Structured AI Integration for Equitable STEM Writing: A Pilot Study of the DISCO Framework

Background: Nursing students, as future healthcare professionals, must understand how artificial intelligence can impact patient care and clinical decision making. Aim: To assess nursing students perspective about integration of artificial intelligence into health care services. Study design: Descriptive correlational, design was utilized. Setting: was conducted at Faculty of Nursing, Assiut university. Subject: 551, nursing students from the final academic year = 291 and 260 nursing student from internship year. Methods: Data were collected using : I- Personal data form, II- Nursing students awareness and knowledge regarding integration of AI into healthcare services, and III- Perception about integration of AI into healthcare services, IV- Nursing students attitude toward integration of AI into healthcare services. Results: the study revealed that nearly three fourths of study sample aware regarding to their level of total awareness toward AI (73.3%) , and two thirds of them are knowledgeable about AI, (66.6%) And more than half of the total studied nursing students have high level of total perception(51.4%), less than half of the total studied nursing students have moderate level of total perception(46.6. And (68.4%) of the total studied nursing students have positive attitude toward AI. Conclusion: there was a high statistical significant positive association among students knowledge, perception and attitude regarding AI integration in health care services at (P< 0.001). Recommendation: Encourage further researches into the long- termed impact of AI training on nursing practices, clinical decision-making, patient care, and healthcare outcomes. Key words: Integration of AI, awareness, knowledge, perception, attitude.

The vision of an intelligent city remains ambiguous, with multiple concepts indicating a gradual alignment over time between the ideas of a smart city and a sustainable city. Intelligent cities are considered an evolved concept of urban development, as they combine artificial intelligence and machine learning to form systems that can learn, adapt, and make decisions. A systematic literature review methodology is utilised to define the theoretical scope and to answer the research question. The analysis targeted comprehensive literature covering published topics on intelligent cities over the past twenty years, employing a reproducible and straightforward method to reduce bias by providing verifiable data processing details. The methodology includes identifying studies, explaining the selection process, conducting analysis and synthesis, and deriving conclusions. Eighty-six "intelligent city" papers were analysed by theme, methodology, and results. The literature on intelligent cities covers various topics, such as urban mobility, infrastructure development, risk management, AI integration, sustainability, ecosystems, community involvement, challenges, technological innovations, and inclusive governmental changes.

This study investigates the impact of AI integration, strategic leadership, and regulatory compliance on clinic performance within the context of private healthcare in Ontario, Canada. Drawing on practical insights from Nature life Health Centre, led by Kamal Khan, the research explores how emerging technologies and managerial competencies converge to shape operational success. AI integration includes the deployment of intelligent systems for diagnostics, patient flow optimization, and administrative automation. Strategic leadership is reflected in visionary planning, staff management, and performance assessment, while regulatory compliance encompasses adherence to Canadian healthcare standards and quality audits. Using a quantitative approach, data were collected from 200 healthcare professionals and analyzed using Structural Equation Modeling (SEM). Findings reveal that all three independent variables significantly and positively influence clinic performance, with AI integration emerging as the most influential driver. The study demonstrates how combining digital innovation with strong leadership and compliance mechanisms enhances revenue generation, patient satisfaction, and service efficiency. This research contributes to the growing discourse on digital transformation in healthcare and provides actionable insights for policymakers and clinic managers aiming to foster sustainable, high-performing healthcare environments in the era of intelligent technologies.

SummaryArtificial intelligence (AI) has rapidly advanced in various domains, including its application in psychotherapy. AI-powered psychotherapy tools present promising solutions for increasing accessibility to mental health care. However, the integration of AI in psychotherapy raises significant ethical concerns that require thorough consideration and regulation to ensure ethical practice, patient safety, and data privacy. This article discusses the ethical considerations surrounding the utilization of AI in psychotherapy, emphasizing the need for responsible implementation, patient privacy, and the human-AI interaction.The challenge raised by the use of artificial intelligence requires a comprehensive approach. Schools, in particular, are crucial in providing both knowledge and ethical guidance, helping young individuals decipher the complexities of online content. Additionally, parental support is essential, requiring the provision of time, fostering relationships, encouraging dialogue, and creating a safe environment to share experiences amidst the intricacies of adolescence. Reimagining social and healthcare services tailored for adolescents is equally crucial, taking into account recent societal changes. The integration of AI in psychotherapy has vast potential to transform mental healthcare. However, ensuring its accuracy and effectiveness demands a proactive approach to address associated ethical considerations. By adopting responsible practices, preserving patient autonomy, and continually refining AI systems, the field can leverage the benefits of AI in psychotherapy while maintaining high ethical standards.

