Search

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

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

Purpose: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, in uveitis diagnosis compared to renowned uveitis specialists, and evaluate clinicians' perception about utilizing artificial intelligence (AI) in ophthalmology practice. Methods: Six cases were presented to uveitis experts, ChatGPT (version 3.5 and 4.0) and Glass 1.0, and diagnostic accuracy was analyzed. Additionally, a survey about the emotions, confidence in utilizing AI-based tools, and the likelihood of incorporating such tools in clinical practice was done. Results: Uveitis experts accurately diagnosed all cases (100%), while ChatGPT achieved a diagnostic success rate of 66% and Glass 1.0 achieved 33%. Most attendees felt excited or optimistic about utilizing AI in ophthalmology practice. Older age and high level of education were positively correlated with increased inclination to adopt AI-based tools. Conclusions: ChatGPT demonstrated promising diagnostic capabilities in uveitis cases and ophthalmologist showed enthusiasm for the integration of AI into clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice from AI-based uveitis chatbots lack assessment. Methods: We appraised diagnostic accuracy and management suggestions of an AI-based chatbot, ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases aligned with new Uveitis Nomenclature guidelines. Participants predicted likely diagnoses, two differentials, and next management steps. Comparative success rates were computed. Results: Ophthalmologists excelled (60–92%) in likely diagnosis, exceeding AI (60%). Considering fully and partially accurate diagnoses, ophthalmologists achieved 76–100% success; AI attained 72%. Despite an 8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans. Conclusions: The study underscores AI chatbots' potential in uveitis diagnosis and management, indicating their value in reducing diagnostic errors. Further research is essential to enhance AI chatbot precision in diagnosis and recommendations. [ABSTRACT FROM AUTHOR]

We evaluated the performance of a chatbot compared to the National Comprehensive Cancer Network (NCCN) Guidelines for the management of ovarian cancer. Using NCCN Guidelines, we generated 10 questions and answers regarding management of ovarian cancer at a single point in time. Questions were thematically divided into risk factors, surgical management, medical management, and surveillance. We asked ChatGPT (GPT-4) to provide responses without prompting (unprompted GPT) and with prompt engineering (prompted GPT). Responses were blinded and evaluated for accuracy and completeness by 5 gynecologic oncologists. A score of 0 was defined as inaccurate, 1 as accurate and incomplete, and 2 as accurate and complete. Evaluations were compared among NCCN, unprompted GPT, and prompted GPT answers. Overall, 48% of responses from NCCN, 64% from unprompted GPT, and 66% from prompted GPT were accurate and complete. The percentage of accurate but incomplete responses was higher for NCCN vs GPT-4. The percentage of accurate and complete scores for questions regarding risk factors, surgical management, and surveillance was higher for GPT-4 vs NCCN; however, for questions regarding medical management, the percentage was lower for GPT-4 vs NCCN. Overall, 14% of responses from unprompted GPT, 12% from prompted GPT, and 10% from NCCN were inaccurate. GPT-4 provided accurate and complete responses at a single point in time to a limited set of questions regarding ovarian cancer, with best performance in areas of risk factors, surgical management, and surveillance. Occasional inaccuracies, however, should limit unsupervised use of chatbots at this time. • There is interest in expanding the use of chatbot technology in medicine. • A high percentage of responses from ChatGPT to questions about ovarian cancer were graded as accurate and complete. • ChatGPT had a higher percentage of inaccurate responses compared to the National Comprehensive Cancer Network Guidelines. • ChatGPT received the highest scores in response to questions regarding risk factors, surgical management, and surveillance. [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]

