Integration of artificial intelligence for sustainable freshwater fishery governance: an Okavango River ecosystem perspective

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Artificial Intelligence (AI) is revolutionizing the field of health sciences, reshaping how we teach, learn, and practice medicine. As AI technologies become increasingly integrated into healthcare systems, their impact on health sciences education cannot be overstated. From personalized learning experiences to advanced diagnostic training, AI is poised to enhance the quality and accessibility of education for future healthcare professionals. However, this transformation also raises critical questions about ethics, equity, and the future role of educators in an AI-driven world. The transformative role of Artificial Intelligence (AI) in health sciences education is increasingly recognized as a pivotal factor in shaping the future of medical training and practice. As AI technologies continue to evolve, their integration into educational curricula presents both opportunities and challenges that must be carefully navigated to enhance the learning experience for future healthcare professionals. One of the most significant contributions of AI to health sciences education is its ability to personalize learning. Traditional teaching methods often follow a one-size-fits-all approach, which can leave some students struggling to keep up while others are not sufficiently challenged. AI-powered platforms, such as adaptive learning systems, analyze individual student performance and tailor content to meet their unique needs. For example, tools like Osmosis and AMBOSS use AI to provide customized study plans, ensuring that students focus on areas where they need the most improvement (Topol, 2019). This personalized approach not only improves learning outcomes but also fosters a more inclusive educational environment. AI is also transforming clinical training by simulating real-world scenarios. Virtual patient simulations, powered by AI, allow students to practice diagnosing and treating conditions in a risk-free environment. These simulations can replicate rare or complex cases that students might not encounter during their clinical rotations. For instance, platforms like Touch Surgery and SimX use AI to create immersive surgical and emergency care simulations, providing students with hands-on experience before they enter the operating room (McGaghie et al., 2011). Such tools bridge the gap between theory and practice, preparing students for the complexities of modern healthcare. Moreover, AI is enhancing the role of educators by automating administrative tasks and providing data-driven insights into student performance. Grading, attendance tracking, and even curriculum design can be streamlined using AI, allowing educators to focus on mentoring and engaging with students. AI-driven analytics can also identify at-risk students early, enabling timely interventions to support their academic success (Wartman & Combs, 2018). By augmenting the capabilities of educators, AI empowers them to deliver more impactful and student-centered teaching. AI's potential to revolutionize health sciences education lies in its ability to personalize learning experiences and improve educational outcomes. For instance, AI-driven tools can facilitate realistic simulations and automated assessments, allowing students to engage in practical scenarios that mimic real-world clinical situations (Santos & Lopes, 2024). This capability not only enhances the learning process but also prepares students for the complexities of patient care in a technology-driven environment (Grunhut et al., 2022). Furthermore, the incorporation of AI into curricula can foster critical thinking and decision-making skills, essential for navigating the ethical dilemmas that arise in medical practice (Grunhut et al., 2022). Despite the promising applications of AI in education, the integration of these technologies into medical curricula has been slow. A scoping review highlighted that many medical schools have yet to adopt AI training, primarily due to a lack of systematic evidence supporting its implementation (Lee et al., 2021). Additionally, concerns regarding data protection and the ethical implications of AI use in healthcare education have been raised, indicating a need for comprehensive AI education that addresses these issues (Veras et al., 2023; Frehywot & Vovides, 2023). Students have expressed a desire for more robust training in AI, emphasizing the importance of understanding its role in healthcare delivery and decision-making processes (Ahmad et al., 2023; Derakhshanian et al., 2024). Moreover, the rapid advancement of AI technologies necessitates continuous curriculum updates to keep pace with emerging trends. As noted in recent literature, the integration of AI into biomedical science curricula should include subjects related to informatics, data sciences, and digital health (Sharma et al., 2024). This approach not only equips students with the necessary skills to utilize AI effectively but also prepares them for the evolving landscape of healthcare, where AI will play an integral role in diagnostics, treatment personalization, and patient management (Santos & Lopes, 2024; Secinaro et al., 2021). However, the implementation of AI in health sciences education is not without challenges. Ethical considerations surrounding AI's impact on healthcare equity and the potential for bias in AI algorithms must be addressed (Frehywot & Vovides, 2023; Han et al., 2019). Ensuring that AI technologies are used responsibly and equitably in education and practice is crucial to avoid exacerbating existing disparities in healthcare access and outcomes (Rigby, 2019). Furthermore, the lack of faculty expertise in AI poses a significant barrier to its integration into medical education, highlighting the need for targeted training and resources for educators (Derakhshanian et al., 2024). However, the integration of AI into health sciences education is not without challenges. Ethical concerns, such as data privacy and algorithmic bias, must be addressed to ensure that AI tools are used responsibly. Additionally, there is a risk of over-reliance on AI, potentially undermining the development of critical thinking and clinical judgment skills. Educators must strike a balance between leveraging AI’s capabilities and preserving the human elements of teaching and learning. Equity is another pressing issue. While AI has the potential to democratize education, access to these technologies remains uneven. Institutions in low-resource settings may struggle to adopt AI-driven tools, exacerbating existing disparities in global health education. Policymakers and educators must work together to ensure that the benefits of AI are accessible to all, regardless of geographic or socioeconomic barriers. In conclusion, AI is a powerful tool that holds immense promise for transforming health sciences education. By personalizing learning, enhancing clinical training, and supporting educators, AI can help prepare the next generation of healthcare professionals to meet the demands of an increasingly complex healthcare landscape. However, its integration must be guided by ethical principles and a commitment to equity, However, the successful integration of AI into educational curricula requires a concerted effort to address ethical concerns, update training programs, and equip both students and faculty with the necessary knowledge and skills. As the healthcare landscape continues to evolve, embracing AI in education will be essential for fostering a new generation of healthcare providers who are adept at leveraging technology to improve patient care. As we embrace this technological revolution, we must remember that AI is not a replacement for human expertise but a complement to it. The future of health sciences education lies in the synergy between human ingenuity and artificial intelligence.

