AI Language Research Articles

OpenAI ChatGPT, Google Bard, and Microsoft Bing: Similarity Index and Analysis of Artificial Intelligence-Based Contents

This research compares AI language models—specifically, OpenAI's ChatGPT, Google's Bard, and Microsoft's Bing—by analyzing their originality, use of external sources, and performance. Conducted on August 5, 2023, the study evaluates how these models respond to different queries, revealing distinct characteristics. ChatGPT stands out with lower similarity scores and lesser reliance on online sources, indicating its potential for creating more unique content. In contrast, Bard and Bing show higher similarity scores, suggesting they draw more from available online content, which could be beneficial for tasks requiring context-rich information. While ChatGPT and Bard excel in grasping context, generating substantial content, and offering relevant insights, there are concerns about accuracy and consistency across queries. Notably, Bing's focus on aiding content creation rather than direct essay generation showcases diverse strengths among AI models. As AI technology progresses, refining these models and addressing inconsistencies will improve their usefulness across various applications. These findings guide users in choosing AI tools that fit their content needs and ensure the credibility of generated outputs.

Delving into educators’ perspectives on ChatGPT in management education: a qualitative exploration

PurposeThis study aims to delve into the perspectives of educators on integrating ChatGPT, an AI language model into management education. In the current research, educators were asked to talk as widely as possible about the perceived benefits, limitations of ChatGPT in management education and strategies to improve ChatGPT for management education. Also, shedding light on what motivates or inhibits them to use ChatGPT in management education in the Indian context.Design/methodology/approachInterpretative phenomenological analysis commonly uses purposive sampling. In this research, the purpose is to delve into educators’ perspectives on ChatGPT in management education. The data was collected from the universities offering management education in Uttarakhand, India. The final sample size for the study was constrained to 57 educators, reflecting the point of theoretical saturation in data collection.FindingsThe present study involved educators discussing the various advantages of using ChatGPT in the context of management education. When educators were interviewed, their responses were categorized into nine distinct sub-themes related to the benefits of ChatGPT in management education. Similarly, when educators were asked to provide their insights on the limitations of using ChatGPT in management education, their responses were grouped into six sub-themes that emerged during the interviews. Furthermore, in the process of interviewing educators about potential strategies to enhance ChatGPT for management education, their feedback was organized into seven sub-themes, reflecting the various approaches suggested by the educators.Research limitations/implicationsIn the qualitative study, perceptions and experiences of educators at a certain period are captured. It would be necessary to conduct longitudinal research to comprehend how perceptions and experiences might change over time. The study’s exclusive focus on management education may not adequately reflect the experiences and viewpoints of educators in another discipline. The findings may not be generalizable and applicable to other educational disciplines.Practical implicationsThe research has helped in identifying the strengths and limitations of ChatGPT as perceived by educators for management education. Understanding educators’ perceptions and experiences with ChatGPT provided valuable insight into how the tool is being used in real-world educational settings. These insights can guide higher education institutions, policymakers and ChatGPT service providers in refining and improving the ChatGPT tool to better align with the specific needs of management educators.Originality/valueAmid the rising interest in ChatGPT’s educational applications, a research gap exists in exploring educators’ perspectives on AI tools like ChatGPT. While some studies have addressed its role in fields like medical, engineering, legal education and natural sciences, the context of management education remains underexplored. This study focuses on educators’ experiences with ChatGPT in transforming management education, aiming to reveal its benefits, limitations and factors influencing adoption. As research in this area is limited, educators’ insights can guide higher education institutions, ChatGPT providers and policymakers in effectively implementing ChatGPT in Indian management education.

Foot and Ankle Patient Education Materials and Artificial Intelligence Chatbots: A Comparative Analysis.

The purpose of this study was to perform a comparative analysis of foot and ankle patient education material generated by the AI chatbots, as they compare to the American Orthopaedic Foot and Ankle Society (AOFAS)-recommended patient education website, FootCareMD.org. ChatGPT, Google Bard, and Bing AI were used to generate patient educational materials on 10 of the most common foot and ankle conditions. The content from these AI language model platforms was analyzed and compared with that in FootCareMD.org for accuracy of included information. Accuracy was determined for each of the 10 conditions on a basis of included information regarding background, symptoms, causes, diagnosis, treatments, surgical options, recovery procedures, and risks or preventions. When compared to the reference standard of the AOFAS website FootCareMD.org, the AI language model platforms consistently scored below 60% in accuracy rates in all categories of the articles analyzed. ChatGPT was found to contain an average of 46.2% of key content across all included conditions when compared to FootCareMD.org. Comparatively, Google Bard and Bing AI contained 36.5% and 28.0% of information included on FootCareMD.org, respectively (P < .005). Patient education regarding common foot and ankle conditions generated by AI language models provides limited content accuracy across all 3 AI chatbot platforms. Level IV.

