American Students are increasingly using generative AI to create inappropriate images and videos–known as deepfake non-consensual intimate images (NCII)–of young girls in the K-12 school system (Walker, 2025). To date, schools do not have clear guidelines on how to handle this rising crisis. Federal policy attention, such as the Take It Down Act, prioritizes removing the harmful content after it is put online, as opposed to taking steps to prevent deepfake NCII from being created. It is time to readjust how generative AI content is regulated. This brief examines different international approaches to mitigating the risk of AI violence against women, such as the AI Act and Digital Services Act (DSA), to propose preventative measures within U.S. policy. Preventing deepfake NCII from circulating schools would include the following: mandating that the NIST AI Risk Management Framework be adopted by all generative AI companies operating within the United States; defining AI deepfake NCII as a form of abuse; establishing rules stating what minors can access on the internet as well as creating safe, protected spaces for them to responsibly use the internet; implementing mandated free training for school teachers and administrations across the country about what constitutes proper AI usage and how to prevent AI-based violence; designing lessons for school-age children about the harm of deepfake NCII; expanding counseling services within public schools to assist children who are affected by deepfake NCII.
Keira Giacometti· Zenodo (CERN European Organi...· 0 citations
American Students are increasingly using generative AI to create inappropriate images and videos–known as deepfake non-consensual intimate images (NCII)–of young girls in the K-12 school system (Walker, 2025). To date, schools do not have clear guidelines on how to handle this rising crisis. Federal policy attention, such as the Take It Down Act, prioritizes removing the harmful content after it is put online, as opposed to taking steps to prevent deepfake NCII from being created. It is time to readjust how generative AI content is regulated. This brief examines different international approaches to mitigating the risk of AI violence against women, such as the AI Act and Digital Services Act (DSA), to propose preventative measures within U.S. policy. Preventing deepfake NCII from circulating schools would include the following: mandating that the NIST AI Risk Management Framework be adopted by all generative AI companies operating within the United States; defining AI deepfake NCII as a form of abuse; establishing rules stating what minors can access on the internet as well as creating safe, protected spaces for them to responsibly use the internet; implementing mandated free training for school teachers and administrations across the country about what constitutes proper AI usage and how to prevent AI-based violence; designing lessons for school-age children about the harm of deepfake NCII; expanding counseling services within public schools to assist children who are affected by deepfake NCII.
Keira Giacometti· Zenodo (CERN European Organi...· 0 citations
Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decision-making, including regulatory compliance. This paper introduces an AI-powered interactive visual system—AISyst—designed to assess the fidelity of synthetic tabular datasets. The system supports multilevel comparisons with real datasets, spanning multivariate resemblance analyses based on dimensionality reduction through suitable two-dimensional projections, bivariate correlation and univariate similarity. AISyst also integrates an AI assistant by leveraging state-of-the-art large language models to summarize key findings and generate suggestions for improving synthetic data generation models. We validated the capabilities of AISyst through three case studies, supported by feedback from industrial AI experts who endorsed its broader deployment.
L. Liu, L. Bogachev, N. Onyiaji et al.· White Rose Research Online (...· 0 citations
Anatomy education serves as the cornerstone of medical training across both modern and traditional medicine systems. However, traditional pedagogical approaches—relying heavily on cadaveric dissection and two-dimensional illustrations—face mounting challenges, including cadaver shortages, ethical concerns, and the need to engage technology-oriented Generation Z learners. Artificial Intelligence (AI) has emerged as a transformative force in anatomical education, offering virtual dissection simulations, adaptive learning platforms, intelligent tutoring systems, and generative AI-powered chatbots. In modern medical education, AI tools such as ChatGPT, Anatbuddy, and VR/AR-based platforms have demonstrated significant potential in personalizing learning, generating assessment materials, and enhancing student engagement. Concurrently, in Ayurvedic anatomy education (Sharira Rachana), AI-powered tools like CADAVIZ and AyurSIM are bridging traditional knowledge with contemporary technological methods, enabling three-dimensional visualization of anatomical concepts and addressing resource disparities. This review consolidates existing evidence on AI applications in both streams of anatomy education, evaluates their effectiveness, and identifies key challenges including content accuracy concerns, over-reliance on technology, ethical considerations, and the need for customized knowledge bases. The findings underscore that AI should augment rather than replace traditional teaching methods, with a balanced, ethically guided approach being essential for effective integration. Future directions include developing specialized AI tools for traditional medicine anatomy, integrating real clinical cases, and establishing systematic AI literacy programmes for both educators and students.
