s competence in identifying the fundamental components, substantiated by cogent explanations. The analysis underscores the congruence between ChatGPT’s evaluations and human assessors’ judgements, underscoring its potential utility in educational quality assurance. Implications for educational practice emphasise ChatGPT’s potential to assist educators in formulating effective learning objectives, meeting SMART criteria and encapsulating crucial components. While ChatGPT’s capabilities are promising, human expertise remains vital for nuanced evaluation. In conclusion, this paper illuminates ChatGPT’s role in shaping educational outcomes and encourages further exploration into AI’s potential impact on educational processes. [ABSTRACT FROM AUTHOR]

AbstractOpenAI’s ChatGPT is an advanced large language model AI that has caused both excitement and concern in academia. TurnItIn and similar software programs are widely used in higher education to detect potential plagiarism. However, the extent to which such software can identify papers produced by ChatGPT remains unclear. The current study partially addressed this question by submitting five versions of short essays about criminological topics generated from ChatGPT to the TurnItIn software. Overall, the results indicated that TurnItIn adequately detected that the essays were not original works (mean percent plagiarized score = 31%). The analyses further illustrated that ChatGPT wrote at an exceedingly high level (mean Flesch-Kincaid Grade Level = 15.1) atypical of essays in higher education. Consequently, it appears that detection of AI-generated writing may be easier than initially assumed although the technology is bound to improve. Accordingly, possible strategies for approaching AI in higher education are proposed. [ABSTRACT FROM AUTHOR]

PurposeMethodsResultsConclusionsPatients are using online search modalities to learn about their eye health. While Google remains the most popular search engine, the use of large language models (LLMs) like ChatGPT has increased. Cataract surgery is the most common surgical procedure in the US, and there is limited data on the quality of online information that populates after searches related to cataract surgery on search engines such as Google and LLM platforms such as ChatGPT. We identified the most common patient frequently asked questions (FAQs) about cataracts and cataract surgery and evaluated the accuracy, safety, and readability of the answers to these questions provided by both Google and ChatGPT. We demonstrated the utility of ChatGPT in writing notes and creating patient education materials.The top 20 FAQs related to cataracts and cataract surgery were recorded from Google. Responses to the questions provided by Google and ChatGPT were evaluated by a panel of ophthalmologists for accuracy and safety. Evaluators were also asked to distinguish between Google and LLM chatbot answers. Five validated readability indices were used to assess the readability of responses. ChatGPT was instructed to generate operative notes, post-operative instructions, and customizable patient education materials according to specific readability criteria.Responses to 20 patient FAQs generated by ChatGPT were significantly longer and written at a higher reading level than responses provided by Google (p < .001), with an average grade level of 14.8 (college level). Expert reviewers were correctly able to distinguish between a human-reviewed and chatbot generated response an average of 31% of the time. Google answers contained incorrect or inappropriate material 27% of the time, compared with 6% of LLM generated answers (p < .001). When expert reviewers were asked to compare the responses directly, chatbot responses were favored (66%).When comparing the responses to patients’ cataract FAQs provided by ChatGPT and Google, practicing ophthalmologists overwhelming preferred ChatGPT responses. LLM chatbot responses were less likely to contain inaccurate information. ChatGPT represents a viable information source for eye health for patients with higher health literacy. ChatGPT may also be used by ophthalmologists to create customizable patient education materials for patients with varying health literacy. [ABSTRACT FROM AUTHOR]

AbstractProblem, research strategy, and findingsTakeaway for practicePlanners are increasingly using online public engagement approaches to broaden their reach in communities. This results in substantial volumes of digital, text-based public feedback data, making it difficult to analyze efficiently and derive meaningful insights. We explored the use of the novel large language model (LLM), ChatGPT, in analyzing a public feedback data set collected via online submissions in Hamilton City (New Zealand) in response to a proposed local plan change. Specifically, we initially employed zero-shot prompts with ChatGPT for tasks like summarizing, topic identification, and sentiment analysis and compared the results with those obtained by human planners and two standard natural language processing (NLP) techniques: latent Dirichlet allocation (LDA) topic modeling and lexicon-based sentiment analysis. The findings show that zero-shot prompting effectively identified political stances (accuracy: 81.7%), reasons (87.3%), decisions sought (85.8%), and associated sentiments (94.1%). Although subject to several limitations, ChatGPT demonstrates promise in automating the analysis of public feedback, offering substantial time and cost savings. In addition, few-shot prompting enhanced performance in more complex tasks, such as topic identification involving planning jargon. We also provide insights for urban planners to better harness the power of ChatGPT to analyze citizen feedback.ChatGPT presents a transformative opportunity for planners, particularly those dealing with growing volumes of public feedback data. However, it cannot be entirely relied upon. Planners must be mindful of ChatGPT’s limitations, including its sensitivity to prompt phrasing, inherent biases from training data, tendency to overgeneralize, and occasional omission of nuanced details. To enhance accuracy, planners should prescreen data for consistency, provide clear and iteratively tested prompts, use few-shot prompts for complex analysis, and explore various combinations of prompting strategies to develop an effective local approach. It is also crucial to ensure human review of the results. [ABSTRACT FROM AUTHOR]

