Role Of AI Research Articles

Modernizando a Redação Policial: Protótipo de Aplicativo Android para a Elaboração de Relatos de Ocorrências

This article presents the development of an Android application prototype designed to assist Military Police officers with writing incident reports. The application leverages Google's Gemini AI API to provide real-time grammar correction and intelligent suggestions for writing improvement, acting as an experienced officer reviewing and enhancing the text. The app's architecture, based on the MVVM (Model-View-ViewModel) pattern, and the user interface design, focused on simplicity and usability, are detailed. The implementation of the Gemini AI API, including the system prompt that defines the AI's role and behavior, is explored, along with cost analysis and comparison with other popular natural language processing APIs like OpenAI and Anthropic. Test results using real incident reports demonstrate the app's ability to improve text clarity, conciseness, and professionalism, as well as provide relevant suggestions for police practice, such as missing information, legal procedures, and evidence collection. The article discusses the impacts and benefits of the application for the PMPR, the limitations of the technology, and suggestions for future work, including integration with other platforms and expansion of AI functionalities.

The Role of AI in Personalizing Insurance Policies

This article analyzes the transformative impact of artificial intelligence on the insurance industry through four key innovations. It examines how AI has enabled real-time risk assessment and dynamic pricing through personalized policy optimization, integrated terabytes of daily customer data for precise underwriting, implemented advanced analytics frameworks for enhanced fraud detection and customer segmentation, and automated operations for improved efficiency. The findings demonstrate that AI implementation has achieved an 82% improvement in risk prediction accuracy, reduced operational costs by 67%, and increased customer satisfaction by 76%. However, these technological advancements necessitate robust ethical governance frameworks, particularly in data privacy and algorithmic fairness. The research comprehensively analyzes AI's role in reshaping insurance operations while emphasizing the critical balance between technological innovation and ethical implementation. These insights offer valuable guidance for insurance providers pursuing digital transformation while maintaining regulatory compliance and customer trust.

From Backlogs to Bots: Generative AI's Impact on Agile Role Evolution

ABSTRACTThis position paper investigates the transformative impact of generative artificial intelligence (GenAI) on Agile roles. Focusing on the product owner, developer, and scrum master, we analyze how GenAI redefines traditional tasks, encouraging a shift towards more strategic and creative functions. Through practical experience, we illustrate AI's role in enhancing Agile processes, its practices and emphasize the need for Agile practitioners to integrate AI understanding. These results highlight the balance between GenAI's efficiencies and Agile's human‐centric principles, proposing research directions for AI‐enriched Agile practices that promise to drive innovation and maintain the agility in a technologically progressive era.

The Synergistic Impact of Human-AI Collaboration: A Multi-Domain Analysis

This comprehensive article explores the transformative impact of human-AI collaboration across healthcare, manufacturing, and scientific research. The article examines how the synergistic integration of artificial intelligence with human expertise is revolutionizing key industries, enhancing efficiency, accuracy, and innovation. In healthcare, we discuss the implementation and impact of AI-assisted diagnostics and AI-augmented surgery, highlighting improvements in diagnostic accuracy and surgical outcomes. The manufacturing sector is analyzed through the lens of AI-driven quality control systems and the introduction of collaborative robots (cobots), demonstrating significant gains in productivity and worker safety. In scientific research, we explore AI's role in data-driven research and hypothesis testing, particularly in areas such as drug discovery and genomics. The article also provides a critical analysis of the complementary strengths of humans and AI, addresses ethical considerations in AI implementation, and speculates on the future potential of this collaboration. Drawing on recent research and case studies, this article presents a holistic view of how human-AI synergy is driving progress across multiple domains, while also considering the challenges and ethical implications of this technological integration. The findings suggest that the future of human-AI collaboration holds immense potential for accelerating scientific discovery, improving healthcare outcomes, and revolutionizing manufacturing processes, ultimately benefiting society as a whole.

Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience

AbstractArtificial Intelligence (AI) is pivotal in advancing science, including nanomaterial studies. This review explores AI‐based image processing in nanoscience, focusing on algorithms to enhance characterization results from instruments like scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, atomic force microscopy etc. It addresses the significance of AI in nanoscience, challenges in advancing AI‐based image processing for nano material characterization, and AI's role in structural analysis, property prediction, deriving structure‐property relations, dataset augmentation, and improving model robustness. Key AI techniques such as Graph Neural Networks, adversarial training, transfer learning, generative models, attention mechanisms, and federated learning are highlighted for their contributions to nano science studies. The review concludes by outlining persisting challenges and thrust areas for future research, aiming to propel nanoscience with AI. This comprehensive analysis underscores the importance of AI‐powered image processing in nanomaterial characterization, offering valuable insights for researchers.

Integrating artificial intelligence into team decision‐making: Toward a theory of AI–human team effectiveness

AbstractArtificial intelligence (AI), a technological advancement radically affecting the workplace of the future, offers benefits for decision‐making processes. The most critical organizational decisions typically occur through teams. However, team theories are fundamentally psychosocial, and AI disrupts this context by introducing a non‐human actor. Therefore, negative implications of AI's role must also be understood. Toward theory on the effective integration of AI into team decision‐making, we synthesize the literatures on team effectiveness and team decision‐making with research from cognate disciplines on human–technology interaction and teaming. Based on this synthesis, we offer propositions highlighting key variable relationships and negative side effects that must be accounted for in AI–human team decision‐making and follow with practical suggestions for management's adaption to this new context. Overall, our analysis emphasizes critical themes, constructs, and relationships valuable for further research aimed at modernizing theory and practice in the face of this emerging technological shift.

Health Librarians' Perceptions and Utilization of ChatGPT for Digital Literacy in Libraries

Libraries are adapting to the digital age, with health librarians utilizing AI technologies like ChatGPT to support patrons' digital literacy (Chen, 2023). Although prior research has explored AI's role in libraries, little is known about how health librarians leverage ChatGPT, especially regarding health information seeking behavior. This study investigates health librarians' experiences and perspectives on integrating ChatGPT into digital literacy services. Through semi-structured interviews, thematic analysis reveals innovative applications of ChatGPT, such as real-time Q&A sessions and personalized learning experiences. Despite technical challenges like accuracy and reliability, librarians implement quality control measures and continuously update ChatGPT's knowledge base. The study revealed ChatGPT's positive impact on patrons, encouraging improved understanding of health information and confidence in digital resource navigation. Despite challenges, health librarians acknowledge ChatGPT's potential to enhance digital literacy services. Recommendations include addressing technical issues, providing librarian training, and improving user engagement strategies (Yamson, 2023). Overall, this research shows ChatGPT's role in advancing digital literacy within libraries, providing perspectives into its effective integration and potential improvements.

Strengthening Healthcare Data Security with Ai-Powered Threat Detection

As the healthcare industry undergoes rapid digital transformation, the need for robust cybersecurity measures has never been more critical. AI-driven solutions, such as Machine Learning (ML) and anomaly detection, are proving to be pivotal in securing healthcare data. These technologies enable healthcare organizations to identify cyber threats proactively, automate incident response, and enhance data security. Furthermore, AI's ability to provide continuous network monitoring, predictive analytics, and real-time anomaly detection offers healthcare providers the tools needed to mitigate risks before they escalate. This research highlights the key applications of AI in healthcare data security, including its effectiveness in vulnerability management and regulatory compliance. It also delves into the ethical and operational challenges of integrating AI-driven threat detection systems into healthcare settings, including concerns related to bias in AI models, regulatory hurdles, and the complexity of AI system integration into existing healthcare infrastructures. In a 2023 study, Accenture reported that AI-based cybersecurity systems reduced detection and response time by up to 60%, illustrating how AI accelerates response to potential data breaches​. Similarly, HIMSS noted that the risk of data breaches in healthcare could be halved by AI technologies that continuously monitor and analyze data​. These findings emphasize AI's role in minimizing the delays and errors typically associated with human-driven responses​. AI techniques such as supervised, unsupervised, and semi-supervised learning play a crucial role in anomaly detection. These models identify irregular patterns in healthcare systems, flagging potential threats even when subtle​. For example, deep learning approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can uncover outliers in healthcare data, enhancing system security​. The ability of AI to continuously learn from previous attacks ensures that systems evolve with emerging threats, further solidifying its place in healthcare cybersecurity​. Additionally, AI aids in predictive analytics, which can forecast future cyber threats based on historical data, allowing healthcare providers to address vulnerabilities proactively​. By leveraging such capabilities, AI not only safeguards sensitive patient data but also enables healthcare institutions to stay compliant with stringent regulatory standards like HIPAA and GDPR​. However, AI implementation in healthcare security comes with its own challenges. Integrating AI into legacy systems requires substantial infrastructure upgrades, and the complexity of dynamic encryption can strain system resources​. Furthermore, the ethical concerns surrounding AI, such as the transparency of decision-making processes and potential bias in AI algorithms, must be carefully managed to ensure equitable and effective security solutions​

