Since 2023, ChatGPT has been leading a research boom in large language models. Research on the applications of large language models in various fields is also being explored. The aim of this study ...was to explore the use of ChatGPT/GPT-4 for post-surgery patient follow-up after oral surgery. Thirty questions that are the most commonly asked or may be encountered during follow-up and in daily practice were collected to test ChatGPT/GPT-4′s responses. A standard prompt was used for each question. The responses given by ChatGPT/GPT-4 were evaluated by three experienced oral and maxillofacial surgeons to assess the suitability of this technology for clinical follow-up, based on the accuracy of medical knowledge and rationality of the advice in ChatGPT/GPT-4′s responses. ChatGPT/GPT-4 achieved full marks in terms of both the accuracy of its medical knowledge and the rationality of its recommendations. Additionally, ChatGPT/GPT-4 was able to accurately sense patient emotions and provide them with reassurance. In conclusion, ChatGPT/GPT-4 could be used for patient follow-up after oral surgeries, but this should be done with careful consideration of the technology’s current limitations and under the guidance of healthcare professionals.
In recent years, artificial intelligence (AI) and machine learning have been transforming the landscape of scientific research. Out of which, the chatbot technology has experienced tremendous ...advancements in recent years, especially with ChatGPT emerging as a notable AI language model. This comprehensive review delves into the background, applications, key challenges, and future directions of ChatGPT. We begin by exploring its origins, development, and underlying technology, before examining its wide-ranging applications across industries such as customer service, healthcare, and education. We also highlight the critical challenges that ChatGPT faces, including ethical concerns, data biases, and safety issues, while discussing potential mitigation strategies. Finally, we envision the future of ChatGPT by exploring areas of further research and development, focusing on its integration with other technologies, improved human-AI interaction, and addressing the digital divide. This review offers valuable insights for researchers, developers, and stakeholders interested in the ever-evolving landscape of AI-driven conversational agents. This study explores the various ways ChatGPT has been revolutionizing scientific research, spanning from data processing and hypothesis generation to collaboration and public outreach. Furthermore, the paper examines the potential challenges and ethical concerns surrounding the use of ChatGPT in research, while highlighting the importance of striking a balance between AI-assisted innovation and human expertise. The paper presents several ethical issues in existing computing domain and how ChatGPT can invoke challenges to such notion. This work also includes some biases and limitations of ChatGPT. It is worth to note that despite of several controversies and ethical concerns, ChatGPT has attracted remarkable attentions from academia, research, and industries in a very short span of time.
The increasing use of Advanced Natural Language Processing (ANLP) models, particularly ChatGPT-4, presents opportunities and challenges to management education and research. These models can enhance ...the style, creativity, and analytical power of research papers, potentially shifting human scholars' roles from creators to ‘prompters’. If machines can perform educational and research tasks more effectively the role of human educators becomes a salient question in a world in which ANLP models offer clear, coherent, and polished insights, the use of which has potentially paradoxical possibilities. From one perspective, a new type of high-quality scholarship and education characterized by strong human involvement that synergistically leverages ANLP models' analytical capabilities, enabling human scholars to probe complex phenomena and make management research truly meaningful and impactful for broader audiences, is possible. We explore these questions through an ‘ideal type’ conceptualization of the possible relations between AI and management education and research.
•Fusion of an empirical grasp of AI technologies with explanation drawing on social and organization theory.•Stressing the central role that ‘sensemaking’ must play in AI use in management education.•Creation of an ideal type framework for understanding the relations between huma and artificial intelligence.•Systematic discussion of the empirical relevance of AI for management education.•Statement of an agenda for further research in terms of a series of research questions.
TPACK in the age of ChatGPT and Generative AI Mishra, Punya; Warr, Melissa; Islam, Rezwana
Journal of digital learning in teacher education,
10/2/2023, Volume:
39, Issue:
4
Journal Article
Peer reviewed
Open access
The educational impact of Generative AI (GenAI) technologies, such as ChatGPT, has received significant attention. We use the TPACK framework to discuss the types of knowledge teachers require to ...effectively use GenAI tools. We highlight the qualities of GenAI that make it like other digital technologies (they are protean, opaque, and unstable) as well as qualities that make it revolutionary (namely, they are generative and social). We describe how these traits affect specific knowledge domains (TK, TPK, TCK, XK, and TPACK) and explore implications for educators. Finally, we argue for a more expansive description of Contextual Knowledge (XK), going beyond the immediate context to include considerations of how GenAI will change individuals, society and, through that, the broader educational context.