AbstractIn the realm of generative artificial intelligence (AI), ChatGPT has garnered widespread acclaim for its remarkably realistic chatbot interactions. Despite its popularity, recent challenges in reliability have emerged. This research uniquely combines the Stimulus-Organism-Response (S-O-R) framework with dual-process theory to dissect the factors influencing ChatGPT’s credibility and popularity. Employing cross-sectional data from select social media platforms, the study identifies quality, accuracy, timeliness, audience familiarity, and congruence with other sources as pivotal elements shaping ChatGPT’s credibility. Significantly, this research reveals how users evaluate the reliability of generative AI-generated search results by comparing them to conventional methods. The study’s novel contribution lies in its exploration of ChatGPT as a medium of communication, offering valuable insights into understanding and enhancing the reliability of generative AI applications in the information landscape. [ABSTRACT FROM AUTHOR]

Artificial intelligence, such as ChatGPT, is advancing rapidly, driving significant transformations in various social areas such as education. This study aimed to divide the student group and test the acceptance of ChatGPT in different student groups through the established model. This study used college students as the research object and the latent class model to distinguish the AI digital ability of students. In this study, the sample of students was divided into groups of weak and strong AI digital based on the latent class model. Taking the whole sample, students with weak and strong digital ability as the research objects, the structural equation model of technology acceptance model and planned behavior theory were established to study the acceptance of ChatGPT among different student groups. The results showed significant heterogeneity in the acceptance of ChatGPT among the student groups. Across the whole sample, perceived behavioral control was the most important factor affecting students' acceptance of the ChatGPT. For students with weak AI digital ability, both attitude and perceived behavioral control play significant roles in their acceptance of ChatGPT. Students with strong AI digital ability are influenced by attitude, subjective norms, and perceived behavioral control when considering their acceptance of ChatGPT. [ABSTRACT FROM AUTHOR]

AbstractAimMethodsResultsConclusionsThis study aimed to evaluate the real-life performance of clinical vignettes and multiple-choice questions generated by using ChatGPT.This was a randomized controlled study in an evidence-based medicine training program. We randomly assigned seventy-four medical students to two groups. The ChatGPT group received ill-defined cases generated by ChatGPT, while the control group received human-written cases. At the end of the training, they evaluated the cases by rating 10 statements using a Likert scale. They also answered 15 multiple-choice questions (MCQs) generated by ChatGPT. The case evaluations of the two groups were compared. Some psychometric characteristics (item difficulty and point-biserial correlations) of the test were also reported.None of the scores in 10 statements regarding the cases showed a significant difference between the ChatGPT group and the control group (p > .05). In the test, only six MCQs had acceptable levels (higher than 0.30) of point-biserial correlation, and five items could be considered acceptable in classroom settings.The results showed that the quality of the vignettes are comparable to those created by human authors, and some multiple-questions have acceptable psychometric characteristics. ChatGPT has potential in generating clinical vignettes for teaching and MCQs for assessment in medical education. [ABSTRACT FROM AUTHOR]

There is an urgent need in the educational community to explore the potential impact of advanced language models powered by artificial intelligence, such as ChatGPT, on thinking skills and creativity. These models hold significant promise for enhancing teaching and learning, but it is crucial to investigate their potential effects on these critical areas of cognitive development. This study employs a narrative inquiry approach, utilising weekly written narratives and qualitative interviews for analysis, to explore and understand student teachers’ experiences, perceptions, and reflections on using ChatGPT in their teaching practices. Exploring the experiences and perspectives of 12 English student teachers, this research scrutinises the influence of ChatGPT on the development of thinking skills and creativity during the practicum part of a teacher education programme. Through narratives and interviews the study uncovers the benefits, challenges, and strategies for optimising the use of ChatGPT in pre-service language teacher education. Key findings reveal the importance of human-computer co-creation, engaging in critical analysis, and fostering collaboration to maximise the educational benefits of ChatGPT. These insights contribute to the ongoing discourse on the effective integration of AI in teacher education and offer practical recommendations for educators and students navigating the complex interplay between AI tools and independent thinking. [ABSTRACT FROM AUTHOR]

We amalgamate the capabilities of the GPT-4 computational model with the avant-garde methodology of autonomous factor generation, culminating in the synthesis of high-return factors within the equity investment milieu. Empirical outcomes elucidate that the factors conceptualized by ChatGPT attain a commendable Sharpe ratio peaking at 4.49, accompanied by an annualized return reaching 66.16%. Notably, the superlative excess returns garnered remain unaccounted for by the quintessential five-factor model. Through the implementation of an unembellished model averaging paradigm, the ensemble of 35 factors, conceived by ChatGPT, manifests an apex long-short annualized return of 88% and a Sharpe ratio registering at 2.46. In stark contrast to conventional data mining techniques, the temporal expenditure requisite for GPT's factor generation is minuscule. It relies on knowledge inference without the need for data input, and it can provide a thorough economic explanation for its factors. [ABSTRACT FROM AUTHOR]

