Search Results (64)

The integration of artificial intelligence (AI) tools, such as the chat generative pre-trained transformer (ChatGPT), into health professions education is rapidly accelerating, creating new opportunities for personalized learning and clinical preparation. These tools have demonstrated the potential to enhance learning efficiency and critical thinking. However, concerns regarding reliability, academic integrity, and potential overreliance highlight the need to better understand how healthcare students adopt and perceive these technologies in order to guide their effective and responsible integration into educational frameworks. This nationwide, cross-sectional, survey-based study was conducted between February and April 2024 among undergraduate students enrolled in medical, pharmacy, nursing, dental, and allied health programs in Saudi Arabia. An online questionnaire collected data on ChatGPT usage patterns, satisfaction, perceived benefits and risks, and attitudes toward integrating them into the curricula. Among 1044 participants, the prevalence of ChatGPT use was 69.25% (n = 723). Students primarily utilized the tool for content summarization, assignment preparation, and exam-related study. Key motivators included time efficiency and convenience, with improved learning efficiency and reduced study stress identified as major benefits. Conversely, major challenges included subscription costs and difficulties in formulating effective prompts. Furthermore, concerns regarding overreliance and academic misconduct were frequently reported. In conclusion, the adoption of generative AI tools such as ChatGPT among healthcare students in Saudi Arabia was high, driven by its perceived ability to enhance learning efficiency and personalization. To maximize its benefits and minimize risks, institutions should establish clear policies, provide faculty oversight, and integrate AI literacy into the education of health professionals. Full article

Artificial intelligence (AI) has transformed the educational landscape and reshaped learning experiences. Its adoption in higher education is increasing due to the recent plethora of AI tools (AITs) and their associated benefits. Romanian universities face the challenge of integrating AITs in the learning process. Thus, the students’ attitudes and behavioral intentions concerning the use of AITs are meaningful. Technology acceptance models have been widely used to investigate factors that affect the intention to use a technology. ChatGPT (Chat Generative Pre-Trained Transformer) is a popular AIT among students. Therefore, this study presents a conceptual model for successfully adopting ChatGPT in a Romanian Higher Education Institution (HEI). A case study was conducted at the Faculty of Cybernetics, Statistics, and Economic Informatics at the Bucharest University of Economic Studies to test this model. Structural equation modeling (SEM) was used to validate and inspect the model’s network of determinants. The findings indicate that perceived ease of use (PEOU) and perceived usefulness (PU) are key predictors of student satisfaction (S) and trust (T), which in turn promote loyalty (L) to the AIT. The paper provides a novel perspective by distinguishing between forms of social presence, and their impact on students’ satisfaction and trust, thereby enhancing the understanding of student behavior toward AIT adoption. Full article

Large language models (LLMs) such as Chat Generative Pre-Trained Transformer (ChatGPT) are increasingly used across domains, yet their generic training data and propensity for hallucination limit reliability in safety-critical fields like forestry. This paper outlines the conception and prototype of ForestGPT, a domain-specialised assistant designed to support forest professionals while preserving expert oversight. It addresses two looming risks: unverified adoption of generic outputs and professional mistrust of opaque algorithms. We propose a four-level development path: (1) pre-training a transformer on curated forestry literature to create a baseline conversational tool; (2) augmenting it with Retrieval-Augmented Generation to ground answers in local and time-sensitive documents; (3) coupling growth simulators for scenario modeling; and (4) integrating continuous streams from sensors, drones and machinery for real-time decision support. A Level-1 prototype, deployed at Futa Expo 2025 via a mobile app, successfully guided multilingual visitors and demonstrated the feasibility of lightweight fine-tuning on open-weight checkpoints. We analyse technical challenges, multimodal grounding, continual learning, safety certification, and social barriers including data sovereignty, bias and change management. Results indicate that trustworthy, explainable, and accessible LLMs can accelerate the transition to Forestry 5.0, provided that human-in-the-loop guardrails remain central. Future work will extend ForestGPT with full RAG pipelines, simulator coupling and autonomous data ingestion. Whilst exemplified in forestry, a complex, safety-critical, and ecologically vital domain, the proposed architecture and development path are broadly transferable to other sectors that demand trustworthy, domain-specific language models under expert oversight. Full article