Background: ChatGPT, a recently developed artificial intelligence (AI) chatbot, has demonstrated improved performance in examinations in the medical field. However, thus far, an overall evaluation of the potential of ChatGPT models (ChatGPT-3.5 and GPT-4) in a variety of national health licensing examinations is lacking. This study aimed to provide a comprehensive assessment of the ChatGPT models' performance in national licensing examinations for medical, pharmacy, dentistry, and nursing research through a meta-analysis. Methods: Following the PRISMA protocol, full-text articles from MEDLINE/PubMed, EMBASE, ERIC, Cochrane Library, Web of Science, and key journals were reviewed from the time of ChatGPT's introduction to February 27, 2024. Studies were eligible if they evaluated the performance of a ChatGPT model (ChatGPT-3.5 or GPT-4); related to national licensing examinations in the fields of medicine, pharmacy, dentistry, or nursing; involved multiple-choice questions; and provided data that enabled the calculation of effect size. Two reviewers independently completed data extraction, coding, and quality assessment. The JBI Critical Appraisal Tools were used to assess the quality of the selected articles. Overall effect size and 95% confidence intervals [CIs] were calculated using a random-effects model. Results: A total of 23 studies were considered for this review, which evaluated the accuracy of four types of national licensing examinations. The selected articles were in the fields of medicine (n = 17), pharmacy (n = 3), nursing (n = 2), and dentistry (n = 1). They reported varying accuracy levels, ranging from 36 to 77% for ChatGPT-3.5 and 64.4–100% for GPT-4. The overall effect size for the percentage of accuracy was 70.1% (95% CI, 65–74.8%), which was statistically significant (p < 0.001). Subgroup analyses revealed that GPT-4 demonstrated significantly higher accuracy in providing correct responses than its earlier version, ChatGPT-3.5. Additionally, in the context of health licensing examinations, the ChatGPT models exhibited greater proficiency in the following order: pharmacy, medicine, dentistry, and nursing. However, the lack of a broader set of questions, including open-ended and scenario-based questions, and significant heterogeneity were limitations of this meta-analysis. Conclusions: This study sheds light on the accuracy of ChatGPT models in four national health licensing examinations across various countries and provides a practical basis and theoretical support for future research. Further studies are needed to explore their utilization in medical and health education by including a broader and more diverse range of questions, along with more advanced versions of AI chatbots. [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]

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]

Recent advances in artificial intelligence (AI) give chatbots, e.g. ChatGPT, more human-like interaction and conversational capability. AI chatbots are becoming more popular for supporting language learning. Most current review research disregards the significance of teachers’ roles in chatbot-assisted language learning. Self-determination theory (SDT) explains the roles by suggesting how teachers satisfy student needs and how chatbots thwart those needs. Therefore, this systemic review aims to: (i) suggest the roles that teachers play in student English learning with AI chatbot; (ii) discuss how those roles satisfy SDT needs of the students; and (iii) discuss the challenges in this learning. This review selected 23 articles published throughout the last ten years (2014–2023). The findings offer (i) four empirical and theoretical contributions: technological pedagogical content knowledge (TPACK) for chatbots, needs satisfaction from teachers, needs thwarting from chatbots, and SDT-based research designs for chatbots; (ii) two practical suggestions: understanding technological knowledge of chatbots and SDT needs support for chatbots; and (iii) six research directions: experimental studies, student language proficiency, more teacher roles, revisiting TPACK, future development of chatbots, and large language model teacher-like chatbots. Overall, the findings enhance our knowledge of TPACK and teacher digital and AI competences for chatbot-assisted language learning. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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]