The rapidly evolving digital landscape, punctuated by the emergence of artificial intelligence (AI) and immersive technologies, is poised to reshape learning environments dramatically. This study explores the potential use of ChatGPT, a state-of-the-art language model developed by OpenAI, in Metaverse learning environments. It sheds light on several opportunities ChatGPT can bring to Metaverse learning environments. This includes personalized learning, virtual teaching assistant, collaborative learning, language learning, accessibility, persistent education, simulations and role plays, and research assistant. In addition to these opportunities, several challenges were also identified. This includes technological issues, digital literacy, information accuracy, social interaction, privacy and security, ethical considerations, and AI dependence. Given these opportunities and challenges, the study identifies several agendas for future research. This fresh overview is believed to provide a roadmap for harnessing the potential of ChatGPT in Metaverse learning environments while navigating the associated challenges, setting the stage for a new era of digital learning in educational settings. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) such as ChatGPT-4 have raised critical questions regarding their distinguishability from human-generated content. In this research, we evaluated the effectiveness of online detection tools in identifying ChatGPT-4 vs human-written text. A two texts produced by ChatGPT-4 using differing prompts and one text created by a human author were analytically assessed using the following online detection tools: GPTZero, ZeroGPT, Writer ACD, and Originality. The findings revealed a notable variance in the detection capabilities of the employed detection tools. GPTZero and ZeroGPT exhibited inconsistent assessments regarding the AI-origin of the texts. Writer ACD predominantly identified texts as human-written, whereas Originality consistently recognized the AI-generated content in both samples from ChatGPT-4. This highlights Originality's enhanced sensitivity to patterns characteristic of AI-generated text. The study demonstrates that while automatic detection tools may discern texts generated by ChatGPT-4 significant variability exists in their accuracy. Undoubtedly, there is an urgent need for advanced detection tools to ensure the authenticity and integrity of content, especially in scientific and academic research. However, our findings underscore an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) might offer a solution for the lack of trained health personnel, particularly in low- and middle-income countries. However, their strengths and weaknesses remain unclear. Here we benchmark different LLMs (Bard 2023.07.13, Claude 2, ChatGPT 4) against six consultants in otorhinolaryngology (ORL). Case-based questions were extracted from literature and German state examinations. Answers from Bard 2023.07.13, Claude 2, ChatGPT 4, and six ORL consultants were rated blindly on a 6-point Likert-scale for medical adequacy, comprehensibility, coherence, and conciseness. Given answers were compared to validated answers and evaluated for hazards. A modified Turing test was performed and character counts were compared. LLMs answers ranked inferior to consultants in all categories. Yet, the difference between consultants and LLMs was marginal, with the clearest disparity in conciseness and the smallest in comprehensibility. Among LLMs Claude 2 was rated best in medical adequacy and conciseness. Consultants' answers matched the validated solution in 93% (228/246), ChatGPT 4 in 85% (35/41), Claude 2 in 78% (32/41), and Bard 2023.07.13 in 59% (24/41). Answers were rated as potentially hazardous in 10% (24/246) for ChatGPT 4, 14% (34/246) for Claude 2, 19% (46/264) for Bard 2023.07.13, and 6% (71/1230) for consultants. Despite consultants superior performance, LLMs show potential for clinical application in ORL. Future studies should assess their performance on larger scale. [ABSTRACT FROM AUTHOR]

Chatbots have transformed educational practices, and ChatGPT represents a significant development in this domain. However, more research is needed to determine how user perceptions influence the overall user experience with ChatGPT. To address this gap, this study investigates the role of ChatGPT, a revolutionary artificial intelligence – (AI)-powered chatbot, in educational settings by investigating its impact on user experience from the perspective of students. It focuses on key perceptions, including human-like personality, human-like empathy, and ChatGPT efforts, and their influence on the user experience. It also explores how ChatGPT’s efforts and usage depth affect user credibility, which in turn shapes the overall user experience. Data from 516 students who have used ChatGPT for academic assignments are collected through a questionnaire, and seemingly unrelated regression (SUR) is employed for analysis. The findings contribute to the literature on AI chatbots and user experience in three ways: by offering empirical insights into ChatGPT’s role in education, by identifying key perceptions and their impact on user experience, and by capturing multiple dimensions of user experience for the design and implementation of ChatGPT as an educational tool. By shedding light on the interplay between user perceptions and user experience, this study provides valuable guidance for educators and developers aiming to optimize the use of ChatGPT in educational contexts and to ensure its effectiveness as a trustworthy and supportive learning tool [ABSTRACT FROM AUTHOR]