Due to excessive usage of artificial intelligence and sudden expansion of Internet of Things, research on smart city and intelligent transportation system have intensified in current times. Traffic system design and traffic flow prediction is evolving as a hot topic among all stakeholders. Traffic system design must be done effectively by the integration of AI and its supporting technologies. Different critical issue like traffic congestion, designing traffic flow, and forecasting future traffic management must be addressed properly through the integration of AI. This chapter dives into the basic phenomenon of AI driven traffic flow prediction in smart cities. It proposes a design model of intelligent traffic system that includes different machine learning algorithms. It reviews AI utilization in traffic system and provides a comprehensive study for future research. Some specific open challenges in this regard are discussed and future research path is explored.

The increasing prevalence of crime in urban areas necessitates innovative solutions to enhance public safety during navigation. This study presents the "Secure Route" system, a community-driven web application designed to provide real-time safe route navigation and crime reporting capabilities. Drawing insights from existing frameworks, the application integrates user feedback, crowdsourced data, and geolocation-based updates to assess route safety. Leveraging techniques such as risk assessment, heatmap visualization, and collaborative data filtering, the system enables users to identify safer routes while avoiding crime-prone areas. This research highlights the importance of community engagement and real-time data in creating a responsive and adaptive safety navigation platform. SecureWalk offers a novel approach to urban safety by fostering collaboration between users and leveraging GIS technologies for informed decision-making. The integration of AI- driven models for analyzing crime patterns and predicting future risks further enhances the accuracy and reliability of the system’s recommendations. By combining collaborative filtering, user feedback, and advanced geospatial analytics, SecureWalk aims to improve both individual safety and community awareness, fostering a safer environment for urban commuters. Keywords: Secure Route, Community-driven, Real- time navigation, Crime reporting Geolocation-based updates, Heatmap visualization

The integration of generative artificial intelligence (Gen AI) into educational settings represents a transformative shift, reshaping conventional educational practices and students’ learning experiences. This study provides valuable insights for the effective educational implementation of Gen AI. The primary objective of this research is to examine how undergraduate students enrolled in educational technology programmes in Nepal perceive the effects of Gen AI on their academic assignments, as well as to identify the factors influencing students’ acceptance and adoption of Gen AI for these tasks. A quantitative research design was adopted, with data collected via an online survey from 174 undergraduate students pursuing a Bachelor in Technical Education in Information Technology in Nepal. Three core variables were measured in this study: the perceived impact of Gen AI, perceived ease of use, and the determinants of Gen AI acceptance and adoption. Approximately 64% of students utilised AI tools on a daily basis when completing their assessment tasks. Respondents demonstrated a relatively positive perception toward the utilisation of Gen AI in assignments (M = 3.82, SD = 0.44). Students held strong beliefs regarding Gen AI’s influence on their assignment completion (M = 3.82, SD = 0.32). They identified instructors’ integration of AI in teaching as a key factor motivating their own use of Gen AI tools. Furthermore, students predominantly relied on mobile applications to access generative AI, highlighting the relevance of portable devices within mobile learning environments. In addition, participants perceived such technologies as user-friendly for completing academic assignments. The findings of this study hold practical implications for educators, institutional policymakers, university administrators, and students interested in the transformative potential of generative artificial intelligence (Gen AI) within educational contexts.

PurposeThis research aimed to examine the current status of artificial intelligence's (AI's) integration into Chinese adult education, by analyzing the influences that AI has had on current adult education practices in China and by discussing the opportunities and challenges that adult education in China is faced with under the rapid AI development in the past 12 years.Design/methodology/approachThis research employed systematic literature analysis. CNKI (China National Knowledge Infrastructure) Chinese Journals Full-text Database was used to collect scholarly publications on the use of AI in adult education in China that was published in the past decade. Data analysis included the following steps: identifying key words and phrases, detecting underlying meanings, searching for logical connections and relationships, collecting and connecting evidence to the research questions, and drawing logical and credible conclusions.FindingsThe findings indicated that AI has been gradually integrated into Chinese adult education through innovations and explorations and AI's influence is broad and profound. More specifically, the following five main themes were identified. The field's understanding of AI technology and AI's influence on adult education has evolved and become more comprehensive; AI challenges traditional Chinese adult education practices by helping to actualize personalized learning and precision education; AI transforms adult learning resource development; AI helps to turn learning environment into an open intelligent learning system; and lastly, AI urges the shift of adult educator's role in adult learning.Research limitations/implicationsThis study is not without limitations. Contextualized in China, this study shares the limitations with other single country studies. One such limitation is “cumulation” issue. This study should be replicated in other country contexts to further validate the generalizability of the five main themes identified in this research.Practical implicationsThe five themes identified in this study can help understand the promises and challenges that AI brings to the field of adult education in China. These five themes can also serve as an integrated lens through which one can make sense of AI's integration into other countries' adult education practices.Originality/valueThis paper fulfills an identified need of understanding the current status of AI's integration into and influence on the field of adult education in China.