Purpose: To evaluate the understandability, actionability, and readability of responses provided by the website of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS), ChatGPT-3.5, Bard, and Bing Chat about amblyopia and the appropriateness of the responses generated by the chatbots. Method: Twenty-five questions provided by the AAPOS website were directed three times to fresh ChatGPT-3.5, Bard, and Bing Chat interfaces. Two experienced pediatric ophthalmologists categorized the responses of the chatbots in terms of their appropriateness. Flesch Reading Ease (FRE), Flesch Kincaid Grade Level (FKGL), and Coleman-Liau Index (CLI) were used to evaluate the readability of the responses of the AAPOS website and chatbots. Furthermore, the understandability scores were evaluated using the Patient Education Materials Assessment Tool (PEMAT). Results: The appropriateness of the chatbots' responses was 84% for ChatGPT-3.5 and Bard and 80% for Bing Chat (P >.05). For understandability (mean PEMAT-U score AAPOS website: 81.5%, Bard: 77.6%, ChatGPT-3.5: 76.1%, and Bing Chat: 71.5%, P <.05) and actionability (mean PEMAT-A score AAPOS website: 74.6%, Bard: 69.2%, ChatGPT-3.5: 67.8%, and Bing Chat: 64.8%, P <.05), the AAPOs website scored better than the chat-bots. Three readability analyses showed that Bard had the highest mean score, followed by the AAPOS website, Bing Chat, and ChatGPT-3.5, and these scores were more challenging than the recommended level. Conclusions: Chatbots have the potential to provide detailed and appropriate responses at acceptable levels. The AAPOS website has the advantage of providing information that is more understandable and actionable. The AAPOS website and chatbots, especially Chat-GPT, provided difficult-to-read data for patient education regarding amblyopia. [J Pediatr Ophthalmol Strabismus. 2024;61(5):325–331.] [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and ChatGPT have been the talk of the town lately. Why not? Considering the fact that ChatGPT acquired one million users in only four days and 100 million users in just two months of launching, it makes perfect sense for project managers and corporate executives to make it a discussion topic. ChatGPT grew 75% faster than any tech giant in recent times. Thus, Project Managers around the world have been scrambling to figure out the impact of AI and Chatbots on projects and their careers. This paper discusses three pathways project managers and executives can follow to launch products and successfully implement projects through AI tools such as ChatGPT, Bard, Jasper, and others. The authors review how real-life companies such as Uber, Lyft, Instacart, and Khan Academy have leveraged AI using ubiquitous mobile devices to enhance their customer base and revenues. The paper also describes the three techniques project managers and executives can immediately utilize to implement Chatbots such as ChatGPT and enhance project success and their companies' bottom line. The three ChatGPT pathways that are discussed in the paper include: 1. Exploiting a Current Value proposition 2. Expanding the Value Proposition 3. Exploring a New Value Proposition We also explore whether AI tools and software pose any threats or present opportunities to the project managers and the projects. A few research studies have shown that AI could have a profound impact on the world economy. Some industries might see a job loss due to the introduction of AI, especially those that do not adapt. Economists euphemistically call this "adjustment costs". [ABSTRACT FROM AUTHOR]

Large language models, e.g., Chat Generative Pre-Trained Transformer (also known as ChatGPT), are currently contributing enormously to making artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a "statistical correlation machine" (correlation instead of causality), and there are indeed ethical concerns associated with the use of AI language models including ChatGPT, such as bias, privacy, and abuse. This paper highlights specific ethical concerns about ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical recommendations for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These best practice examples are expected to motivate the ethical use of ChatGPT. [ABSTRACT FROM AUTHOR]

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

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]

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]

Despite the growing popularity of ChatGPT and chatbot-assisted writing, research on the use of ChatGPT in second language (L2) writing classrooms remains insufficient. Using reflection papers and focus group interviews, the qualitative study examined doctoral students' views on the impact of using ChatGPT on L2 writing and their expected responses. Thematic analysis revealed that ChatGPT could support writers at the pre-writing, during-writing and post-writing stages and serve as a self-learning tool for writing and thinking development with its human and non-human features. Nonetheless, its generative nature also gave rise to concerns for learning loss, authorial voice, unintelligent texts, academic integrity as well as social and safety risks. Based on the benefits and drawbacks, the doctoral students expected the education sector to make concerted efforts for the effective, ethical and responsible use of ChatGPT in L2 writing. Suggestions are accordingly provided for future considerations in teaching and research to leverage ChatGPT for L2 writing. [ABSTRACT FROM AUTHOR]