ChatGPT’s capability to generate original content on any topic represents a concern for academic institutions, since students can plagiarise the AI-created content with a low probability of being caught. While prior research on academic misconduct emphasized the combined effects of perceived costs and benefits in explaining students’ decisions to cheat, the low detectability of ChatGPT-based plagiarism significantly reduces the cost of being caught, making rational choice theory less powerful in explaining academic misconduct in the AI era. Considering the effects of both rational decision-making and moral considerations, this study shows that individuals experiencing moral disengagement are more likely to engage in ChatGPT-based plagiarism. Other significant predictors include perceived benefits, punishment severity, and informal sanctions. Furthermore, moral disengagement enhances the effects of formal sanctions on ChatGPT-based plagiarism. The results have implications for professors, academic administrators, and plagiarism detection software designers to enforce academic integrity in the ChatGPT era. [ABSTRACT FROM AUTHOR]

In this study, training and prediction performance on turning data were investigated with ChatGPT, which is a popular AI platform nowadays. In this context, the resultant cutting forces obtained as a result of different turning simulations with FEM, training and estimation were made using regression, ANN, ANFIS methods. Using the same data, training and predictions were made with ChatGPT-3 with different prediction algorithms. As a result, the lowest average error rates in the predictions made with the training data; 2E-6% for ANFIS in prediction methods and 0.19% for ANN1 conversation in GPT-3 were obtained. The lowest average error rates in the predictions made with the test data; 5.41% for regression using logarithmic Box–Cox transformation in prediction methods, and 22.66% for ANN1 conversation in GPT-3 were achieved. The highest prediction performance in GPT-3 conversations was observed when GPT-3 was asked to make predictions with ANN algorithm on both training and test data. As a result, GPT-3 has not yet generated acceptable solutions for machining problems due to its low performance in predicting test data. However, due to the fast advancement of artificial intelligence technologies, it is obvious that solutions to this and more engineering problems will be generated in near future. Highlights Prediction with ChatGPT-3 Prediction performance of artificial intelligence Cutting force prediction of turning operation [ABSTRACT FROM AUTHOR]

ChatGPT, an artificial intelligence large language model, took the world by storm in November 2022. Its launch was met with excitement, and it garnered over 1 million users within the first five days of its release, surpassing social media giants like Facebook, Instagram, Twitter and even the top search engine, Google. Having been trained on volumes of data, ChatGPT has displayed the ability to produce human-like responses to questions. Concerns have been raised among members of the academic and research community regarding the ethical usage of ChatGPT and language models in education and academic/scientific research. This paper examines the development and rise of ChatGPT and similar AI tools, their pros and cons, their impact on academic research and possible intellectual property conflicts. It concludes that ChatGPT and language models are here to stay and makes recommendations for its ethical usage to prevent and detect plagiarism and protect intellectual property rights. [ABSTRACT FROM AUTHOR]

ChatGPT holds significant potential for enhancing learning through integration into education as an advanced chatbot. With the goal of harnessing this potential, our research focused on exploring the utilization of ChatGPT in designing a course plan for pre-service science teachers. We adopted a qualitative research approach and employed ChatGPT as an assistant to create a course plan for classroom assessment in science education. Our conversation to create this plan served as our data source for document analysis. We conducted interpretive analysis for qualitative data. The findings emphasized the benefits of ChatGPT in developing an implementable course plan, delivering adaptable information, and time saving. However, there were limitations to consider. These challenges encompassed issues such as communicating out of ChatGPT and the possibility of miscommunication. Despite these limitations, the research findings clearly demonstrate that ChatGPT is highly effective in developing a course plan. As researchers who have personally experienced the process of creating a course plan using ChatGPT, we believe that its potential needs to be maximally utilized. We suggest its application across different subjects and disciplines to thoroughly examine its strengths and weaknesses in depth. [ABSTRACT FROM AUTHOR]

AbstractIn the rapidly evolving landscape of technology, the emergence of Chat Generative Pre-trained Transformer (ChatGPT) marks a pivotal milestone in the realm of Artificial Intelligence (AI). However, little research has reported the predictors of people’s intentions to use ChatGPT. This pioneering study empirically examines user adoption through the lens of the Technology Acceptance Model (TAM) using a convenience sampling method. The study surveyed 784 ChatGPT users in China, of whom 58.93% were males. The results have revealed several key findings: (1) perceived usefulness, perceived ease of use, behavioral intention, and use behavior were positively correlated with each other; (2) behavioral intention acted as a mediating factor in the relationship between perceived usefulness and use behavior, as well as the relationship between perceived ease of use and use behavior; (3) perceived usefulness and behavioral intention played a chain-mediated role between perceived ease of use and use behavior; (4) the relationship between behavioral intention and use behavior exhibited greater strength among females compared to males; (5) the association between behavioral intention and use behavior was found to be stronger among urban users in comparison to their rural counterparts; (6) the connections between perceived ease of use and perceived usefulness, perceived ease of use and behavioral intention, and behavioral intention and use behavior were observed to be stronger among individuals with higher educational backgrounds relative to those with lower educational backgrounds. These findings provide crucial nuanced insights to advance the practical application of ChatGPT, emphasizing the need for enhanced usability and ease of use. However, this study exclusively captured usage behaviors within the Chinese user base. Future investigations could encompass diverse demographics across multiple countries, enabling cross-cultural comparisons. [ABSTRACT FROM AUTHOR]