Amidst rapid growth and increasing complexity, the events and festivals industry is undergoing a significant transformation through the integration of Artificial Intelligence (AI) technologies. This paper investigates the impact of AI on the events landscape, examining its current applications, emerging trends, and future implications. The research questions address AI's current use in events, emerging trends, and potential future transformations. The study highlights AI's potential to improve attendee experiences, streamline event management, and enhance safety measures, offering deep insights into its substantial industry impact. Using a comprehensive research methodology, this paper explores the complex relationship between AI and events/festivals using empirical research, industry analysis, and case studies. It employs frameworks like AI-driven engagement tools, predictive analytics, and immersive experiences to assess AI's impact on the events landscape. Key findings highlight AI's ability to personalize experiences, optimize logistics, and enhance safety and sustainability. The paper also addresses challenges like data privacy, algorithmic bias, and ethical considerations, emphasizing the need for responsible AI deployment and risk mitigation. By offering best practices and ethical guidelines, this research aims to guide industry stakeholders in integrating AI effectively, steering the events and festivals industry towards greater innovation and efficiency.

Employee attrition is a critical challenge for organizations, impacting productivity, operational costs, and workforce stability. Traditional approaches to managing attrition rely on reactive strategies, often failing to provide predictive insights. The advent of Artificial Intelligence (AI) has transformed attrition management by enabling data-driven decision-making, predictive analytics, and proactive employee engagement.This research explores the role of AI in predicting and managing employee attrition through machine learning algorithms, natural language processing (NLP), and AI-driven sentiment analysis. AI models analyze vast datasets, including employee performance metrics, engagement surveys, and organizational culture indicators, to identify early warning signs of attrition. Predictive analytics empowers HR professionals to implement targeted retention strategies, enhance employee experience, and reduce voluntary turnover.Furthermore, AI-driven chatbots and virtual HR assistants contribute to employee satisfaction by offering personalized career development suggestions, real-time feedback, and mental well-being support. Explainable AI (XAI) frameworks ensure transparency in AI-driven decisions, fostering trust between employees and organizations. Despite AI’s potential, ethical concerns, data privacy, and algorithmic biases remain key challenges that require robust governance frameworks.This study provides a comprehensive analysis of AI applications in attrition management, highlighting case studies from multinational corporations that have successfully integrated AI for workforce retention. The findings underscore AI's transformative potential in HRM, enabling organizations to shift from reactive to proactive attrition management strategies. The paper concludes with future research directions on AI’s evolving role in predictive HR analytics and its integration with emerging technologies like blockchain and the metaverse for enhanced workforce planning.