The rapid proliferation of Chat Generative Pre-trained Transformer (ChatGPT) marks a pivotal moment in artificial intelligence, eliciting responses from academic shock to industrial awe. As these technologies advance from passive tools toward proactive, agentic systems, their transformative potential and inherent risks are magnified globally. This paper presents a comprehensive, critical review of ChatGPT’s impact across five key domains: natural language understanding (NLU), content generation, knowledge discovery, education, and engineering. While ChatGPT demonstrates profound capabilities, significant challenges remain in factual accuracy, bias, and the inherent opacity of its reasoning—a core issue termed the “Black Box Conundrum”. To analyze these evolving dynamics and the implications of this shift toward autonomous agency, this review introduces a series of conceptual frameworks, each specifically designed to illuminate the complex interactions and trade-offs within these domains: the “Specialization vs. Generalization” tension in NLU; the “Quality–Scalability–Ethics Trilemma” in content creation; the “Pedagogical Adaptation Imperative” in education; and the emergence of “Human–LLM Cognitive Symbiosis” in engineering. The analysis reveals an urgent need for proactive adaptation across sectors. Educational paradigms must shift to cultivate higher-order cognitive skills, while professional practices (including practices within education sector) must evolve to treat AI as a cognitive partner, leveraging techniques like Retrieval-Augmented Generation (RAG) and sophisticated prompt engineering. Ultimately, this paper argues for an overarching “Ethical–Technical Co-evolution Imperative”, charting a forward-looking research agenda that intertwines technological innovation with vigorous ethical and methodological standards to ensure responsible AI development and integration. Ultimately, the analysis reveals that the challenges of factual accuracy, bias, and opacity are interconnected and acutely magnified by the emergence of agentic systems, demanding a unified, proactive approach to adaptation across all sectors. Full article

Artificial intelligence (AI)-generated content (AIGC) is an innovative technology that utilizes machine learning, AI models, reward modeling, and natural language processing (NLP) to create diverse digital content such as videos, images, and text. It has the potential to support various human activities with significant implications in teaching and learning, facilitating heuristic teaching for educators. By using AIGC, teachers can create extensive knowledge content and effectively design instructional strategies to guide students, aligning with heuristic teaching. However, incorporating AIGC into heuristic teaching has controversies and concerns, which potentially mislead outcomes. Nevertheless, leveraging AIGC greatly benefits teachers in enhancing heuristic teaching. When integrating AIGC to support heuristic teaching, challenges and risks must be acknowledged and addressed. These challenges include the need for users to possess sufficient knowledge reserves to identify incorrect information and content generated by AIGC, the importance of avoiding excessive reliance on AIGC, ensuring users maintain control over their actions rather than being driven by AIGC, and the necessity of scrutinizing and verifying the accuracy of information and knowledge generated by AIGC to preserve its effectiveness. Full article

As artificial intelligence (AI) becomes increasingly utilized to perform tasks requiring human intelligence, patients who are pregnant may turn to AI for advice on over-the-counter (OTC) medications. However, medications used in pregnancy may pose profound safety concerns limited by data availability. This study focuses on a chatbot’s ability to accurately provide information regarding OTC medications as it relates to patients that are pregnant. A prospective, descriptive design was used to compare the responses generated by the Chat Generative Pre-Trained Transformer 3.5 (ChatGPT-3.5) to the information provided by UpToDate^®. Eighty-seven of the top pharmacist-recommended OTC drugs in the United States (U.S.) as identified by Pharmacy Times were assessed for safe use in pregnancy using ChatGPT-3.5. A piloted, standard prompt was input into ChatGPT-3.5, and the responses were recorded. Two groups independently rated the responses compared to UpToDate on their correctness, completeness, and safety using a 5-point Likert scale. After independent evaluations, the groups discussed the findings to reach a consensus, with a third independent investigator giving final ratings. For correctness, the median score was 5 (interquartile range [IQR]: 5–5). For completeness, the median score was 4 (IQR: 4–5). For safety, the median score was 5 (IQR: 5–5). Despite high overall scores, the safety errors in 9% of the evaluations (n = 8), including omissions that pose a risk of serious complications, currently renders the chatbot an unsafe standalone resource for this purpose. Full article