Purpose: The diagnosis and management of prostate cancer (PCa), the second most common cancer in men worldwide, are highly complex. Hence, patients often seek knowledge through additional resources, including AI chatbots such as ChatGPT and Google Bard. This study aimed to evaluate the performance of LLMs in providing education on PCa. Methods: Common patient questions about PCa were collected from reliable educational websites and evaluated for accuracy, comprehensiveness, readability, and stability by two independent board-certified urologists, with a third resolving discrepancy. Accuracy was measured on a 3-point scale, comprehensiveness was measured on a 5-point Likert scale, and readability was measured using the Flesch Reading Ease (FRE) score and Flesch–Kincaid FK Grade Level. Results: A total of 52 questions on general knowledge, diagnosis, treatment, and prevention of PCa were provided to three LLMs. Although there was no significant difference in the overall accuracy of LLMs, ChatGPT-3.5 demonstrated superiority over the other LLMs in terms of general knowledge of PCa (p = 0.018). ChatGPT-4 achieved greater overall comprehensiveness than ChatGPT-3.5 and Bard (p = 0.028). For readability, Bard generated simpler sentences with the highest FRE score (54.7, p < 0.001) and lowest FK reading level (10.2, p < 0.001). Conclusion: ChatGPT-3.5, ChatGPT-4 and Bard generate accurate, comprehensive, and easily readable PCa material. These AI models might not replace healthcare professionals but can assist in patient education and guidance. [ABSTRACT FROM AUTHOR]

The ChatGPT (Chat Generative Pre-Trained Transformer) has been rapidly developing with a growing number of users. The potential for problematic use/dependence caused by the use of ChatGPT urgently needs to be explored. This study recruited a sample of 1040 Taiwanese adults to develop the Problematic ChatGPT use Scale (PCUS) and investigate the relationships between ChatGPT problematic use and related factors such as age, gender, demographic categories, usage time, user satisfaction rating, and depression. The results of exploratory and confirmatory factor analysis verified that the PCUS had good factorial/construct validity. Additionally, the PCUS showed high internal consistency and test-retest reliability. Among the factors related to the PCUS scores, usage time and depression showed a positive correlation with PCUS scores. Furthermore, male PCUS scores were significantly higher than female PCUS scores. The PCUS scores showed no significant relationship with age, demographic categories (undergraduate students, graduate students, and working professionals), and various satisfaction evaluations. Limitations and recommendations for future research were also discussed. [ABSTRACT FROM AUTHOR]

We live in an age of artificial intelligence (AI). Open AI, an American artificial intelligence organization, claims that generative chatbots are here to stay. Although computer technology remains unevenly distributed, the presence of AI continues to be felt in many areas of daily life. Further, AI development happens in few countries whose cultural practices and beliefs tend to be embedded in AI output and functionality. Western and Global North perspectives tend to dominate conversations about AI. How might AI technology function equitably in an ethno‐racially and linguistically diverse world? This paper explores intersections of technology, coloniality, and bias to contextualize the user‐experience for individuals in groups characterized as high, medium, and low‐resourced languages. For whom does AI function in our globalizing world? Engaging with this question through a languacultural (Agar, 1994) lens, I employ a language and culture perspective to guide inquiry into AI’s output and impact, based on a children’s storybook. Specifically, I deploy Kenyan, Indonesian, and Taiwanese languacultural perspectives of three participants in a culturally cognizant reading (Washington, 2023) of a product of chatbot‐human composing, ‘Alice and Sparkle’, a children’s book illustrated by MidJourney and co‐authored by ChatGPT and Ammaar Reshi. Overall, through scrutiny of encoded cultural tropes and values inscribed within text and images, this paper proposes considerations for literacy pedagogy and research that stem from a pursuit of inclusive AI. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) like ChatGPT have potential applications in medical education such as helping students study for their licensing exams by discussing unclear questions with them. However, they require evaluation on these complex tasks. The purpose of this study was to evaluate how well publicly accessible LLMs performed on simulated UK medical board exam questions. 423 board-style questions from 9 UK exams (MRCS, MRCP, etc.) were answered by seven LLMs (ChatGPT-3.5, ChatGPT-4, Bard, Perplexity, Claude, Bing, Claude Instant). There were 406 multiple-choice, 13 true/false, and 4 "choose N" questions covering topics in surgery, pediatrics, and other disciplines. The accuracy of the output was graded. Statistics were used to analyze differences among LLMs. Leaked questions were excluded from the primary analysis. ChatGPT 4.0 scored (78.2%), Bing (67.2%), Claude (64.4%), and Claude Instant (62.9%). Perplexity scored the lowest (56.1%). Scores differed significantly between LLMs overall (p < 0.001) and in pairwise comparisons. All LLMs scored higher on multiple-choice vs true/false or "choose N" questions. LLMs demonstrated limitations in answering certain questions, indicating refinements needed before primary reliance in medical education. However, their expanding capabilities suggest a potential to improve training if thoughtfully implemented. Further research should explore specialty specific LLMs and optimal integration into medical curricula. [ABSTRACT FROM AUTHOR]