Generative AI in Student Essays: English Teachers' Perspectives on Effective Assessment Methods

The increasing use of AI-assisted writing tools in education presents new challenges in ensuring the integrity and originality of student essays. This study investigates English teachers’ perceptions of various essay assessment methods in light of these challenges. Through a mixed-methods approach, combining quantitative Likert-scale data and qualitative open-ended responses, the research reflects the perspectives of 50 experienced educators from the Faculty of Linguistics at the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute.” The study investigates teachers' familiarity with AI tools, the challenges they face in assessing AI-assisted student work, and their views on AI's role in aiding or potentially hindering student writing skills. Findings indicate that while many educators support AI for tasks like grammar and proofreading, concerns persist over its impact on students' critical thinking and originality. Assessment methods currently in use include in-class handwritten essays, oral defenses, AI-detection software, and portfolios. The results indicate that hybrid assessment methods are rated as the most effective for evaluating AI-influenced essays, indicating a preference for combining traditional and AI-specific techniques. Implications and recommendations for research and practice have been outlined, emphasizing a balanced approach that promotes ethical AI usage, critical thinking, and academic integrity in writing assessments.

Use of AI in India and International Education in Developmental Countries

Abstract: The use of AI in education, particularly in developing countries like India, is transforming how learning is accessed and delivered. By overcoming traditional barriers such as lack of infrastructure, qualified teachers, and resources, AI-driven solutions are offering innovative methods to enhance education. Below is a more detailed exploration of AI's role in India and other developing countries, along with references to highlight these trends.

Exploring the Efficacy of a ChatGPT-Based Application in Enhancing Oral English Proficiency for Chinese ESL Students

This research explores the impact of Chat GPT AI Characters on ESL learning, comparing them to traditional peer practice in language education. It investigates AI's influence on language proficiency, engagement, and response quality, drawing from literature on ESL challenges. Using mixed methods—pre- and post-test questionnaires, classroom experiments—the study evaluates two groups' language fluency, response speed, and answer quality. Thematic analysis uncovers student perspectives, while statistical methods like t-tests and correlation studies highlight group differences. Ethical considerations prioritize privacy and consent. Anticipated outcomes include improved language skills and engagement. Acknowledging limitations in sample size and cultural biases, this study illuminates AI's role in ESL education, informing future teaching practices.

Enhancing or Hindering? AI's Role in Sparking Creativity in Language Teaching: Insights from Private High School EFL Teachers

The integration of artificial intelligence in education presents both opportunities and challenges, particularly in the field of language teaching. This study aims to investigate Turkish EFL teachers' perceptions of AI's role in fostering or hindering creativity in language teaching. Through semi-structured interviews with 10 EFL teachers from private high schools, the research explores the potential benefits and challenges of AI integration. Key findings reveal that AI enhances student engagement, provides personalized learning experiences, and offers timely feedback. However, concerns about insufficient training, technical issues, and over-reliance on AI potentially undermining fundamental skills were also expressed. While AI tools support improvements in language skills and foster creative thinking, there is apprehension about standardization and the risk of diminishing originality. The study underscores the necessity for effective teacher training and a balanced approach to AI integration, ensuring it complements rather than replaces traditional teaching methods. These insights provide valuable guidance for educators, policymakers, and technology developers to optimize AI use in EFL education while fostering a dynamic and creative learning environment. Future research should explore innovative ways to integrate AI into language teaching without compromising creativity.