We tested whether ChatGPT can play a role in designing field courses in higher education. In collaboration with ChatGPT, we developed two field courses; the first aimed at creating a completely new field trip, while the second was tailored to fit an existing university module, and then compared to the human module design. From our case studies, several insights emerged. These include the importance of precise prompt engineering and the need to iterate and refine prompts to achieve optimal results. We outlined a workflow for effective prompt engineering, emphasising clear objectives, sequential prompting, and feedback loops. We also identify best practices, including highlighting the importance of collaborating with human expertise, validating AI suggestions, and integrating adaptive management for continual refinement. While ChatGPT is a potent tool with the potential to save a significant amount of time and effort in field course design, human expertise remains indispensable for achieving optimal results. [ABSTRACT FROM AUTHOR]

This study aims to explore the students’ experience with ChatGPT in providing scaffolding for writing essays. It also discloses students’ appraisals of utilising ChatGPT. Drawing upon data from semi-structured interviews, this study involved 12 students learning English as a Foreign Language who utilised ChatGPT in academic writing classes. The findings revealed that students derived advantages from using ChatGPT for translation, writing accuracy, writing efficiency, idea generation, and practical use. This study also reveals students’ criticism in terms of inaccurate information generated as well as the possibility of students committing academic dishonesty when using ChatGPT. This study indicates that although students use ChatGPT to help them write their essays, they have the awareness to fact-check the information to avoid committing academic dishonesty. [ABSTRACT FROM AUTHOR]

PurposeMethodsResultsConclusionTo assess the accuracy and completeness of ChatGPT-generated answers regarding uveitis description, prevention, treatment, and prognosis.Thirty-two uveitis-related questions were generated by a uveitis specialist and inputted into ChatGPT 3.5. Answers were compiled into a survey and were reviewed by five uveitis specialists using standardized Likert scales of accuracy and completeness.In total, the median accuracy score for all the uveitis questions (n = 32) was 4.00 (between “more correct than incorrect” and “nearly all correct”), and the median completeness score was 2.00 (“adequate, addresses all aspects of the question and provides the minimum amount of information required to be considered complete”). The interrater variability assessment had a total kappa value of 0.0278 for accuracy and 0.0847 for completeness.ChatGPT can provide relatively high accuracy responses for various questions related to uveitis; however, the answers it provides are incomplete, with some inaccuracies. Its utility in providing medical information requires further validation and development prior to serving as a source of uveitis information for patients. [ABSTRACT FROM AUTHOR]

The present research aims to highlight the underlying factors that drive students’ adoption of the ChatGPT chatbot in higher education. This study extends the meta-UTAUT framework by including additional exogenous factors of anthropomorphism, trust, design novelty, and institutional policy. Empirical examination with Structural Equation Modelling among 355 students in Dutch higher education institutions revealed attitude and behavioural intention as significant positive predictors of students’ ChatGPT use behaviour. Institutional policy negatively moderated the effect of behavioural intention on use behaviour. Behavioural intention was significantly and positively influenced by attitude, performance expectancy, social influence, and facilitating conditions. Anthropomorphism, design novelty, trust, performance expectancy, and effort expectancy were unveiled as significant positive antecedents of attitude. The central theoretical contributions of this research include investigating students’ use behaviour instead of behavioural intention, establishing attitude as a core construct, underlining additional antecedents of attitude, and highlighting the importance of institutional policy. The present study contributes to prior research on technology adoption, especially in the area of artificial intelligence in education. The findings yield valuable insights for chatbot designers, product managers, and higher education policy writers. [ABSTRACT FROM AUTHOR]

AbstractAimsMethodsResultsConclusionIn 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]

AbstractWith the rapid evolution of artificial intelligence (AI), this study aims to examine the interplay among the perceived usability, perceived enjoyment, perceived responsiveness, and intention to continue using ChatGPT. Structural equation modeling (SEM) was used to investigate our proposed model. We recruited 446 ChatGPT users through an online survey conducted on the Connect platform, powered by CloudResearch. Perceived usability (β = 0.254) and enjoyment (β = 0.438) significantly influence satisfaction with ChatGPT. However, perceived responsiveness is not significantly related to perceived attachment or satisfaction. Furthermore, we established that perceived attachment (β = 0.405) and satisfaction (β = 0.447) are pivotal in shaping users’ intentions to continue using ChatGPT, providing insights into human–AI interactions. The practical implications of our findings suggest that generative AI chatbots should be crafted with a focus on enjoyable, user-centered interfaces that foster long-term user satisfaction and engagement. AI developers should design AI conversation flows and chatbot personas to optimize the user experience. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusionsThis study aimed to compare the performance of ChatGPT, a large language model (LLM), with human neurosurgical applicants in a neurosurgical national selection interview, to assess the potential of artificial intelligence (AI) and LLMs in healthcare and provide insights into their integration into the field.In a prospective comparative study, a set of neurosurgical national selection-style interview questions were asked to eight human participants and ChatGPT in an online interview. All participants were doctors currently practicing in the UK who had applied for a neurosurgical National Training Number. Interviews were recorded, anonymised, and scored by three neurosurgical consultants with experience as interviewers for national selection. Answers provided by ChatGPT were used as a template for a virtual interview. Interview transcripts were subsequently scored by neurosurgical consultants using criteria utilised in real national selection interviews. Overall interview score and subdomain scores were compared between human participants and ChatGPT.For overall score, ChatGPT fell behind six human competitors and did not achieve a mean score higher than any individuals who achieved training positions. Several factors, including factual inaccuracies and deviations from expected structure and style may have contributed to ChatGPT's underperformance.LLMs such as ChatGPT have huge potential for integration in healthcare. However, this study emphasises the need for further development to address limitations and challenges. While LLMs have not surpassed human performance yet, collaboration between humans and AI systems holds promise for the future of healthcare. [ABSTRACT FROM AUTHOR]