Generative AI for pentesting: the good, the bad, the ugly

This paper examines the role of Generative AI (GenAI) and Large Language Models (LLMs) in penetration testing exploring the benefits, challenges, and risks associated with cyber security applications. Through the use of generative artificial intelligence, penetration testing becomes more creative, test environments are customised, and continuous learning and adaptation is achieved. We examined how GenAI (ChatGPT 3.5) helps penetration testers with options and suggestions during the five stages of penetration testing. The effectiveness of the GenAI tool was tested using a publicly available vulnerable machine from VulnHub. It was amazing how quickly they responded at each stage and provided better pentesting report. In this article, we discuss potential risks, unintended consequences, and uncontrolled AI development associated with pentesting.

Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students' integrity matter?

ChatGPT, an AI-powered language model, is revolutionising the academic world. Scholars, researchers, and students use its advanced capabilities to achieve their educational objectives, including generating innovative ideas, delivering assignments, and conducting extensive research projects. Nevertheless, the use of ChatGPT among students is contentious, giving rise to significant apprehensions regarding integrity and AI-facilitated deceit. At the same time, scholarly communities currently need more well-defined standards for adopting such academia-oriented technology. This study aims to determine students' use of ChatGPT using the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), notably the role of students' integrity in determining adoption behaviour. The analysis of 921 responses demonstrated that the utilisation of ChatGPT is influenced positively by performance expectancy, social influence, educational self-efficacy, technology self-efficacy, and personal anxiety. Conversely, student integrity was found to negatively impact usage. Remarkably, student integrity has a positive moderating effect between effort expectancy and ChatGPT usage. At the same time, it has a negative moderating effect on the link between performance expectancy and technology self-efficacy with ChatGPT usage. Hence, we propose that the academic community, AI language model developers, publishers, and relevant stakeholders collaborate to establish explicit rules for the utilisation of AI chatbots in an ethical manner for educational purposes.

Tell me a story: a framework for critically investigating AI language models

ABSTRACT Large language models are rapidly being rolled out into high-stakes fields like healthcare, law, and education. However, understanding of their design considerations, operational logics, and implicit biases remains limited. How might these black boxes be understood and unpacked? In this article, we lay out an accessible but critical framework for inquiry, a pedagogical tool with four dimensions. Tell me your story investigates the design and values of the AI model. Tell me my story explores the model’s affective warmth and its psychological impacts. Tell me our story probes the model’s particular understanding of the world based on past statistics and pattern-matching. Tell me ‘their’ story compares the model’s knowledge on dominant (e.g. Western) versus ‘peripheral’ (e.g. Indigenous) cultures, events, and issues. Each mode includes sample prompts and key issues to raise. The framework aims to enhance the public’s critical thinking and technical literacy around generative AI models.

ChatGPT or Bard: Who is a better Certified Ethical Hacker?

In this study, we compare two leading Generative AI (GAI) tools, ChatGPT and Bard, specifically in Cybersecurity, using a robust set of standardized questions from a validated Certified Ethical Hacking (CEH) dataset. In the rapidly evolving domain of Generative AI (GAI) and large language models (LLM), a comparative analysis of tools becomes essential to measure their performance. We determine the Comprehensiveness, Clarity, and Conciseness of the AI-generated responses through a detailed questioning-based framework. The study revealed an overall accuracy rate of 80.8 % for ChatGPT and 82.6 % for Bard, indicating comparable capabilities and specific differences. Bard slightly outperformed ChatGPT in accuracy, while ChatGPT exhibited superiority in Comprehensiveness, Clarity, and Conciseness of responses. Introducing a confirmation query like “Are you sure?” increased accuracy for both generative AI tools, illustrating the potential of iterative query processing in enhancing GAI tools' effectiveness. The readability evaluation placed both tools at a college reading level, with Bard marginally more accessible. While evaluating certain questions, a distinct pattern emerged where Bard provided generic denials of assistance while ChatGPT referenced “ethics.” This discrepancy illustrates the contrasting philosophies of the developers of these tools, with Bard possibly following stricter guidelines, especially in sensitive topics like Cybersecurity. We explore the implications and identify key areas for future research that become increasingly relevant as GAI tools see broader adoption.

