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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. [ABSTRACT FROM AUTHOR]

This paper explores the transformative potential of Large Language Models Artificial Intelligence (LLM AI) in educational contexts, particularly focusing on the innovative practice of prompt engineering. Prompt engineering, characterized by three essential components of content knowledge, critical thinking, and iterative design, emerges as a key mechanism to access the transformative capabilities of LLM AI in the learning process. This paper charts the evolving trajectory of LLM AI as a tool poised to reshape educational practices and assumptions. In particular, this paper breaks down the potential of prompt engineering practices to enhance learning by fostering personalized, engaging, and equitable educational experiences. The paper underscores how the natural language capabilities of LLM AI tools can help students and educators transition from passive recipients to active co-creators of their learning experiences. Critical thinking skills, particularly information literacy, media literacy, and digital citizenship, are identified as crucial for using LLM AI tools effectively and responsibly. Looking forward, the paper advocates for continued research to validate the benefits of prompt engineering practices across diverse learning contexts while simultaneously promoting potential defects, biases, and ethical concerns related to LLM AI use in education. It calls upon practitioners to explore and train educational stakeholders in best practices around prompt engineering for LLM AI, fostering progress towards a more engaging and equitable educational future. [ABSTRACT FROM AUTHOR]

Introduction: Chatbot generative pre-trained transformer (ChatGPT) is a new artificial intelligence-powered language model of chatbot able to help otolaryngologists in practice and research. We investigated the accuracy of ChatGPT-3.5 and -4 in the referencing of manuscripts published in otolaryngology. Methods: ChatGPT-3.5 and ChatGPT-4 were interrogated for providing references of the top-30 most cited papers in otolaryngology in the past 40 years including clinical guidelines and key studies that changed the practice. The responses were regenerated three times to assess the accuracy and stability of ChatGPT. ChatGPT-3.5 and ChatGPT-4 were compared for accuracy of reference and potential mistakes. Results: The accuracy of ChatGPT-3.5 and ChatGPT-4.0 ranged from 47% to 60%, and 73% to 87%, respectively (p < 0.005). ChatGPT-3.5 provided 19 inaccurate references and invented 2 references throughout the regenerated questions. ChatGPT-4.0 provided 13 inaccurate references, while it proposed only one invented reference. The stability of responses throughout regenerated answers was mild (k = 0.238) and moderate (k = 0.408) for ChatGPT-3.5 and 4.0, respectively. Conclusions: ChatGPT-4.0 reported higher accuracy than the free-access version (3.5). False references were detected in both 3.5 and 4.0 versions. Practitioners need to be careful regarding the use of ChatGPT in the reach of some key reference when writing a report. [ABSTRACT FROM AUTHOR]