This article develops an index to measure the level of susceptibility of courses to cheating using ChatGPT (Chat Generative Pre-trained Transformer), an advanced text-based artificial intelligence (AI) language model. It demonstrates the application of the index to a sample of business courses in a mid-sized university. The study finds that the vulnerability index varies across disciplines and teaching modalities. As advanced language models become more common in academic settings and create new educational challenges, the study provides an intuitive and practical mechanism for instructors and academic units to measure and assess the vulnerability of their courses to various language-based predictive models. [ABSTRACT FROM AUTHOR]

This qualitative study explored the factors that influence teachers’ behavioural intention (BI) to use ChatGPT for teaching. The data was collected from 214 Bhutanese teachers using open-ended questions and interviews. The thematic analysis revealed four key factors that are likely to influence teachers’ BI to use ChatGPT in the context. These factors included perceived usefulness, perceived ease of use, perceived lack of integrity, and perceived impact on aptitude. This study concludes with potential theoretical and practical implications in the field of teacher education. [ABSTRACT FROM AUTHOR]

This article presents the use of an artificial intelligence (AI) tool (ChatGPT) to transform a didactic lecture to an interactive workshop. The workshop on scientific and scholarly report writing was conducted for preclinical medical students. The functions of the AI as teaching assistance and documentation were expected to support the workshop. In this workshop, the AI was used to summarise and generate the summary notes based on students' inputs throughout the interactive activities during the workshop. Future research should focus on potential functions of AI in medical education and its impacts on student's learning outcomes. [ABSTRACT FROM AUTHOR]

ChatGPT, an artificial intelligence-supported chatbot, has become a resource in the field of education for students across various disciplines. The conversation that unfolds based on the user-generated questions and ChatGPT’s responses implies the emergence of an argumentation between the student and ChatGPT. In this study, the argumentation between a university student and ChatGPT, conducted to examine situations where a tangent line cannot be drawn to a function’s graph, was analyzed using Toulmin’s model enriched with Habermas’ construct of rationality. The findings indicate that ChatGPT offers rational support to the warrant in student’s reasoning in the of context epistemic rationality regarding the definitions of continuity, derivative, and tangent concepts. However, ChatGPT provides problematic responses in the context of epistemic rationality regarding the relationship between continuity and derivative, as well as in the context of communicative rationality concerning the geometric interpretation of the derivative and the definition of the tangent line. ChatGPT has not provided reliable and stable support to the student in the reasoning process. It can be concluded that ChatGPT, which is considered to have the potential to assist learning processes, requires expertise in calculus concepts for its use in mathematical reasoning processes. Otherwise, it may be confusing and/or misleading. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConclusionsGenerative AI will become an integral part of education in future. The potential of this technology in different disciplines should be identified to promote effective adoption. This study evaluated the performance of ChatGPT in tutorial and case-based learning questions in physiology and biochemistry for medical undergraduates. Our study mainly focused on the performance of GPT-3.5 version while a subgroup was comparatively assessed on GPT-3.5 and GPT-4 performances.Answers were generated in GPT-3.5 for 44 modified essay questions (MEQs) in physiology and 43 MEQs in biochemistry. Each answer was graded by two independent examiners. Subsequently, a subset of 15 questions from each subject were selected to represent different score categories of the GPT-3.5 answers; responses were generated in GPT-4, and graded.The mean score for physiology answers was 74.7 (SD 25.96). GPT-3.5 demonstrated a statistically significant (p = .009) superior performance in lower-order questions of Bloom’s taxonomy in comparison to higher-order questions. Deficiencies in the application of physiological principles in clinical context were noted as a drawback. Scores in biochemistry were relatively lower with a mean score of 59.3 (SD 26.9) for GPT-3.5. There was no statistically significant difference in the scores for higher and lower-order questions of Bloom’s taxonomy. The deficiencies highlighted were lack of in-depth explanations and precision. The subset of questions where the GPT-4 and GPT-3.5 were compared demonstrated a better overall performance in GPT-4 responses in both subjects. This difference between the GPT-3.5 and GPT-4 performance was statistically significant in biochemistry but not in physiology.The differences in performance across the two versions, GPT-3.5 and GPT-4 across the disciplines are noteworthy. Educators and students should understand the strengths and limitations of this technology in different fields to effectively integrate this technology into teaching and learning. [ABSTRACT FROM AUTHOR]

