From fragments to digital wholeness: An AI generative approach to reconstructing archaeological vessels

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

BackgroundThe current research on the application of generative artificial intelligence in has shown rapid growth, and most of the existing research focuses on general nursing scenarios. And is still in the exploratory stage in the specialty field and lacks a systematic compendium of its application in the field of cardiovascular specialty nursing.ObjectiveA review of studies related to the application of generative artificial intelligence in the field of specialized cardiovascular nursing informing relevant research and practice.MethodsThis review was conducted according to the JBI guidelines for scoping reviews, using the PRISMA-ScR reporting tool.The databases searched included PubMed, Embase, Web of Science, APA PsycNetInfo (Ovid), the Cochrane Library, MEDLINE (Ovid), CINAHL, SinoMed, CNKI, WanFang databases, peer-reviewed studies written in English and Chinese from the time of database construction until March 29, 2025. Eligible studies were screened by title and abstract, and full-text screening was performed by two independent evaluators.ResultsNineteen studies were included in this review, and the main application tools were 9 text generation models, 3 multimodal generation models, 4 temporal prediction models, and 3 chatbots. The application scenarios mainly include clinical decision support, patient health management, and nursing education and counseling. Generative artificial intelligence has outstanding effects in reducing nursing workload and precise intervention, but there are limitations in the field of health education and counseling, such as logical disconnection, poor information quality, and lack of humanistic care.ConclusionsGenerative artificial intelligence provides technical support for the intelligent transformation of cardiovascular specialty care, especially effective in the areas of clinical decision support and patient health management. There is an urgent need to solve the existing problems to promote its in-depth application in this field, and it is suggested that future research focuses on the construction of specialized multimodal models.

This study explores the application of artificial intelligence (AI) in the fashion industry, focusing on its benefits to the design process. AI is defined as the ability of a computer to mimic human intelligence, including data collection, analysis, and response using algorithms and data processing. By incorporating AI, the design process becomes more efficient, allowing fashion designers to create more innovative and complex designs. The study employs three stages of the art-making process: (1) creation, (2) design, and (3) exploration as its main method. As part of the research, a collection inspired by the traditional folk song “Bangau Oh Bangau” compares manual and computer-assisted approaches. The findings of this comparison show that using generative AI in the fashion design process speeds up the overall process, making digital approaches more effective and efficient. In addition to accelerating the process, generative AI also expands the scope of creativity by allowing designers to explore various new concepts that may be difficult to achieve through manual methods. This not only enhances the creative diversity in fashion collections but also helps the fashion industry continue to grow by introducing fresh and exciting designs to the market. In conclusion, the application of artificial intelligence in fashion design offers numerous advantages, including increased productivity and creativity. This technology gives the fashion industry powerful tools to create designs that meet market demands and push traditional creativity's boundaries.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

The rise of Generative Artificial Intelligence (GenAI) technologies and applications, such as ChatGPT, has prompted significant changes to the landscape of higher education. Amidst the excitement and debates about the potential of GenAI in education, it is often too easy to ignore the perspectives and attitudes of students and teachers towards GenAI and their views on how technology should be integrated into the learning environment. This study fills a gap in the literature by conducting a survey of Chinese university students and teachers to explore and compare their use patterns and attitudes toward GenAI. Intriguingly, the results revealed that despite the differences in use patterns, both teachers and students exhibited similar positive attitudes toward the utility and positive impact of GenAI. However, students seemed to be more concerned about the use of GenAI tools than teachers. The study also examined if language teachers and non-language teachers have different use patterns and attitudes toward GenAI. The study highlights the significance of considering both teacher and student perspectives in policy-making and curriculum design related to GenAI, as well as the need to understand the nuanced attitudes of teachers from different disciplines to provide targeted support.

Universities worldwide are rapidly advancing artificial intelligence (AI) to reconcile the perennial tension between educational quality, scalability, and personalization.This paper analyzes 13 studies and case analyses, focusing on AI applications in higher education: (1) adaptive learning systems, (2) real-time instructional tools, and (3) automated assessment feedback. These technologies improve course completion, reduce instructor preparation time, and enhance student writing.Nonetheless, they also introduce fresh risks related to academic integrity, data privacy, shifting teacherstudent roles, and equity in resource distribution. This paper proposes a two-tier, triadic governance framework to address the intertwined challenges of educational ethics and quality, user adaptation, and the digital divide posed by generative AI. Universities should adopt honor codes, AI evaluation guidelines, secure data sandboxes, and AI-literacy curricula. Governments and industry must provide regulatory standards, resources, and multilingual large-language-model infrastructure. AI can significantly drive equity and excellence in higher education when internal self-regulation aligns with external policies.

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

The burgeoning integration of Artificial Intelligence (AI), particularly its generative artificial intelligence (GenAI) and geospatial artificial intelligence (GeoAI) applications, is fundamentally transforming Geographic Information Systems (GIS) and, consequently, the professional landscape of map and geospatial libraries. While these technologies offer unprecedented capabilities for data analysis, visualization, and content creation, their widespread adoption introduces a complex array of social and ethical challenges that necessitate critical examination by library professionals. Aligning with the broader feminist critique of positivist science practices that typically dominate GIS methodologies and using feminist theory, this paper critically reflects on the social and ethical implications of GeoAI and GenAI within map and geospatial library contexts. It draws upon a review of existing literature in feminist geography, critical cartography, and AI ethics, alongside insights derived from professional practice. A central tenet of this reflection is the recognition that, much like the historically contested fields of cartography and geography, GeoAI and GenAI is a feminist issue. This critical lens reveals that both cartography/geography and GeoAI/GenAI present similar opportunities for embedding and perpetuating societal biases and systems of domination within spatial representations. This can be seen through the ways in which GeoAI/GenAI perpetuate hierarchies, restricting access and agency based on race, class, gender, and sexuality. Through critical reflection, this paper aims to equip map and geospatial library professionals with the theoretical and pedagogical exploration of feminist geospatial ethics. It seeks to foster a deeper understanding of librarians’ social and ethical obligations in an increasingly AI-driven geospatial information environment, thereby empowering them to navigate these complexities responsibly and ensure the realization of GeoAI and GenAI’s relationship with systems of oppression.