ChatGPT, an extensively recognised language model created by OpenAI, has gained significant prominence across various industries, particularly in education. This study aimed to investigate the factors that influence students' intentions to adopt and utilise ChatGPT for their academic studies. The study used a Structural Equation Model (SEM) for analysing the data gathered from 108 participants, comprising both undergraduate and postgraduate students enrolled in public and private universities in Vietnam. The findings indicated that students' inclination to adopt ChatGPT (referred to as adoption intention or AI) was influenced by their perception of its user-friendliness (PEU). However, the perceived usefulness (PU) of ChatGPT did not have a direct impact on students' adoption intention; instead, it had an indirect influence through personalisation (with a positive effect) and interactivity (with a negative effect). Importantly, there was no significant indirect effect of PU on AI mediated by perceived trust and perceived intelligence. This study is one of the initial empirical inquiries into ChatGPT adoption within an Asian context, providing valuable insights in this emerging area of research. As the use of ChatGPT by students becomes increasingly inevitable, educational institutions should carefully consider integrating it into the assessment process. It is crucial to design assessments that encourage responsible usage of ChatGPT, preserving students' critical thinking abilities and creativity in their assessment writing. Moving forward, educators will play a pivotal role by offering clear guidelines and instructions that set out the appropriate and ethical use of artificial intelligence tools in the assessments. [ABSTRACT FROM AUTHOR]

Background In November 2022, OpenAI released a chatbot named ChatGPT, a product capable of processing natural language to create human-like conversational dialogue. It has generated a lot of interest, including from the scientific community and the medical science community. Recent publications have shown that ChatGPT can correctly answer questions from medical exams such as the United States Medical Licensing Examination and other specialty exams. To date, there have been no studies in which ChatGPT has been tested on specialty questions in the field of nephrology anywhere in the world. Methods Using the ChatGPT-3.5 and -4.0 algorithms in this comparative cross-sectional study, we analysed 1560 single-answer questions from the national specialty exam in nephrology from 2017 to 2023 that were available in the Polish Medical Examination Center's question database along with answer keys. Results Of the 1556 questions posed to ChatGPT-4.0, correct answers were obtained with an accuracy of 69.84%, compared with ChatGPT-3.5 (45.70%, P  = .0001) and with the top results of medical doctors (85.73%, P  = .0001). Of the 13 tests, ChatGPT-4.0 exceeded the required ≥60% pass rate in 11 tests passed, and scored higher than the average of the human exam results. Conclusion ChatGPT-3.5 was not spectacularly successful in nephrology exams. The ChatGPT-4.0 algorithm was able to pass most of the analysed nephrology specialty exams. New generations of ChatGPT achieve similar results to humans. The best results of humans are better than those of ChatGPT-4.0. [ABSTRACT FROM AUTHOR]

Purpose: This study analyses the performance and proficiency of the three Artificial Intelligence (AI) generative chatbots (ChatGPT-3.5, ChatGPT-4.0, Bard Google AI®) and in answering the Multiple Choice Questions (MCQs) of postgraduate (PG) level orthopaedic qualifying examinations. Methods: A series of 120 mock Single Best Answer' (SBA) MCQs with four possible options named A, B, C and D as answers on various musculoskeletal (MSK) conditions covering Trauma and Orthopaedic curricula were compiled. A standardised text prompt was used to generate and feed ChatGPT (both 3.5 and 4.0 versions) and Google Bard programs, which were then statistically analysed. Results: Significant differences were found between responses from Chat GPT 3.5 with Chat GPT 4.0 (Chi square = 27.2, P < 0.001) and on comparing both Chat GPT 3.5 (Chi square = 63.852, P < 0.001) with Chat GPT 4.0 (Chi square = 44.246, P < 0.001) with. Bard Google AI® had 100% efficiency and was significantly more efficient than both Chat GPT 3.5 with Chat GPT 4.0 (p < 0.0001). Conclusion: The results demonstrate the variable potential of the different AI generative chatbots (Chat GPT 3.5, Chat GPT 4.0 and Bard Google) in their ability to answer the MCQ of PG-level orthopaedic qualifying examinations. Bard Google AI® has shown superior performance than both ChatGPT versions, underlining the potential of such large language processing models in processing and applying orthopaedic subspecialty knowledge at a PG level. [ABSTRACT FROM AUTHOR]