From Causal Inferences to Predictive Analytics: Using AI to Settle on Damages

From the perspective of both plaintiffs and defendants, the measurement of damages quantum is of the utmost importance. It is surprising to see this process left entirely to the court’s discretion, as quantum is traditionally considered a question of fact. Quantifying damages presents significant challenges due to the subjective nature of court discretion, leading to uncertainty for both plaintiffs and defendants. This research addresses this issue by examining difficult-to-quantify contract damages through empirical and comparative methodologies. Based on prior studies on French civil law and American common law, this empirical study involved quantitative analysis of various contract cases. Methodological advancements, including Machine Learning (ML), and Natural Language Processing (NLP) techniques, facilitated automated extraction and analysis of key variables. With a focus on overcoming sample size limitations and enhancing accuracy, this study achieved a classification accuracy of over 85% for identified essential variables. The more recent integration of generative AI and Large Language Modeling marked significant progress in quantifying damages. I conclude with recommendations for sustainable management practices in this field.

Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!

Abstract Machine learning (ML) and deep learning (DL) have potential applications in medicine. This overview explores the applications of AI in cardiovascular imaging, focusing on echocardiography, cardiac MRI (CMR), coronary CT angiography (CCTA), and CT morphology and function. AI, particularly DL approaches like convolutional neural networks, enhances standardization in echocardiography. In CMR, undersampling techniques and DL-based reconstruction methods, such as variational neural networks, improve efficiency and accuracy. ML in CCTA aids in diagnosing coronary artery disease, assessing stenosis severity, and analyzing plaque characteristics. Automatic segmentation of cardiac structures and vessels using AI is discussed, along with its potential in congenital heart disease diagnosis and 3D printing applications. Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities. The growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns. The “black box” problem in DL models poses challenges for interpretability crucial in clinical practice. Evaluation metrics like ROC curves, image quality, clinical relevance, diversity, and quantitative performance assess GAI models. Automation bias highlights the risk of unquestioned reliance on AI outputs, demanding careful implementation and ethical frameworks. Ethical considerations involve transparency, respect for persons, beneficence, and justice, necessitating standardized evaluation protocols. Health disparities emerge if AI training lacks diversity, impacting diagnostic accuracy. AI language models, like GPT-4, face hallucination issues, posing ethical and legal challenges in healthcare. Regulatory frameworks and ethical governance are crucial for fair and accountable AI. Ongoing research and development are vital to evolving AI ethics.

AI, ML, and Large Language Models in Cybersecurity

AI, ML, and Large Language Models in Cybersecurity

Illustrating Tomorrow: The Role of Comics in Engaging Research Participants in Disruptive Futures Research

This study examines the application of comics in futures research to involve stakeholders in infrastructure strategy discussions for disruptive futures. It employs a graphic novel called “Vanishing Point! Our Built World,” which augments generative AI art with human storytelling to transport participants into a speculative 2150 with a solarpunk theme. The narrative applies the concept of future-self continuity and a temporal discontinuity framework to promote visions of sustainability for both humanity and the environment. The research emphasises the ability of comics to foster meaningful discussions on future infrastructure coordination by incorporating Social Construction Theory, Social Construction of Technology Theory, and Actor Network Theory. Comics being adapted into videos and used in workshops highlight their effectiveness in promoting engagement and facilitating semi-structured discussions. This demonstrates comics as a dynamic tool in futures research and participatory methodologies. Relevant themes: Anthropocene, Ecocene and Climate Change/Post-capitalism/Utopias, Eutopias and Dystopias/Solar Punk and its relations/Artificial Intelligence and Automation/Wicked Problems/Futures and Metaphor/Reimagining Futures Tools and Methodologies. Statement acknowledging use of Generative AI Art and copyright ownership: “Vanishing Point! Our Built World” graphic novel artwork was created with Midjourney generative AI. These images were created using the author’s prompts and ideas. The author owns these AI-generated photos and their derivatives. Synthesia was used to create animated films and AI-generated avatars from comic artwork. Statement on the use of Generative AI in the production of this manuscript: Referring to Author Guidelines on Using Generative AI and Large Language Models| Sage Learning Resources (sagepub.com ), this manuscript employed various AI tools to assist with research and writing. ChatGPT4 and its plugin, Scholar AI, helped identify relevant literature in addition to traditional research. Litmaps revealed article linkages, clarifying the research landscape. ChatGPT4 critiqued the manuscript. Quillbot improved the manuscript's clarity and consistency by improving its language.

Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians

ImportanceThe workload of clinical documentation contributes to healthcare costs and professional burnout. The advent of generative AI language models presents a promising solution. The perspective of clinicians may contribute to effective and responsible implementation of such tools. ObjectiveTo evaluate 3 uses for generative AI for clinical documentation in pediatric emergency medicine (PEM), measuring time savings, effort reduction, physician attitudes, and identifying potential risks and barriers. DesignA mixed-methods study. SettingSingle Pediatric Emergency Department. Participants10 PEM attending physicians. InterventionParticipants were asked to write a supervisory note for four clinical scenarios, with varying level of complexity, twice without any assistance and twice with the assistance of ChatGPT Version 4.0. Participants evaluated two additional ChatGPT-generated clinical summaries: a structured handoff and a visit summary for a family, written at an 8th grade reading level. Finally, a semi-structured interview was performed to assess physicians’ perspective on the use of ChatGPT in PEM. Main Outcomes and MeasuresBetween subjects’ comparison of the effort and time taken to complete the supervisory note with and without ChatGPT assistance. Effort was measured using a self-reported Likert scale of 0-10. Physicians’ scoring of and attitude toward the ChatGPT-generated summaries was measured using a 0-10 Likert scale and open-ended questions. Summaries were scored for completeness, accuracy, efficiency, readability, and overall satisfaction. A thematic analysis was performed to analyze the content of the open-ended questions and to identify key themes. ResultsChatGPT yielded a 40% reduction in time and a 33% decrease in effort for supervisory notes in intricate cases, with no discernible impact on simpler ones. ChatGPT-generated summaries for structured handoffs and family-letters were highly rated, ranging from 7.0 to 9.0 out of 10, and most participants favored their inclusion in clinical practice. However, there were several critical reservations, out of which a set of general recommendations for applying ChatGPT to clinical summaries was formulated. ConclusionsPEM attendings in our study perceived that ChatGPT can deliver high-quality summaries while saving time and effort in many scenarios, but not all.

Invitation to join the Healthcare AI Language Group: HeALgroup.AI Initiative

Invitation to join the Healthcare AI Language Group: HeALgroup.AI Initiative

Deciphering Deception: How Different Rhetoric of AI Language Impacts Users’ Sense of Truth in LLMs

Users are increasingly exposed to AI-generated language, presenting potential deception and communication risks. This study delved into the rhetorical aspect of AI-generated language influencing users’ truth discernment. We conducted a user study comparing three levels of rhetorical presence and four persuasive rhetorical elements, using interviews to understand users’ truth-detection methods. Results showed that outputs with fewer rhetorical elements posed challenges for users in distinguishing truth from false, while those with more rhetoric often misled users into false truths. Users’ AI expectations influenced truth judgments, with responses meeting expectations perceived as more truthful. Casual, human-like responses were often deemed false, while technical, precise AI responses were preferred. This research emphasizes that rhetorical elements of AI language can significantly bias individuals regardless of a statement’s actual truth. For enhanced transparency in human-AI communication, it is advisable for AI designs to thoughtfully integrate rhetorical elements and establish guiding principles aimed at minimizing the potential for deceptive responses.

Generative AI: An AI paradigm shift in the making?

AbstractIt is sometimes difficult to evaluate progress in Generative AI, that is, image generation and large language models. This may be because they represent a paradigm shift in AI, and the traditional ways of developing, evaluating, understanding, and deploying AI systems no longer apply. Instead, we need to develop new such approaches, possibly by extending those currently in use in cognitive neuroscience and psychology. In this manner, a new AI paradigm can be created, providing a significant leap in AI research and practice.