The integration of artificial intelligence (AI) into cosmetic medicine promises to revolutionize the field by enhancing diagnosis, treatment planning, and patient care. This manuscript explores the current adoption and perceptions of AI among professionals in the realm of cosmetic dermatology and plastic surgery, utilizing insights from the IMCAS Congress 2024 attendees. A survey employing a digital questionnaire with 14 questions was distributed among attendees of the IMCAS Congress 2024 to evaluate their familiarity with AI, usage in clinical practice, perceived advantages, and concerns regarding data privacy and security. The survey revealed that a majority of respondents are familiar with AI's potential in cosmetic medicine, yet there is a notable discrepancy between awareness and actual application in practice. Concerns over data privacy and a pronounced need for further training were also highlighted. Despite recognizing AI's benefits in cosmetic medicine, significant barriers such as data privacy concerns and the need for more comprehensive training resources must be addressed. Enhancing education on AI-applications and developing strategies to mitigate privacy risks are imperative for leveraging AI's full potential in improving patient care and outcome in cosmetic medicine.

This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.

The maritime industry stands on the brink of a technological revolution, with artificial intelligence (AI) at the helm. This paper explores AI's transformative potential in ship management, where it is poised to redefine roles and responsibilities within the maritime workforce. With the potential to automate up to 300 million full-time jobs globally, AI's impact on route optimization, predictive maintenance, and decision support systems is significant. The research underscores AI's role in enhancing fuel efficiency and operational effectiveness, setting the stage for a new era in maritime operations. Through a comprehensive methodological approach that includes a literature review, benchmark, case studies, and discussions with key stakeholders—ship-owners, classification societies, and technology providers—the paper analyzes the technical and financial impact of AI-powered initiatives. It proposes a novel AI-based decision support system tailored to the unique challenges of ship management. The paper outlines a detailed governance structure for AI adoption, reflecting the industry's shift towards smart, eco-friendly ships. This shift is projected to increase the number of technical employees in the industry from 35,549 in 2018 to 49,000 by 2028, highlighting the growing demand for skilled professionals in this evolving landscape. The findings reveal that AI significantly enhances operational efficiency and decision-making capabilities through predictive analytics and real-time optimization. The integration of AI initiatives, such as digital twins, is identified as a key future enhancement, potentially offering substantial efficiency gains and risk mitigation. The study also notes that 6.5% of ships in operation and 51% on order are capable of operating on alternative fuels, reflecting a growing uptake of methanol, LPG, and the first hydrogen-fuelled newbuilds. This trend underscores the industry's commitment to sustainable shipping practices and the pivotal role of AI in achieving these goals. The study introduces a pioneering AI framework for ship management, emphasizing the need for a custom AI evaluation framework to assess and prioritize AI technology applications across the maritime industry. It suggests that sustainable value creation can be realized through AI adoption models, enhancing collaboration and regulatory compliance. The research highlights AI's potential to revolutionize the efficiency, safety, and sustainability of maritime operations, presenting transformative insights for the industry's advancement.