The digitalization and sustainability trends are triggering changes in the accounting industry. Manual accounting methods tend to fail to scale in terms of complexity. The increasing focus on sustainability has further highlighted traditional accounting methods’ shortcomings in effectively measuring and reporting environmental, social, and governance (ESG) concerns. However, many AI accounting solutions are available; the integration of AI into most of the accounting systems is difficult. Concerns about data security, ethics, the usability of technology, and implementation costs pose significant obstacles. This paper seeks to address the deficiency of sustainable accounting and the adoption of AI by offering methods of achieving a shift towards sustainable digital accounting and presenting solutions to the problem by advising corporations and the government on the relationship between technology and sustainable policies. While AI has been applied in various fields, its integration into accounting, especially in the context of sustainability, remains an emerging area of research. Given the increasing emphasis on sustainability in corporate reporting and operations, understanding how AI can contribute to this shift is essential. Still, there are aspects AI integration such as ethical, technological, and regulatory compliance which remain to be understood. In this article, the authors are going to make use of a quantitative method based on data gathered from Web of Science to study AI and sustainable digital accounting. Using this approach, the study provides a fresh, data-driven perspective on the trends and challenges surrounding AI integration in accounting systems. The primary research question is: How can AI be utilized to transform sustainable accounting practices? Other objectives concern the evaluation of AI integration regarding the effectiveness and sustainability of the accounting practices and the concerns around adoption of AI. The findings of the study highlight that AI achieves greater accuracy, accountability as well as productivity in accounting procedures and simultaneously aids sustainable practices. These findings are relevant to organizations that are looking to implement AI into their accounting functions and to those responsible for policy formulated aimed at promoting sustainable initiatives in the corporate sector.

Background The use of cloud-based storage for personal health records (PHRs) has significantly increased globally over the past thirty years. The four recently published GPOC Series articles introduced the concept of a Global Patient co-Owned Cloud (GPOC) for personal health records. The series includes a systematic review and meta-analysis, a summit, a sandbox, and a survey, with 100% participation from UN member states and key international health organisations. GPOC aims to establish patient co-ownership of PHRs, addressing integration and access challenges. Methods This study is built upon the published GPOC systematic review and meta-analysis that focused on examining cloud-based personal health records and elements such as data security, efficiency, performance, privacy and cost-based factors. However, this study selectively reviews the ethical, legislative and potential human rights dimensions of GPOC. Thus, it includes ethical aspects of co-ownership, rights, privacy, policies, and AI integration. The original study was PROSPERO registered with CRD42022342597, which serves as the foundation for the current study. Results This study offers a comprehensive global overview of ethics, legislation and initiatives by states and organisations. We analyse AI integration and future challenges for GPOC implementation. We present principles from ‘ Ethics by Design ’ and the ‘ Principles of Biomedical Ethics ’ by Beauchamp and Childress alongside the European General Data Protection Regulation (GDPR). The study presents a global overview of the relevant global latticework of legislation. Conclusions This study suggests that GPOC could potentially establish a new human right to patient co-ownership of personal health information. GPOC aims to facilitate global AI integration in healthcare and address existing challenges in PHR integration. A decentralised GPOC, supported by blockchain consensus, may offer benefits such as enhanced data security, interoperability, and equitable access to healthcare information globally. Thus, GPOC may have a positive impact on global health.

AI and Computer Engineering are combining to revolutionize software development. They encourage creativity, enhance functionality, and increase efficiency. As AI-driven tools and frameworks become indispensable to the development of today, their merging with CE is changing the game and making it possible to create intelligent, adaptable systems that can solve the most challenging problems in multiple industries. This study aims to investigate where artificial intelligence and computer engineering intertwine in modern software development, specifically by looking into methods that enhance innovation, functionality, and efficiency in software through integrated AI-driven tools, frameworks, and performance measures.A three-phase mixed-methods approach was adopted. The secondary data analysis included pertinent literature and open-source code repositories, structured surveys, and interviews of fifty industry professionals. While qualitative analysis was done by applying thematic coding to find trends, obstacles, and possibilities in AI integration, quantitative analysis focused on performance indicators such as execution speed, accuracy, and memory utilization. The most widely used AI frameworks are TensorFlow and PyTorch due to their performance and flexibility. The industry-specific trends have been identified with different priorities: healthcare focuses on accuracy (77.5%), retail on the execution speed (55 ms) and minimal resource usage, and finance on balanced optimization. Major challenges were seen in the form of barriers such as a high learning curve (20%), scalability issues (25%), and compatibility issues (30%).The report emphasizes that better tools, increased cross-platform support, and thorough training are needed to overcome the hurdles related to the adoption of AI. It identifies industry-specific needs for AI software optimization and provides useful advice on how to successfully use AI technology in sectors like healthcare, retail, and finance. Specific Contribution: This study provides practical insights into how AI is revolutionizing software development. It provides a strong analytical framework to assess performance metrics and solve integration issues by analyzing industry-specific trends and trade-offs in AI integration. This advances our understanding of CE-AI convergence and guides future research and development in AI-augmented systems.