AbstractIn the ever-evolving domain of artificial intelligence (AI), understanding how users interact with AI-driven solutions, like ChatGPT, has become vital. The purpose of this research is to explore factors influencing user satisfaction and their willingness to subscribe to ChatGPT's superior services. Employing partial least squares structural equation modeling, the study examines responses from 554 ChatGPT users. The results reveal that perceived intelligence and service quality of ChatGPT significantly influence perceived usefulness, knowledge management, and overall user satisfaction. Interestingly, while perceived usefulness notably augments user satisfaction, it doesn’t directly drive willingness to subscribe to paid services. Similarly, knowledge management boosts satisfaction but doesn’t affect payment readiness. Importantly, user satisfaction substantially encourages willingness to pay for advanced features. Personal innovativeness contributes to willingness to pay, but it doesn’t modify the impacts of perceived usefulness, knowledge management, or satisfaction on payment readiness. Finally, perceived risk negatively impacts willingness to pay. These findings illuminate the intricate mechanisms governing user-AI interactions and factors determining their readiness for premium subscriptions. This research lays groundwork for future scholarly investigations of AI-user dynamics. [ABSTRACT FROM AUTHOR]

AbstractThis study offers a critical examination of university policies developed to address recent challenges presented by generative AI (GenAI) to higher education assessment. Drawing on Bacchi’s ‘What’s the problem represented to be’ (WPR) framework, we analysed the GenAI policies of 20 world-leading universities to explore what are considered problems in this AI-mediated assessment landscape and how these problems are represented in policies. Although miscellaneous GenAI-related problems were mentioned in these policies (e.g. reliability of AI-generated outputs, equal access to GenAI), the primary problem represented is that students may not submit original work for assessment. In the current framing, GenAI is often viewed as a type of external assistance separate from the student’s independent efforts and intellectual contribution, thereby undermining the originality of their work. We argue that such problem representation fails to acknowledge how the rise of GenAI further complicates the process of producing original work and what it means by originality in a time when knowledge production becomes increasingly distributed, collaborative and mediated by technology. Therefore, a critical silence in higher education policies concerns the evolving notion of originality in the digital age and a more inclusive approach to address the originality of students’ work is required. [ABSTRACT FROM AUTHOR]

AbstractTechnologically assisted methods have been extensively utilized in positive psychology interventions (PPIs), which aim to elevate the happiness levels of the widespread and diverse general public, a population that traditional methodologies struggle to comprehend and impact. Nevertheless, the literature provides insufficient insights into the effectiveness of chatbot-based PPIs (Chat-PPIs). This study endeavors to fill this void by employing both retrieval-based and generative pre-trained transformer (GPT)-based chatbots and executing three randomized controlled trials involving 326 participants to investigate the hypothesized effectiveness mechanisms. The statistical analysis affirms the effectiveness of Chat-PPI. Moreover, our results indicate that personalized PPI recommendations, adaptive multi-round dialogues, and real-time feedback significantly augment the efficacy of Chat-PPI. Besides exploring and confirming the mechanisms behind Chat-PPI, this study also endorses the use of generative chatbots, which, although less controllable, provide more natural interaction that can boost the efficacy of Chat-PPI. [ABSTRACT FROM AUTHOR]

AbstractThe integration of ChatGPT as a supplementary tool for writing instruction has gained traction. However, uncertainties persist regarding how ChatGPT complements teacher assessment and the overall effectiveness of this combined approach. To address this, we conducted a mixed-methods investigation involving 46 undergraduate students from a research university in southern China, engaging them in a Chinese academic writing task. The intraclass correlation coefficient results revealed ChatGPT’s efficiency in scoring students’ writing, showing moderate to good consistency with teacher evaluations. A paired sample t-test unveiled significant differences in feedback quantity and types between ChatGPT and teacher assessments. Drawing from both interview data and quantitative findings, the study uncovers three ways in which ChatGPT complements teacher assessment, benefiting students with various writing proficiency levels: (1) fostering deeper comprehension of teacher assessments among students, (2) encouraging students to make judgments regarding feedback, and (3) promoting independent thinking about revisions. This study contributes to a more comprehensive understanding of the role of ChatGPT within the context of a combined assessment approach. It underscores that certain inherent weaknesses in ChatGPT’s functioning can paradoxically lead to favorable outcomes. By shedding light on the synergy between ChatGPT and teacher assessments, this research seeks to inform and enhance writing instruction in higher education. [ABSTRACT FROM AUTHOR]