This study examines the integration of Artificial Intelligence (AI) in supply chain management (SCM) during the transition from Industry 4.0 to Industry 6.0. The focus is on improving operational efficiency, promoting human-centric collaboration, and advancing sustainability within supply chains. As industries progress, the need to incorporate AI technologies that improve decision-making and operational resilience while ensuring sustainable practices becomes increasingly critical. This systematic review aims to explore how AI is transforming SCM through these industrial transitions. Utilising the PRISMA framework, a systematic review was conducted to gather and analyse relevant literature published between 2010 and 2023. A comprehensive search of databases including Web of Science, Scopus, IEEE Xplore, Google Scholar, and ScienceDirect was performed. The review involved rigorous screening for eligibility and thematic analysis using Atlas-ti software to identify key themes and patterns related to AI integration in SCM. The findings indicate that AI integration significantly improves SCM by improving demand forecasting, inventory management, and overall decision-making capabilities. Industry 5.0 focuses on human-AI collaboration, improving customisation and problem-solving. AI technologies also contribute to sustainability by optimising resource utilisation and reducing environmental impacts. However, challenges such as cybersecurity risks and workforce skill gaps need to be addressed to fully leverage AI's potential. Integrating AI in SCM not only improves operational efficiency and sustainability but also promotes resilience against disruptions. The insights from this review offer valuable guidance for both academics and practitioners aiming to optimise supply chain operations through AI technologies from Industry 4.0 to Industry 6.0. The study underlines the importance of a balanced approach that integrates technological developments with human-centric and sustainable practices.

Background: The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical procedures, offering substantial benefits across various domains. With advancements in AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), healthcare systems are witnessing improvements in early detection, patient treatment, and overall administration. This article traces the evolution of AI, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes. Methods: This comprehensive review analyzes the existing literature on AI applications in healthcare, focusing on various AI methodologies and their integration into clinical settings. It evaluates the effectiveness of AI in processing large datasets, improving diagnostic precision, and facilitating data-driven decision-making. The study also explores the ethical, legal, and technical challenges associated with AI deployment in medical environments. Results: AI technologies have demonstrated significant improvements in healthcare, particularly in early disease detection, personalized treatment plans, and resource management. The use of AI in analyzing vast medical datasets has enhanced diagnostic accuracy, reduced costs, and optimized patient care. However, challenges related to ethical considerations, patient privacy, and system reliability remain critical barriers to full-scale AI adoption. Conclusion: Despite the challenges, AI is positioned as an indispensable tool in modern medicine, capable of enhancing preventive care, personalizing treatments, and improving healthcare delivery. This review proposes a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare. Further research is essential to maximize AI's potential while addressing ethical and practical concerns, ensuring safe and effective implementation in clinical settings.

The integration of Artificial Intelligence (AI) in healthcare has been impeded by a significant issue: a lack of trust among healthcare professionals, stemming from the opacity of AI decision-making processes and a general unfamiliarity with AI technologies. This study investigates the impact of AI's explainability and healthcare professionals' familiarity with AI on their trust in AI applications within healthcare settings. Adopting a quantitative research methodology, the study utilized a structured questionnaire to gather data from a diverse group of healthcare professionals, including doctors, nurses, and administrators, across various hospitals and healthcare institutions in Pakistan. The research employed a stratified random sampling approach to ensure a comprehensive and representative data set. The results indicated a positive and significant relationship between AI explainability and trust in AI (Path Coefficient: 0.62, t-Value: 5.20), suggesting that clearer and more transparent AI decision-making processes enhance healthcare professionals' trust., Similarly, familiarity with AI was found to positively influence trust in AI (Path Coefficient: 0.48, t-Value: 4.35), highlighting the importance of exposure and understanding of AI systems among healthcare professionals. These findings have crucial implications for both AI developers and healthcare administrators. For AI developers, the emphasis must be on creating more transparent and interpretable AI systems. For healthcare administrators, the results suggest the need to invest in training and educational programs to increase professionals' familiarity with AI, thereby enhancing trust and acceptance. The study significantly contributes to the existing literature by empirically validating the importance of AI explainability and familiarity in building trust in AI within the healthcare context, especially in a developing country setting. For policymakers, these insights are critical in guiding strategies and policies aimed at effectively integrating AI into healthcare systems. By addressing the identified factors, healthcare sectors can better leverage AI's potential, leading to improved patient care and more efficient healthcare operations.