To evaluate the response capabilities, in a public healthcare system otolaryngology job competition examination, of ChatGPT 3.5 and an internet-connected GPT-4 engine (Microsoft Copilot) with the real scores of otolaryngology specialists as the control group. In September 2023, 135 questions divided into theoretical and practical parts were input into ChatGPT 3.5 and an internet-connected GPT-4. The accuracy of AI responses was compared with the official results from otolaryngologists who took the exam, and statistical analysis was conducted using Stata 14.2. Copilot (GPT-4) outperformed ChatGPT 3.5. Copilot achieved a score of 88.5 points, while ChatGPT scored 60 points. Both AIs had discrepancies in their incorrect answers. Despite ChatGPT's proficiency, Copilot displayed superior performance, ranking as the second-best score among the 108 otolaryngologists who took the exam, while ChatGPT was placed 83rd. A chat powered by GPT-4 with internet access (Copilot) demonstrates superior performance in responding to multiple-choice medical questions compared to ChatGPT 3.5. [ABSTRACT FROM AUTHOR]

This study explores the potential of ChatGPT as a virtual tutor to facilitate self-directed learning (SDL) among adult learners in asynchronous online contexts. Although SDL has been identified as a critical skill, factors such as the lack of skills to find resources and the absence of a supportive learning environment could impede adult learners' ability to engage in SDL. By harnessing the power of AI, ChatGPT can assist adult learners in setting learning goals, locating available resources, designing personalized learning plans, monitoring their performance, and reflecting on their learning experiences, ultimately leading to the effective completion of SDL. This study also discusses several existing limitations and challenges, including the need for instructors to provide guidance on creating prompts to use ChatGPT effectively, the possibility of learners becoming reliant on ChatGPT, the significance of institutions to provide policies of proper use of ChatGPT, and the necessary for AI developers to continuously improve the algorithms and data used by the system to minimize the likelihood of providing incorrect and irrelevant information. Additionally, some questions are raised regarding the balance between the use of ChatGPT and engagement with human instructors to ensure optimal learning outcomes. This study aims to enlighten adult educators and practitioners towards the integration of ChatGPT in asynchronous online learning to facilitate adult learning. [ABSTRACT FROM AUTHOR]

In 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]

The release of ChatGPT has marked the dawn of a new information revolution that will transform how people communicate and make meaning. However, to date, little is known about the implications of ChatGPT for L2 composition instruction. To address this gap, the present study uses a systematic review design to synthesize available research on the educational potentials of ChatGPT as an instructional assistant, outline the implications of these potentials for L2 writing instruction, and discuss their practical applications. The findings, based on a meta-analysis of 42 research articles, demonstrate that ChatGPT can enhance L2 writing instruction by boosting learners’ motivation, automating instructional tasks, and offering instantaneous, personalized feedback to learners. These findings have important implications for harnessing the instructional potential of generative AI in L2 writing classes. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative approach to significantly enhance the precision and interpretability of brain tumor detection and segmentation. Our method ingeniously integrates the cutting-edge capabilities of the ChatGPT chatbot interface with a state-of-the-art multi-modal convolutional neural network (CNN). Tested rigorously on the BraTS dataset, our method showcases unprecedented performance, outperforming existing techniques in terms of both accuracy and efficiency, with an impressive Dice score of 0.89 for tumor segmentation. By seamlessly integrating ChatGPT, our model unveils deep-seated insights into the intricate decision-making processes, providing researchers and physicians with invaluable understanding and confidence in the results. This groundbreaking fusion holds immense promise, poised to revolutionize the landscape of medical imaging, with far-reaching implications for clinical practice and research. Our study exemplifies the transformative potential achieved through the synergistic combination of multi-modal CNNs and natural language processing, paving the way for remarkable advancements in brain tumor detection and segmentation. [ABSTRACT FROM AUTHOR]

The article presents the discussion on use of AI-based chatbots like OpenAI's ChatGPT in the chemical engineering industry, highlighting both the potential benefits and the challenges of deploying such technologies. Topics include recent updates to ChatGPT, differences between paid and free versions; and case studies demonstrating its application in process safety.