ABSTRACTArtificial intelligence (AI) is increasingly recognized as a vital tool in electrical engineering, offering automation, error reduction, and enhanced accessibility. However, its adoption has lagged compared to other fields, highlighting a need for a comprehensive examination of its applications and challenges. This study systematically reviews AI applications in electrical engineering, classifying research findings to uncover progress, challenges, and opportunities. It aims to identify trends, gaps, and implications to guide future research and practical applications. A systematic literature review (SLR) was conducted, analyzing studies published between 2014 and 2024. Fifty‐seven publications meeting inclusion criteria were categorized into five themes: AI algorithms, power engineering, smart grid technologies, electric vehicle systems, and AI integration. The review revealed growing interest in AI applications within electrical engineering, with a significant rise in publications, particularly from China. AI algorithms demonstrated broad applicability and versatility across various domains, highlighting their potential for innovation. Additionally, there is a considerable opportunity for developing and applying frameworks to test AI innovations in electrical engineering. AI integration in electrical engineering has advanced significantly in areas such as power engineering, smart grid technologies, and electric vehicle systems. However, substantial untapped potential remains, particularly in developing frameworks for testing AI innovations. This review underscores the importance of global research efforts and identifies promising directions for advancing AI applications in electrical engineering research and practice.

This study examines the role of artificial intelligence in enhancing quality management practices within Algerian startups, focusing specifically on how AI-driven business intelligence contributes to competitive advantage through quality improvements in emerging market contexts. The research employs quantitative methodology, utilizing structural equation modeling (CB-SEM) to analyze data collected from 357 Algerian startups. The study implements a comprehensive measurement framework incorporating quality management practices, AI implementation status, and competitive advantage indicators, validated through confirmatory factor analysis. The analysis reveals that quality management supported by artificial intelligence has a moderate positive impact on competitive advantage (correlation coefficient 0.346, p &lt; 0.001). Organizations implementing AI-enabled quality management systems achieved a 52.4% improvement in overall quality metrics. Customer response capability scored highest among quality dimensions (mean score 2.86), while product-market alignment showed room for improvement (mean score 2.53). The research identified three critical areas of AI integration success: quality control automation, predictive quality management, and customer response systems. The study provides actionable insights for startups in emerging markets implementing AI-driven quality management systems. The findings suggest a staged approach to technology adoption, emphasizing the importance of foundational quality management practices before advanced AI integration. Results indicate that successful implementation requires balanced investment in both technological infrastructure and organizational capabilities. While Algerian startups demonstrate awareness of and commitment to AI-enabled quality management, with 50.8% showing a positive disposition toward adoption, actual implementation remains at moderate levels. The study highlights significant opportunities for enhancement in quality management through strategic AI integration, particularly in emerging market contexts where technological infrastructure and resource constraints present unique challenges.

This research aims to investigate the interconnectedness among intellectual capital, human capital, public relation strategy, AI integration, corporate social responsibility (CSR), and company image within organizational settings.The study utilizes purposive sampling to select participants from diverse industries. Data collection involves in-depth interviews and document analysis. Through thematic analysis, the research explores the nuanced relationships among the variables, uncovering how they contribute to shaping company image. The findings shed light on the significance of strategic integration of intellectual and human capital, effective public relation strategies, AI utilization, and CSR initiatives in bolstering company image, offering valuable insights for organizational stakeholders.

This research explores the dynamic interplay between tourism and communication, with a specific focus on the influence of digital advancements, including AI-powered tools, on tourism communication within a descriptive qualitative approach and literature study framework. Research Issues: The primary research issue addressed in this study pertains to the impact of digital advancements and AI integration in descriptive qualitative approach and literature study-based tourism communication. Communication Philosophy: A dialogical communication philosophy is adopted, emphasizing meaningful engagement between tourism providers and travelers. Communication Paradigm: The study aligns with a constructivist communication paradigm, recognizing the co-creation of meaning in tourism communication. This research underscores the significance of collaborative efforts among government agencies, travel industry stakeholders, travelers, and academics to fully utilize AI's potential in enhancing qualitative tourism communication. The study's findings are anticipated to benefit all stakeholders in tourism communication by improving efficiency and personalization, ultimately contributing to the industry's growth and sustainability.