Perceptions of ChatGPT in healthcare: usefulness, trust, and risk.

This study explores the perceptions of ChatGPT in healthcare settings in Taiwan, focusing on its usefulness, trust, and associated risks. As AI technologies like ChatGPT increasingly influence various sectors, their potential in public health education, promotion, medical education, and clinical practice is significant but not without challenges. The study aims to assess how individuals with and without healthcare-related education perceive and adopt ChatGPT, contributing to a deeper understanding of AI's role in enhancing public health outcomes. An online survey was conducted among 659 university and graduate students, all of whom had prior experience using ChatGPT. The survey measured perceptions of ChatGPT's ease of use, novelty, usefulness, trust, and risk, particularly within clinical practice, medical education, and research settings. Multiple linear regression models were used to analyze how these factors influence perception in healthcare applications, comparing responses between healthcare majors and non-healthcare majors. The study revealed that both healthcare and non-healthcare majors find ChatGPT more useful in medical education and research than in clinical practice. Regression analysis revealed that for healthcare majors, general trust is crucial for ChatGPT's adoption in clinical practice and influences its use in medical education and research. For non-healthcare majors, novelty, perceived general usefulness, and trust are key predictors. Interestingly, while healthcare majors were cautious about ease of use, fearing it might increase risk, non-healthcare majors associated increased complexity with greater trust. This study highlights the varying expectations between healthcare and non-healthcare majors regarding ChatGPT's role in healthcare. The findings suggest the need for AI applications to be tailored to address specific user needs, particularly in clinical practice, where trust and reliability are paramount. Additionally, the potential of AI tools like ChatGPT to contribute to public health education and promotion is significant, as these technologies can enhance health literacy and encourage behavior change. These insights can inform future healthcare practices and policies by guiding the thoughtful and effective integration of AI tools like ChatGPT, ensuring they complement clinical judgment, enhance educational outcomes, support research integrity, and ultimately contribute to improved public health outcomes.

Book Review: Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions by Beth Buyserie and Travis N. Thurston

"Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions," edited by Beth Buyserie and Travis N. Thurston, is a timely and essential addition to the ongoing dialogue about AI's role in education. This book thoroughly examines the opportunities and challenges that AI technologies bring to both traditional and online classroom environments. This comprehensive volume delves into the multifaceted impact of AI on educational practices, offering insights into how these technologies can enhance learning experiences and pedagogical strategies. The editors, Buyserie and Thurston, have curated a collection of essays and studies that highlight the transformative potential of AI in personalizing learning, automating administrative tasks, and providing real-time feedback to students.

Optimizing AI's Role in Advancing Interior Design Industry

Optimizing AI's Role in Advancing Interior Design Industry

Comparative Analysis of Sustainability, Carbon Footprint, and AI's Role in Reducing Emissions Across AWS, Azure, and Google Cloud

Comparative Analysis of Sustainability, Carbon Footprint, and AI's Role in Reducing Emissions Across AWS, Azure, and Google Cloud

AI assistance in aesthetic medicine-A consensus on objective medical standards.

Aesthetic medicine has traditionally relied on clinical scales for the objective assessment of baseline appearance and treatment outcomes. However, the scales focus on limited aesthetic areas mostly and subjective interpretation inherent in these scales can lead to variability, which undermines standardization efforts. The consensus meeting aimed to establish guidelines for AI application in aesthetic medicine. In February 2024, the AI Consensus Group, comprising international experts in various specialties, convened to deliberate on AI in aesthetic medicine. The methodology included a pre-consensus survey and an iterative consensus process during the meeting. AI's implementation in Aesthetic Medicine has achieved full consensus for enhancing patient assessment and consultation, ensuring standardized care. AI's role in preventing overcorrection is recognized, alongside the need for validated objective facial assessments. Emphasis is placed on comprehensive facial aesthetic evaluations using indices such as the Facial Aesthetic Index (FAI), Facial Youth Index (FYI), and Skin Quality Index (SQI). These evaluations are to be gender-specific and exclude makeup-covered skin at baseline. Age and gender, as well as patients' ancestral roots, are to be considered integral to the AI assessment process, underlining the move towards personalized, precise treatments. The consensus meeting established that AI will significantly improve aesthetic medicine by standardizing patient assessments and consultations, with a strong endorsement for preventing overcorrection and advocating for validated, objective facial assessments. Utilizing indices such as the FAI, FYI, and SQI allows for gender-specific, age adjusted evaluations and insists on a makeup-free baseline for accuracy.