Copyright of Journal of Higher Education & Science / Yüksekögretim ve Bilim Dergisi is the property of Zonguldak Bulent Ecevit Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces the concepts of Chat Generative Pre-Trained Transformer (GPT) and artificial intelligence (AI). Chat GPT utilizes the GPT language model, which is trained using deep learning techniques and the transformer algorithm. It leverages the transformer's ability to understand human language and generate natural responses in conversations. ChatGPT is utilized in various contexts such as virtual assistants, chatbots, and interactive platforms to improve user interactions with technology. Our efforts also explore the wider domain of artificial intelligence, encompassing machine learning, deep learning, and natural language processing. The advancements in artificial intelligence (AI) technology have had a significant impact on various industries. The study emphasizes the significance of ongoing enhancement, safeguarding, confidentiality, and ethical deliberations in the creation and implementation of ChatGPT and AI chatbots. Ongoing research endeavors to improve the dependability and credibility of AI chatbot systems, despite obstacles such as bias and comprehensibility AI chatbots, can facilitate tailored and efficient human-machine interactions by giving priority to ethical considerations and promoting collaboration. In contemporary research initiatives, the integration of ChatGPT and AI technologies is of great significance, as it presents unique prospects for exploration and invention. ChatGPT, due to its capacity to understand and produce written content, functions as a potent instrument for enhancing communication, resolving issues, and disseminating knowledge in several fields. Hence, it is imperative for researchers to fully grasp the capabilities and consequences of AI, particularly on platforms like ChatGPT, to optimally harness the entire potential of these technologies in their respective fields. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) patients can benefit from personalized education on lifestyle and nutrition management strategies to enhance healthcare outcomes. The potential use of chatbots, introduced in 2022, as a tool for educating CKD patients has been explored. A set of 15 questions on lifestyle modification and nutrition, derived from a thorough review of three specific KDIGO guidelines, were developed and posed in various formats, including original, paraphrased with different adverbs, incomplete sentences, and misspellings. Four versions of AI were used to answer these questions: ChatGPT 3.5 (March and September 2023 versions), ChatGPT 4, and Bard AI. Additionally, 20 questions on lifestyle modification and nutrition were derived from the NKF KDOQI guidelines for nutrition in CKD (2020 Update) and answered by four versions of chatbots. Nephrologists reviewed all answers for accuracy. ChatGPT 3.5 produced largely accurate responses across the different question complexities, with occasional misleading statements from the March version. The September 2023 version frequently cited its last update as September 2021 and did not provide specific references, while the November 2023 version did not provide any misleading information. ChatGPT 4 presented answers similar to 3.5 but with improved reference citations, though not always directly relevant. Bard AI, while largely accurate with pictorial representation at times, occasionally produced misleading statements and had inconsistent reference quality, although an improvement was noted over time. Bing AI from November 2023 had short answers without detailed elaboration and sometimes just answered "YES". Chatbots demonstrate potential as personalized educational tools for CKD that utilize layman's terms, deliver timely and rapid responses in multiple languages, and offer a conversational pattern advantageous for patient engagement. Despite improvements observed from March to November 2023, some answers remained potentially misleading. ChatGPT 4 offers some advantages over 3.5, although the differences are limited. Collaboration between healthcare professionals and AI developers is essential to improve healthcare delivery and ensure the safe incorporation of chatbots into patient care. [ABSTRACT FROM AUTHOR]