Artificial Intelligence (AI)-based ChatGPT developed by OpenAI is now widely accepted in several fields, including education. Students can learn about ideas and theories by using this technology ...while generating content with it. ChatGPT is built on State of the Art (SOA), like Deep Learning (DL), Natural Language Processing (NLP), and Machine Learning (ML), an extrapolation of a class of ML-NLP models known as Large Language Model (LLMs). It may be used to automate test and assignment grading, giving instructors more time to concentrate on instruction. This technology can be utilised to customise learning for kids, enabling them to focus more intently on the subject matter and critical thinking ChatGPT is an excellent tool for language lessons since it can translate text from one language to another. It may provide lists of vocabulary terms and meanings, assisting students in developing their language proficiency with resources. Personalised learning opportunities are one of ChatGPT’s significant applications in the classroom. This might include creating educational resources and content tailored to a student’s unique interests, skills, and learning goals. This paper discusses the need for ChatGPT and the significant features of ChatGPT in the education system. Further, it identifies and discusses the significant applications of ChatGPT in education. Using ChatGPT, educators may design lessons and instructional materials specific to each student’s requirements and skills based on current trends. Students may work at their speed and concentrate on the areas where they need the most support, resulting in a more effective and efficient learning environment. Both instructors and students may profit significantly from using ChatGPT in the classroom. Instructors may save time on numerous duties by using this technology. In future, ChatGPT will become a powerful tool for enhancing students’ and teachers’ experience.
•Artificial Intelligence (AI)-based ChatGPT developed by OpenAI is now widely accepted in several fields, including education.•Students can learn about ideas and theories by using this technology while generating content with it.•ChatGPT is a promising tool for language lessons since it can translate text from one language to another.•This paper discusses the need for ChatGPT and the significant features of ChatGPT in the education system.•The paper identifies and discusses the significant applications of ChatGPT in education.
With the prevalence of generative AI tools like ChatGPT, automated detectors of AI-generated texts have been increasingly used in education to detect the misuse of these tools (e.g., cheating in ...assessments). Recently, the responsible use of these detectors has attracted a lot of attention. Research has shown that publicly available detectors are more likely to misclassify essays written by non-native English speakers as AI-generated than those written by native English speakers. In this study, we address these concerns by leveraging carefully sampled large-scale data from the Graduate Record Examinations (GRE) writing assessment. We developed multiple detectors of ChatGPT-generated essays based on linguistic features from the ETS e-rater engine and text perplexity features, and investigated their performance and potential bias. Results showed that our carefully constructed detectors not only achieved near-perfect detection accuracy, but also showed no evidence of bias disadvantaging non-native English speakers. Findings of this study contribute to the ongoing debates surrounding the formulation of policies for utilizing AI-generated content detectors in education.
•We study the potential bias in detecting ChatGPT-generated essays in a large-scale assessment.•Detectors based on linguistic features showed near-perfect detection performance.•Detectors built using well-sampled data from GRE do not show bias against non-native English speakers.•Findings shed light on the fairness in applying automated LLM detectors in education.
The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some ...types of commits that cannot be traced by the SZZ algorithm, referred to as "ghost commits". The evaluation of how these ghost commits impact the SZZ implementations remains limited. Moreover, these implementations have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories.
Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the corresponding bug-inducing commit(s) as a standard practice. As of v6.1-rc5, 76,046 pairs of bug-fixing patches and bug-inducing commits are available. This provides a unique opportunity to evaluate the SZZ algorithm on a large dataset that has been created and reviewed by project developers, entirely independently of the biases of software engineering researchers.
In this paper, we apply six SZZ implementations to 76,046 pairs of bug-fixing patches and bug-introducing commits from the Linux kernel. Our findings reveal that SZZ algorithms experience a more significant decline in recall on our dataset (↓ 13.8%) as compared to prior findings reported by Rosa et al., and the disparities between the individual SZZ algorithms diminish. Moreover, we find that 17.47% of bug-fixing commits are ghost commits. Finally, we propose Tracing-Commit SZZ (TC-SZZ), that traces all commits in the change history of lines modified or deleted in bug-fixing commits. Applying TC-SZZ to all failure cases, excluding ghost commits, we found that TC-SZZ could identify 17.7% of them. Our further analysis based on git log found that 34.6% of bug-inducing commits were in the function history, 27.5% in the file history (but not in the function history), and 37.9% not in the file history. We further evaluated the effectiveness of ChatGPT in boosting the SZZ algorithm's ability to identify bug-inducing commits in the function history, in the file history and not in the file history.
The purpose of this article is to organize the concepts related to the digitization of the modern enterprise and to identify potential digitization directions for process automation using the ...integration of RPA and GPT technologies. As research methods, the article uses a literature review of the past 20 years and presents a case study. The first part of the article reviews the concepts of digitization and process automation. A distinction was made in the understanding of the terms, which became the basis for describing the possibilities of integrating ChatGPT with RPA. The key prospects for using ChatGPT were then identified, and limitations were discussed. The possibility of using ChatGPT integration with RPA was illustrated with a case study of a service company form green energy sector.