Dr. Sanjiv Sexena2 Dr. Archana Gautam1*· Zenodo (CERN European Organi...· 0 citations
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Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future gatekeepers of safe pharmaceutical use, trainees in sciences connected to medicine, and consumers of health information, pharmacy students hold a special place in this conversation.[2,3,20,33,56] In order to describe what is known about pharmacy (and allied health) students' knowledge of AI, their attitudes toward its use in clinical and self-care contexts, and their actual practices—including the use of AI for drug information, dosing guidance, and self-diagnosis-adjacent tasks—this review synthesizes nine cross-sectional Knowledge, Attitude, and Practice (KAP) studies conducted between 2022 and 2026 across Zambia.[3] Saudi Arabia.[2,6] India.[1,4,8] Syria.[9] Malaysia.[5] and other settings. AI awareness is almost universal (82–100%) in all contexts.[2,3,5,6,8,9] but conceptual understanding of AI subtypes, medical uses, and limitations is generally lower (30–60%).[2,3,5,6,8,9] Concerns concerning data privacy, false information, over-reliance, and professional displacement temper the generally positive attitudes.[1,2,6,7,8] Practice is still limited: formal curricular training is uncommon (7–46%).[2,3,6,7,9] and the majority of self-reported AI use focuses on academic activities (summarizing, presentations, exam preparation) rather than verified clinical decision-support.[3,4,5,7] There is a dearth of direct data on AI-assisted self-diagnosis and self-medication, particularly among pharmacy students. In order to outline the emerging risk landscape and suggest curricular and regulatory responses, this review draws conclusions from related findings, such as the use of ChatGPT for "getting drug information".[2] perceived AI usefulness in "medication management".[1,2] and expressed willingness to trust AI-generated dosing recommendations.[2]
*1Dr. Jegathis Kumar M., 2Karthikeyan S., 3Magesh Kumar· Zenodo (CERN European Organi...· 0 citations
Anatomy education serves as the cornerstone of medical training across both modern and traditional medicine systems. However, traditional pedagogical approaches—relying heavily on cadaveric dissection and two-dimensional illustrations—face mounting challenges, including cadaver shortages, ethical concerns, and the need to engage technology-oriented Generation Z learners. Artificial Intelligence (AI) has emerged as a transformative force in anatomical education, offering virtual dissection simulations, adaptive learning platforms, intelligent tutoring systems, and generative AI-powered chatbots. In modern medical education, AI tools such as ChatGPT, Anatbuddy, and VR/AR-based platforms have demonstrated significant potential in personalizing learning, generating assessment materials, and enhancing student engagement. Concurrently, in Ayurvedic anatomy education (Sharira Rachana), AI-powered tools like CADAVIZ and AyurSIM are bridging traditional knowledge with contemporary technological methods, enabling three-dimensional visualization of anatomical concepts and addressing resource disparities. This review consolidates existing evidence on AI applications in both streams of anatomy education, evaluates their effectiveness, and identifies key challenges including content accuracy concerns, over-reliance on technology, ethical considerations, and the need for customized knowledge bases. The findings underscore that AI should augment rather than replace traditional teaching methods, with a balanced, ethically guided approach being essential for effective integration. Future directions include developing specialized AI tools for traditional medicine anatomy, integrating real clinical cases, and establishing systematic AI literacy programmes for both educators and students.