The integration of artificial intelligence (AI) in education presents a transformative opportunity to keep pace with technological advancements. In medical education, AI can personalize training, improve diagnostic skills through simulations, and enhance access to databases. However, its implementation faces significant challenges, including high costs, ethical concerns, and risks to academic integrity. In Kazakhstan, despite strategic goals for digitalization, these global challenges are compounded by local barriers. This review aims to analyze the benefits and challenges of AI integration in medical education based on international and national literature and to develop recommendations for stakeholders in Kazakhstan.A scoping review of 34 relevant sources (up to May 2025) was conducted using PubMed, Web of Science, Scopus, and Google Scholar databases, with a specific focus on studies related to Kazakhstan.The analysis confirmed AI's significant potential to enhance education. However, several critical barriers were identified in Kazakhstan: a low level of AI literacy among students (up to 95.3% lack specialized knowledge) and faculty, pronounced infrastructural inequality between regions, and a deficit in local scientific capacity.For the successful integration of AI, Kazakhstan must shift its focus from merely implementing technology to cultivating human capital. Bridging the gap between AI's potential and the system's actual readiness requires systemic actions: large-scale training of personnel, fostering interdisciplinaryresearch, and creating an equitable, ethically grounded educational ecosystem.Keywords:medical education, artificial intelligence, digital health, Kazakhstan.

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

Background/Aim: From accurate diagnostics to personalised treatment plans, artificial intelligence (AI) has the potential to revolutionise healthcare. The abundance of medical data has led to AI being employed for accurate diagnoses, treatment protocols and patient care. Students' perception of AI integration in medical education is crucial for its successful implementation. This study aimed to assess the acceptance and understanding of AI integration among students in medical education across different regions of India through a cross-sectional observation. Methods: A pan-India survey was conducted among medical students between 1 August 2023 to 20 August 2023 with a pre-validated questionnaire covering AI awareness and understanding through Google Form, circulated via WhatsApp. Results: A total of 730 medical students completed the survey of which 58.6 % were males and 41.4 % were females. Most students (80.7 %) knew about AI, but 53.6 % had limited awareness of AI in medicine. Opinions on AI integration was diverse, with 46.8 % in favour. Workshops (45.2 %) and lectures (31.1 %) were preferred learning formats. Students were interested in various AI topics and expect AI to positively impact medicine (45.9 %). Radiology, surgery and general medicine were predicted to be most influenced by AI. Concerns about overreliance on AI (49.2 %) and lack of empathy (43.7 %) were highlighted. Conclusions: Medical students in India display a keen interest in AI and its integration into medical education. To fully harness AI's potential in healthcare, comprehensive AI curricula and faculty training are needed. Students are aware of the challenges and opportunities, emphasising the importance of balanced AI adoption in medical practice and education.

The integration of artificial intelligence (AI) in customer service marketing is revolutionizing the way businesses interact with their customers. This chapter explores the transformative potential of AI technologies in enhancing customer service experiences and optimizing marketing strategies. AI-driven tools such as chatbots, predictive analytics, and personalized recommendations are enabling companies to provide timely, efficient, and personalized interactions that meet the evolving expectations of customers. By analyzing large datasets, AI can identify patterns and trends, allowing businesses to anticipate customer needs and tailor their marketing efforts accordingly. The chapter discusses the implementation of AI in various customer service scenarios, highlighting case studies where AI has successfully augmented traditional marketing approaches. This research underscores the importance of strategic AI adoption and provides insights into future trends that will shape the landscape of customer service marketing.

The integration of artificial intelligence (AI), including the recent appearance of revolutionary large language models (LLMs), marks a transformative era in the field of negotiations, reshaping traditional practices and presenting a range of opportunities and challenges. This article delves into the evolving interplay between negotiation and various AI technologies, as they now combine massive computational power with user-friendly interfaces capable of fluent, multi-topic conversations. The article categorizes AI's role in negotiation into assistance, semi-automation, and automation, each offering unique advantages and addressing different negotiation needs. While AI's ability to compensate for human limitations in rationality, emotion, and computational capacity is promising, it also raises concerns regarding biases, ethical considerations, and the reliability of automated decision-making. The burgeoning AI and negotiation collaboration necessitates a balanced approach, harnessing AI's potential to enhance negotiation outcomes while conscientiously navigating its challenges. This article aims to foster understanding of and influence the future trajectory of negotiation and AI, highlighting the need for ongoing research and development to ensure ethical, effective, and equitable negotiation practices in an AI-augmented future.