The article is devoted to the application of generative artificial intelligence (AI) in the study and teaching of foreign languages. In recent years, AI has been actively introduced into the educational process, offering personalized approaches, automated knowledge assessment and interactive teaching methods. The authors analyze existing research and identify the main areas of use of generative AI: chatbots, dialog systems, platforms for teaching writing and adaptive educational technologies. In conclusion, practical recommendations are formulated for teachers, researchers and educational organizations on the integration of generative AI into language education. The work highlights the importance of further research and collaboration between scientists, educators, and technology developers for the effective use of AI in the educational process. As a problem, especially important for the national education system, the task of forming ethical principles is outlined, indicating the limits of what is acceptable in the use of generative AI by teachers and students. Methodically, the article is carried out in the genre of a systematic review. The criteria for selecting sources included the year of publication (from 2017 to 2024) and the type of publication (theoretical articles, empirical research, review articles, technology reviews, editorial opinions, and discussion articles). Although there has been a significant body of published research and literature reviews on artificial intelligence (AI) in recent years, the existing literature specifically devoted to the application of generative AI in language education, as well as generative AI in general, is still very limited. Based on the review, the article makes a number of recommendations. First, educators should consider including generative AI tools in their teaching practice, while remaining vigilant and attentive to potential risks. Continuous professional development is crucial to ensure informed decisions and effective integration of generative AI tools. Secondly, researchers need to continue empirical research on the effectiveness and impact of generative AI tools, ethical considerations, educational interventions to develop specific language skills and involve stakeholders in responsible integration. Finally, society as a whole should take into account that the impact of generative AI goes beyond language education, and it is important to consider its integration into other subject areas.

With the continuous development of science and technology, exploration in the field of life sciences has become increasingly profound, encompassing the understanding of human beings themselves and the exploration of disease etiology and treatment methods.Artificial intelligence is defined as the intelligence exhibited by artificially created systems. The application of artificial intelligence is quite extensive and can replace humans in various aspects of work, significantly enhancing efficiency in daily life and work. Artificial intelligence has a dual nature, as it could provide convenience to human society but could also bring about various adverse effects.This paper summarizes the current applications of artificial intelligence in the field of life sciences. By analyzing the applications of artificial intelligence in areas such as gene editing, drug development, and bioinformatics, it demonstrates the potential of artificial intelligence in the field of life sciences. At the same time, this paper also points out the ethical, technical, social, and various challenges that the application of artificial intelligence technology in the field of life sciences faces, with the aim of providing reference for research in related fields.

The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

Publications on the application of artificial intelligence (AI) to many situations, including those in clinical medicine, created in 2023-2024 are reviewed here. Because of the short time frame covered, here, it is not possible to conduct exhaustive analysis as would be the case in meta-analyses or systematic reviews. Consequently, this literature review presents an examination of narrative AI's application in relation to contemporary topics related to clinical medicine. The landscape of the findings reviewed here span 254 papers published in 2024 topically reporting on AI in medicine, of which 83 articles are considered in the present review because they contain evidence-based findings. In particular, the types of cases considered deal with AI accuracy in initial differential diagnoses, cancer treatment recommendations, board-style exams, and performance in various clinical tasks, including clinical imaging. Importantly, summaries of the validation techniques used to evaluate AI findings are presented. This review focuses on AIs that have a clinical relevancy evidenced by application and evaluation in clinical publications. This relevancy speaks to both what has been promised and what has been delivered by various AI systems. Readers will be able to understand when generative AI may be expressing views without having the necessary information (ultracrepidarianism) or is responding as if the generative AI had expert knowledge when it does not. A lack of awareness that AIs may deliver inadequate or confabulated information can result in incorrect medical decisions and inappropriate clinical applications (Dunning-Kruger effect). As a result, in certain cases, a generative AI system might underperform and provide results which greatly overestimate any medical or clinical validity.

This paper presents a scoping review on the application of generative artificial intelligence (GenAI) in educational research and proposes a living GenAI governance model to ensure a critical, responsible, and ethical usage of GenAI at macro, meso, and micro levels. Employing a quantitative and qualitative integration methodology, this review utilized two software tools: VOSviewer provided an initial overview of the field, while Bibliometrix revealed its conceptual structure. Through a two-dimensional map, subthemes were categorized into four quadrants: motor themes, basic themes, emerging or disappearing themes, and niche themes. Following the bibliometric analysis, we conducted a more detailed examination through content analysis by reviewing the titles and abstracts of 194 publications, selecting 35 pertinent articles. Based on these findings, we propose a Living GenAI Governance Model that maintains an ethical foundation and a dynamic perspective for using GenAI in educational research. The study’s limitations serve as starting points for future research, as this review utilized only one database (WoS). Future studies should expand on the use of databases and include updated references, given the rapid theoretical production and practical application in this field. The target audience for this article is diverse and spans multiple levels, including policymakers, academic authorities, university managers, students, teachers, and researchers. The primary contribution of this work lies in its comprehensive and structured vision of the model, which facilitates the study of inter-level relationships and the dynamic mapping of its various components.