The rise of artificial intelligence in vascular surgery: A bibliometric analysis (2020-2024)

Aim: This study aims to perform a comprehensive bibliometric analysis of academic publications on AI applications in vascular surgery, identifying key authors, influential journals, prevalent research themes, and international collaborations, focusing on infrastructure, conceptual structure, and social networks within the field. Material and Methods: The analysis covers 815 documents published from 2020 to 2024, retrieved from the Web of Science Core Collection database. Metrics analyzed include publication growth, citation rates, key contributors, leading journals, prevalent themes, and international collaborations. Results: The research output showed a 15% annual growth rate, peaking in 2023. Despite increasing publications, the average citation rate per article declined. The study identified 5039 contributors with significant international co-authorship. Leading authors included Lareyre F and Raffort J, and the "Journal of Vascular Surgery" was the most influential journal. The USA and China led in contributions, reflecting robust research infrastructure. Key themes include risk assessment, diagnostic methods, and patient management, highlighting AI's role in enhancing diagnostic accuracy, treatment planning, and patient outcomes in vascular surgery. Conclusion: The analysis highlights the rapid growth and collaborative nature of AI research in vascular surgery. Key contributors, influential journals, and emerging themes were identified, emphasizing AI's role in improving diagnostics and patient outcomes. Limitations include the focus on one database and a five-year period, suggesting future research should include more databases and a longer timeframe. Exploring high-impact studies and practical applications will further advance the field.

제품디자인에서 생성형 인공지능 활용을 통한 협업프로세스 활용 가능성 탐색

With the rapid advancement of generative AI, most industries are entering a new phase. Generative AI is being utilized across a wide range of fields, including design. Consequently, this study explored the application of AI in the product design field and integrated it into the design process. The goal was to identify the differences between traditional product design processes and those enhanced by AI. Through this research, the potential of AI in product design was confirmed, and future methods of its application were investigated. First, the study considered the domains of human and AI design separately. AI's role was seen as generating and displaying various images, while humans decided on keywords and concrete designs. Next, experimental results indicated that at crucial moments in the design process, the designer’s ability and judgment in selecting designs remained important. Additionally, the effective use of prompts through questions became crucial in applying generative AI. Language, as the culmination of human knowledge and the most fundamental means of expression, plays a critical role. Ultimately, the effectiveness and efficiency of AI usage will depend on the user's level of knowledge in the respective field or their ability to extract knowledge from AI learning. As AI technology advances, paradigms across all industries are shifting. This phenomenon will merge with traditional design methods, resulting in an evolved form of design approach.

Assessing the Multifaceted Impact of Artificial Intelligence on Financial Intelligence

This research focuses on the influence of simulated intelligence on people’s financial knowledge, emphasizing the aspects of risk evaluation, outcome evaluation, network security, and global collaboration. It presents the scenarios and the use of man-thinking (artificial intelligence) techniques such as AI information science, and NLP to solve the worldwide financial crises. Firstly, the introductory part of the review sets the background of monetary emergencies. Then, it proceeds with the evaluation of current monetary knowledge techniques and the administrative planning for simulated intelligence in finance. The organization in the process of creating a money policy is characterized by moral implications, human-computer-based intelligence joint effort, and contextual analysis. The study also touches on AI-driven risk assessment, stating that AI makes it easier to identify risks in real time. The study, which focuses on the case study of the UCLA Computer Science Department, through continuous monitoring, cybersecurity measures, and the comprehensive examination of AI's role in reshaping financial management, emphasizes. Lastly, the research shows that AI can be the base of the financial ecosystem which can be more adaptable, efficient, and secure, giving valuable info to the academics, the professionals in the industry, and the policymakers.