Teacher training in Physics often faces challenges related to engaging students and conveying abstract concepts effectively. Generative AI tools, such as ChatGPT, present transformative opportunities for designing innovative and tailored educational activities. This study investigates the impact of ChatGPT on pre-service Physics teacher training, focusing on its usability, effectiveness, and influence on participant satisfaction. Utilizing a quantitative research approach, two Likert-scale surveys were administered to 24 prospective Physics teachers in Ecuador, both before and after an intervention workshop. The workshop introduced participants to ChatGPT’s features and its applications in designing playful, Physics-focused learning activities. Results indicated a significant increase in familiarity with AI tools, enhanced activity design quality, and high satisfaction rates. Notably, $79\%$ of participants highlighted ChatGPT’s utility in adapting activities to diverse learning levels, and $83\%$ acknowledged its efficiency in reducing preparation time. These findings underscore ChatGPT’s potential to revolutionize Physics education by facilitating the creation of personalized and engaging learning resources. Future research should explore larger sample sizes and longitudinal impacts to fully realize the implications of AI-driven tools in educational contexts. Full article

Background/Objectives: Chat Generative Pretrained Transformer (ChatGPT) is a useful resource for individuals working in the healthcare field. This paper will include descriptions of several ways in which ChatGPT-4 can achieve greater accuracy in its diagnosis and treatment plans for ulcerative colitis (UC) and Crohn’s disease (CD) by following the guidelines set out by the European Crohn’s and Colitis Organization (ECCO). Methods: The survey, which comprised 102 questions, was developed to assess the precision and consistency of respondents’ responses regarding the UC and CD. The questionnaire incorporated true/false and multiple-choice questions, with the objective of simulating real-life scenarios and adhering to the ECCO guidelines. We employed Likert scales to assess the responses. The inquiries were put to ChatGPT-4 on the initial day, the 15th day, and the 180th day. Results: The 51 true or false items demonstrated stability over a six-month period, with an initial accuracy of 92.8% at baseline, 92.8% on the 15th day, and peaked to 98.0% on the 180th day. This finding suggests a negligible effect size. The accuracy of the multiple-choice questions was initially 90.2% on Day 1, reached its highest point at 92.2% on Day 15, and then decreased to 84.3% on Day 180. However, the reliability of the data was found to be suboptimal, and the impact was deemed negligible. A modest, transient increase in performance was observed at 15 days, which subsequently diminished by 180 days, resulting in negligible effect sizes. Conclusions: ChatGPT-4 demonstrates potential as a clinical decision support system for UC and CD, but its assessment is marked by temporal variability and the inconsistent execution of various tasks. Essential initiatives that should be carried out before involving artificial intelligence (AI) technology in IBD trials are routine revalidation, multi-rater comparisons, prompt standardization, and the cultivation of a comprehensive understanding of the model’s limitations. Full article

Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence (AI) engine. Research on tourism using ChatGPT has gained traction from scholars all over the world. However, limited studies on ChatGPT and the tourism industry have been conducted using an analysis of three-stage visit behavior. We analyzed the current trend in tourism research using ChatGPT with a bibliometric analysis based on the Scopus database. A total of 110 documents were used in this study for document review, and R studio Version 2022.12.0+353 was used to analyze the documents. The results present indicators for a systematic review of the documents, such as the number of publications and co-word analysis. A theoretical system was developed in this study to explore travelers’ behavior using ChatGPT in the pre-, during, and post-travel periods. The study results contribute to the development of the tourism industry to understand tourist behavior using ChatGPT. Full article

Recent advances in natural language processing (NLP) have enabled the development of powerful language models such as Generative Pre-trained Transformers (GPTs). This study evaluates the performance of ChatGPT in generating news summaries by comparing them with summaries written by professional journalists at The New York Times. Using BERTScore as the primary metric, we assessed the semantic similarity between ChatGPT-generated and human-authored summaries. We further employed OpenAI’s moderation API to examine the extent to which each set of summaries contained potentially biased, inflammatory, or violent language. The results indicate that ChatGPT-generated summaries exhibit a high degree of contextual alignment with human-written summaries, achieving a BERTScore F1-score above 0.87. Moreover, ChatGPT outputs consistently omit language flagged as problematic by moderation algorithms, producing summaries that are less likely to include harmful or polarizing content—a feature we define as moderation-friendly summarization. These findings suggest that ChatGPT can serve as a valuable tool for automated news summarization, offering content that is both contextually accurate and aligned with content moderation standards, thereby supporting more objective and responsible news dissemination. Full article