Dr. Sanjiv Sexena2 Dr. Archana Gautam1*· Zenodo (CERN European Organi...· 0 citations
Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future gatekeepers of safe pharmaceutical use, trainees in sciences connected to medicine, and consumers of health information, pharmacy students hold a special place in this conversation.[2,3,20,33,56] In order to describe what is known about pharmacy (and allied health) students' knowledge of AI, their attitudes toward its use in clinical and self-care contexts, and their actual practices—including the use of AI for drug information, dosing guidance, and self-diagnosis-adjacent tasks—this review synthesizes nine cross-sectional Knowledge, Attitude, and Practice (KAP) studies conducted between 2022 and 2026 across Zambia.[3] Saudi Arabia.[2,6] India.[1,4,8] Syria.[9] Malaysia.[5] and other settings. AI awareness is almost universal (82–100%) in all contexts.[2,3,5,6,8,9] but conceptual understanding of AI subtypes, medical uses, and limitations is generally lower (30–60%).[2,3,5,6,8,9] Concerns concerning data privacy, false information, over-reliance, and professional displacement temper the generally positive attitudes.[1,2,6,7,8] Practice is still limited: formal curricular training is uncommon (7–46%).[2,3,6,7,9] and the majority of self-reported AI use focuses on academic activities (summarizing, presentations, exam preparation) rather than verified clinical decision-support.[3,4,5,7] There is a dearth of direct data on AI-assisted self-diagnosis and self-medication, particularly among pharmacy students. In order to outline the emerging risk landscape and suggest curricular and regulatory responses, this review draws conclusions from related findings, such as the use of ChatGPT for "getting drug information".[2] perceived AI usefulness in "medication management".[1,2] and expressed willingness to trust AI-generated dosing recommendations.[2]
*1Dr. Jegathis Kumar M., 2Karthikeyan S., 3Magesh Kumar· Zenodo (CERN European Organi...· 0 citations
AI Explained: A Guide for Non-Technical Readers builds understanding of artificial intelligence from first principles rather than diving in at the top. Written by experienced policy and technical experts, the book walks through rules and logic-based approaches, statistical methods, neural networks, machine learning, and generative models in accessible, structured terms. Each major section concludes with a dedicated use-cases chapter grounding abstract concepts in practical scenarios drawn from healthcare, law, and business. Rather than teaching readers how to build or deploy AI, the book answers a more fundamental question: how do these systems achieve the outcomes they produce? Coverage of AI policy, ethics, and societal impact rounds out the treatment, informed directly by the authors' advisory roles with governments and international bodies.
Generative artificial intelligence (Gen AI) has taken the academic world by storm. This G.I.F.T.S. paper argues that personalizing academic assignments may curb students’ tendency to copy and paste content from Gen AI outputs. I demonstrate how I personalized a written assignment in one of my courses. A total of 81 paper grades were analyzed, and no significant difference was found between students who engaged in personalizing their learning and those who did not. Final course grades showed the same result. Although no statistically significant difference was detected, personalizing assignments remains a valuable way to engage students in the Gen AI era.
Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· Conference on Technology Eth...· 27 citations· ⚡3
Solutions in artificial intelligence (AI) are becoming increasingly widespread in system development endeavors. As the AI systems affect various stakeholders due to their unique nature, the growing influence of these systems calls for eth-ical considerations. Academic discussion and practical examples of autonomous system failures have highlighted the need for implementing ethics in software development. However, research on methods and tools for implementing ethics into AI system design and development in practice is still lacking. This paper be-gins to address this focal problem by providing a baseline for ethics in AI based software development. This is achieved by reporting results from an industrial multiple case study on AI systems development in the health care sector. In the context of this study, ethics were perceived as interplay of transparency, re-sponsibility and accountability, upon which research model is outlined. Through these cases, we explore the current state of practice out on the field in the ab-sence of formal methods and tools for ethically aligned design. Based on our data, we discuss the current state of practice and outline existing good practic-es, as well as suggest future research directions in the area.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· arXiv.org· 2 citations
Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an “unknown unknown” problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.
Anh Nguyen-Duc, P. Abrahamsson· ESEC/SIGSOFT FSE· 9 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