The current article used real data to demonstrate the analysis and synthesis of Mixed Methods Research (MMR) data with generative Artificial Intelligence (Gen AI). I explore how reliable and valid Gen AI data outputs are and how to improve their use. The current content is geared towards enhancing methodological application regardless of field or discipline and includes access to a prompt library and examples of using outputs. The demonstration data used emanated from a study done in South Africa, with a quantitative sample size of 969 first-year engineering students and, for the qualitative part, 14 first-year students. In the current article, I compare my original analysis to ChatGPT results. Generative AI as a mind tool is best used with human insight, and I found this to be especially true when coding qualitative data. ChatGPT produced generic codes if asked to do inductive coding, and the results improved when training the Gen AI on human examples, which led to moderate and significant correlations between human and machine coding. The quantitative analysis was accurate for the descriptive statistics, but the researcher had to use best judgment to select the correct inferential analysis. Quantitative and qualitative analysis should be conducted separately in generative AI before asking the Chatbot for help with mixed methods results. In the current paper, I give guidelines and a tutorial on how to use chatbots in an ethically responsible and scientifically sound manner for research in social and human sciences. [ABSTRACT FROM AUTHOR]

With the recent arrival of publicly available AI chatbots like ChatGPT, Copilot, Gemini, and Claude follows a need for knowledge about how students in higher education perceive and use these tools, and what this might mean for their learning processes. This scoping review analyzes 24 empirical articles published between 1 January 2022 and 5 September 2023 on students' perceptions and use of AI chatbots in higher education. The articles were reviewed using a five-stage scoping review methodology. The findings underscore a global research interest in how students engage with AI chatbots, which is especially pronounced in Asia. The studies span diverse disciplines, with a predominance in science, technology, engineering, and mathematics disciplines. The empirical findings reveal that students perceive AI chatbots as highly useful and motivating as personal task assistants and for getting immediate feedback and help with writing, coding, and academic tasks. However, students are concerned about the accuracy and reliability of the responses from the chatbots, as well as potential negative impacts on their learning processes, critical thinking, discipline, and creativity. The purpose-driven use of AI chatbots among students and their potentially positive influence on motivation and learning processes offer insights for educators and policymakers. Our research concludes that while positive attitudes, perceptions, and critical use prevail, addressing students' concerns is crucial for responsible AI integration in higher education. [ABSTRACT FROM AUTHOR]

Purpose: ChatGPT is a generative artificial intelligence (AI) technology that can solve multiple complex tasks. ChatGPT-4 can facilitate educational empowerment in China through technology to understand and generate Chinese text. Although ChatGPT's benefits have been widely discussed, its educational capabilities have not been systematically assessed. This study provides evidence of and insights into the educational applications of AI tools in China. Design/Approach/Methods: This study uses various tests to systematically assess the latest iteration of the AI chatbot ChatGPT-4, including the Watson-Glaser Critical Thinking Appraisal (WGCTA), Five Core Competencies Questionnaire, and written test of China's 2022 National Teacher Certificate Examination (NTCE). Findings: The WGCTA results suggest that ChatGPT requires strong critical thinking. Compared with the other four competencies, the tool showed a lower aptitude for creativity. Regarding its educational applications, ChatGPT performed well on the 2022 NTCE written test. As technology enhances, ChatGPT and similar AI tools have potential applications in China for lesson planning, student self-learning, classroom interaction, and checking assignments. Originality/Value: This study systematically tested ChatGPT at a logical level and assessed its core competencies and educational applications. The study innovatively used 2022 NTCE data to test ChatGPT, with results providing support for the application of generative AI in future curricula and instruction in China. [ABSTRACT FROM AUTHOR]

Highlights: We live in a technology-driven age, so the education field must rationally consider chatbots powered by artificial intelligence (AI). Faced with increasing application of AI in education, teachers should be reflective educators, and students should be self-educators. AI chatbots will evolve into a new prosthesis before being institutionalized into the fabric of school education. Reflective educators, institutionalized educators, self-educators foreshadow the future rise of hybrid educators. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience. The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources, including AI-based chatbots. Session layer enhancements are also required, such as AI-based online proctoring and user authentication using Biometrics. These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms. It also needs the support of the Networking layer for IP-based geolocation features, the Virtual Private Network (VPN) feature, and the support of Software-Defined Networks (SDN) for optimum Quality of Service (QoS). Finally, in addition to these, non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics. [ABSTRACT FROM AUTHOR]

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