Accelerating digitization, algorithmic computation, artificial intelligence, and machine learning, along with the increasing automation of work, communication, and everyday life, are central to critical studies of technology and political economy, as well as to public discourse concerning technology's role in creating futures. Ongoing transformations in technological capacity have also been scrutinized for their impact on experience, emotion, and culture. Building on David Theo Goldberg's assertion that "tracking capitalism" creates pervasive dread, this article explores how the digital automation of education, specifically, generates forms of algorithmic anxiety that impact teaching and learning, constraining pedagogical visions of alternative futures. Algorithmic anxiety in education contributes to dread's proliferation. If dread is the "driving social sensibility" today, and algorithmic anxiety a pedagogical vector of its spread, then a less dreadful education should center an imaginative struggle for new sensibilities. [ABSTRACT FROM AUTHOR]

Hospital librarians receive invites to teach thinking and searching in an evidence-based way and critical appraisal of the literature to nurses. With these invitations, the hospital librarians play a central role in establishing an evidence-based culture in the hospital and contribute to the nursing staff feeling competent and confident in fulfilling evidence-based competencies. This author just prepared a 17-minute online talk as part of an international nursing webinar on "searching nursing literature in an evidence-based way." Using this experience, remembering other teaching and presentation experiences, and some "help" from AI tools, this experienced hospital librarian suggests decision points for colleagues to create a meaningful, practical information session for nurses and introduce to some AI tools along the way. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI language models—ChatGPT and Google Bard—focusing on their ability to solve undergraduate finance problems. We find that GPT-4 significantly outperforms Bard-1.0, excelling in easy problems but struggling with complex ones. The results suggest that it is crucial to handle AI with care in order to uphold academic integrity. [ABSTRACT FROM AUTHOR]

The role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context. Information literacy and digital literacy are related. Academic libraries should include digital topics in information literacy training. Information literacy should be trained at the start of university education and before students begin dissertation writing. University libraries should propose guidelines for the use of generative artificial intelligence tools by students. [ABSTRACT FROM AUTHOR]

This study explored the potential of ChatGPT in the search for reliable references. Results showed that the reliability of ChatGPT's references vary across different source types. For journal articles, they were all fabricated and not compliant with APA 7 format. Webpages did not match actual existing sites. However, for books, the generated references correctly corresponded to real, existing ones. In the case of journal websites, the links within the responses were functional. Although this exploration gained interesting insights, researchers are strongly recommended not to depend on ChatGPT for references due to accuracy issues and must undertake manual, scholarly searches. [ABSTRACT FROM AUTHOR]

With over 180 million users engaging with ChatGPT by late-2023 estimates, the need for educational resources, workshops, and discussions on its use has become imperative. This article explores the evolving role of ChatGPT in academic libraries, highlighting proactive initiatives by university libraries to integrate the technology. Through 25 prompts nested in 10 relevant use cases, the authors underscore the utility of ChatGPT, while cautioning against the tool's limitations. The article is a call for libraries to stay informed, skill share, and adapt their practices to harness the benefits of AI, while also mitigating potential pitfalls. [ABSTRACT FROM AUTHOR]

The article offers information on exploring generative artificial intelligence (AI), specifically ChatGPT, and its potential applications in information literacy instruction. Topics include the impact of generative AI on teaching and learning, the development and features of generative AI technologies like ChatGPT, and the need for thoughtful integration of AI in education.

The arrival of ChatGPT has prompted numerous conversations about artificial intelligence's role in shaping the future of libraries. Our article seeks to contextualize this new technology within music discovery research by defining the historical underpinnings of artificial intelligence in computer music and music information retrieval. We also identify four areas in librarianship research and practice where applications of this technology should be explored; augmenting library interactions with conversational search, creating tools to assist with metadata clean up and creation, integrating music holdings into commercial discovery systems, and improving music discovery platforms. We hope by identifying potential areas of research we inspire others with practical ways they can contribute to the shape of artificial intelligence in music discovery. [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]

AbstractLarge language models (LLMs) such as GPT-3 and their derivative products such as ChatGPT have garnered significant attention for their remarkable ability to process texts and conduct human-like conversations. Guided by the Diffusion of Innovation theory, this study examines the early discussions about LLMs on Twitter, specifically about ChatGPT and GPT-3, during the first three months following the release of ChatGPT. By utilizing topic structural modeling and sentiment analysis on a sample of 42,273 #ChatGPT tweets and 17,639 #GPT3 tweets, we explore how laypeople and technical professionals differ in their attitudes in the early stage of the adoption of LLMs. Our findings suggest that the discussion surrounding ChatGPT and GPT-3 primarily revolves around relative advantage and compatibility, with the majority of #ChatGPT conversations demonstrating negative sentiment and #GPT3 discussions containing more positive topics. The Twitter discussion using #ChatGPT is highly business-oriented, while the discussion of #GPT3 covers a broader range of topics in terms of the characteristics, applications, and potential ethical concerns of LLMs. This study offers implications for government agencies and policymakers, suggesting that further research is needed to fully understand the potential applications and risks of LLMs. [ABSTRACT FROM AUTHOR]