Who has the last word? Lessons from using ChatGPT to develop an AI-based Spanish writing assistant

This article deals with the complex relationship between human intelligence and so-called artificial intelligence in the context of an ongoing project to develop a writing assistant for Spanish learners, both native and non-native. The authors have used ChatGPT to generate validation data to assess the performance of the language model on different parameters before testing it with real users. The article describes how they approached the generation of validation data, what they learned along the way, and what the results were. It first introduces the project and describes its main phases. It then explains the criteria the authors used to determine the types of problems to be covered by the validation data, and how they instructed the chatbot to generate this data. Finally, it summarises the main lessons they learnt from working with the chatbot and some of the challenges they faced in getting it to work properly. The description is accompanied by numerous examples. By engaging with the chatbot in a critical and constructive way, and by establishing close interdisciplinary collaboration with IT specialists, the authors conclude that the key challenge is to demonstrate in practice that humans, not the chatbot, are the masters. In this context, they argue that generative AI language models are not here to replace us, but to help us produce faster and with higher quality to meet our growing and increasingly diverse demands for a better life.

ChatGPT and ELT: Exploring Teachers’ Voices

This study investigates the perceptions of English language teachers regarding the use of ChatGPT in English Language Teaching (ELT). The study aims to fill the research gap by exploring teachers' perspectives on the integration of ChatGPT as an instructional tool and its implications for ELT practices. Using a mixed methods approach, the study employed focus group discussions and an online survey to gather insights from a sample of 115 English language teachers in Metro Manila, Philippines. Findings revealed English language teachers’ perspectives on the use of ChatGPT in ELT including potential use of ChatGPT in ELT (e.g., integration for vocabulary expansion and writing practice and enhancing language fluency and communication), potential benefits of ChatGPT to English languge learners (e.g., translation and language comprehension support, conversational practice and language development, access to information and cultural understanding), and potential limitations of using ChatGPT in ELT (e.g., cheating and plagiarism, lack contextual understanding and nuance, reliability and accuracy of information). These findings shed light on teachers' perceptions of ChatGPT as a language teaching tool including ChatGPT’s integration into ELT, use of ChatGPT in writing activities, use of ChatGPT in improving language skills, and other benefits of using ChatGPT, including teachers' concerns regarding the authenticity of AI-generated content and the potential impact on language learning outcomes. The findings of this research contribute to the existing literature on the integration of AI technology in ELT and offer practical recommendations for educators seeking to leverage the potential of ChatGPT in language teaching. By giving voice to teachers' perspectives, this study underscores the importance of considering teachers' perceptions and experiences when incorporating emerging technologies like ChatGPT into the ELT classroom. Ultimately, this research aims to foster a deeper understanding of the complex dynamics between AI technology and language teaching, paving the way for informed decision-making and effective implementation in the field of ELT.

A research of the impact of ChatGPT on education

The integration of AI language models, particularly ChatGPT, into higher education has sparked concerns about academic integrity and its impact on student learning experiences. This research aims to explore the applications, benefits, challenges, and future implications of ChatGPT in education, striving to achieve a balanced and beneficial integration of AI in higher education. The study reviews related works on ChatGPT, investigating its development, capabilities, and potential applications in education. Firstly, the paper emphasizes that ChatGPT changes teaching methods, enabling teachers to adopt more flexible and interactive approaches to education. Secondly, the paper highlights that ChatGPT can provide personalized learning experiences for students by generating customized teaching content based on their needs and offering real-time assistance and guidance, thereby enhancing learning effectiveness. However, the paper also acknowledges some potential challenges and issues, including concerns regarding plagiarism and privacy, as well as the possibility of biases and the generation of erroneous information. Addressing these issues requires technological improvements and the development of sound usage policies. The paper concludes by summarizing the findings and prospects of the research.

Parrots and Paragrams: AI Language Models and Erasure Poetry

Parrots and Paragrams: AI Language Models and Erasure Poetry

Dual use concerns of generative AI and large language models

ABSTRACT We suggest the implementation of the Dual Use Research of Concern (DURC) framework, originally designed for life sciences, to the domain of generative AI, with a specific focus on Large Language Models (LLMs). With its demonstrated advantages and drawbacks in biological research, we believe the DURC criteria can be effectively redefined for LLMs, potentially contributing to improved AI governance. Acknowledging the balance that must be struck when employing the DURC framework, we highlight its crucial political role in enhancing societal awareness of the impact of generative AI. As a final point, we offer a series of specific recommendations for applying the DURC approach to LLM research.