Beautimeter is a new tool powered by generative pre-trained transformer (GPT) technology, designed to evaluate architectural and urban beauty. Rooted in Christopher Alexander’s theory of centers, this work builds on the idea that all environments possess, to varying degrees, an innate sense of life. Alexander identified 15 fundamental properties, such as levels of scale and thick boundaries, that characterize living structure, which Beautimeter uses as a basis for its analysis. By integrating GPT’s advanced natural language processing capabilities, Beautimeter assesses the extent to which a structure embodies these 15 properties, enabling a nuanced evaluation of architectural and urban aesthetics. Using ChatGPT4o, the tool helps users generate insights into the perceived beauty and coherence of spaces. We conducted a series of case studies, evaluating images of architectural and urban environments, as well as carpets, paintings, and other artifacts. The results demonstrate Beautimeter’s effectiveness in analyzing aesthetic qualities across diverse contexts. Our findings suggest that by leveraging GPT technology, Beautimeter offers architects, urban planners, and designers a powerful tool to create spaces that resonate deeply with people. This paper also explores the implications of such technology for architecture and urban design, highlighting its potential to enhance both the design process and the assessment of built environments. Full article

Can Chat Generative Pre-Trained Transformer or “ChatGPT” and other Large Language Models (LLMs) be used to create challenging and creative assignments for undergraduate students? This article explores the use of ChatGPT as an interview proxy for students. Drawing inspiration from the medical community’s concept of the simulated patient, ChatGPT was employed to act as an imagined proxy for a figure from the world of sports. Students in an undergraduate “Politics of Sport” course conducted interviews with the ChatGPT proxy using questions derived from peer-reviewed academic research. The assignment had two main objectives: to challenge students to engage meaningfully with academic research and apply it to real-world situations by simulating real-world conditions and to help students consider the limitations of ChatGPT when handling real-world scenarios. Despite some issues that arose during the module, student feedback and coursework indicated that this approach was engaging, fun, and creative for students. It is suggested that this method could be effectively applied across various academic disciplines. Full article

ChatGPT (Chat-Generative Pre-trained Transformer) is an accessible and innovative tool for obtaining healthcare information. This study evaluates the quality and reliability of information provided by ChatGPT 4.0^® regarding Obstructive Sleep Apnea (OSA), comparing it with responses from sleep medicine specialists. Thirty frequently asked questions about OSA were posed to ChatGPT 4.0^® and two expert physicians. Responses from both sources (V1: AI and V2: Medical Experts) were blindly evaluated by a panel of six specialists using a five-point Likert scale across precision, relevance, and depth dimensions. The AI-generated responses (V1) achieved a slightly higher overall score compared to those from medical experts (V2), although the difference was not statistically significant (p > 0.08). These results suggest that both sources offer comparable quality and content. Additionally, ChatGPT’s responses were clear and easily understandable, providing an accessible explanation of OSA pathology. Full article

Although large language models (LLMs) like the Generative Pre-trained Transformer (GPT) are growing increasingly popular, much remains to learn about their potential for website quality auditing. The article evaluates the performance of LLM AI plugins (GPT models) in website and web application auditing. The author built and tested two original ChatGPT-4o Plus (OpenAI) plugins: Website Quality Auditor (WQA) and WebGIS Quality Auditor (WgisQA). Their performance was cautiously and carefully analysed and compared to traditional auditing tools. The results demonstrated the limitations of the AI plugins, including their propensity for false outcomes. The general conclusion is that using AI tools without considering their characteristics may lead to the propagation of AI hallucinations in audit reports. The study fills in the research gap with the results on the capabilities and limitations of AI plugins in the context of auditing. It also suggests further directions for improvement. Full article

ChatGPT, or Chat Generative Pre-trained Transformer, developed by OpenAI, is a versatile chatbot known for generating human-like text responses. Since its launch in November 2022, it has sparked interest and debate. This bibliometric study aimed to explore ChatGPT-related publications using the Web of Science database from 2023 to June 2024. Original articles in English were retrieved on 24 June 2024, using the topic field “ChatGPT”. Citation records were analyzed using bibliometrix 4.1 and VOSviewer 1.6.20. Between January 2023 and 24 June 2024, 3231 original articles on ChatGPT were published in 1404 journals, with an average citation rate of 5.6 per article. The United States led with 877 articles, followed by China and India. The University of California System, Harvard University, and the State University System of Florida were the most prolific institutions. Keyword co-occurrence network analysis revealed the interdisciplinary nature of ChatGPT research, particularly contributions in healthcare, education, and technology. In conclusion, this bibliometric analysis identified critical areas of ChatGPT research focus, such as applications in educational settings and its ethical implications. These findings are crucial for fostering further advancements that leverage ChatGPT’s capabilities while mitigating its risks. Full article