Exploring artificial intelligence for applications of drones in forest ecology and management

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.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the "intensity gradient" of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients' intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI-patient interaction.

Artificial intelligence (AI) is transforming the education sector of different nations of the world. In spite of its commonly attested huge prospects, AI is yet to be integrated into Nigerian educational systems at a significant extent. This study examines the prospects and challenges of AI integration into Nigerian educational systems. The study adopts Venkatesh’s Unified Theory of Acceptance and Use of Technology, which explains how technologies get accepted and used in society. Questionnaire, and library and internet materials are used to source data. Survey design and mixed method with their allied analytical tools are employed. The results show pedagogic, technological, informative, administrative and socio-economic benefits of AI integration into Nigerian educational systems. Besides, it identifies the challenges constraining AI integration into the sector and proffers some solutions. The study concludes that the current low level of AI awareness and integration in Nigeria limits the ideal extent of AI impact on Nigerian educational systems. Stakeholders should collaborate significantly in making efforts to increase AI integration and find lasting solutions to the problems of AI integration into the educational systems.

The relentless integration of Artificial Intelligence (AI) into neurosurgery necessitates a meticulous exploration of the associated ethical concerns. This systematic review focuses on synthesizing empirical studies, reviews, and opinion pieces from the past decade, offering a nuanced understanding of the evolving intersection between AI and neurosurgical ethics. Following PRISMA guidelines, a systematic review was conducted to identify studies addressing AI in neurosurgery, emphasizing ethical dimensions. The search strategy employed keywords related to AI, neurosurgery, and ethics. Inclusion criteria encompassed empirical studies, reviews, and ethical analyses published in the last decade, with English language restriction. Quality assessment using Joanna Briggs Institute tools ensured methodological rigor. Eight key studies were identified, each contributing unique insights to the ethical considerations associated with AI in neurosurgery. Findings highlighted limitations of AI technologies, challenges in data bias, transparency, and legal responsibilities. The studies emphasized the need for responsible AI systems, regulatory oversight, and transparent decision-making in neurosurgical practices. The synthesis of findings underscores the complexity of ethical considerations in the integration of AI in neurosurgery. Transparent and responsible AI use, regulatory oversight, and mitigation of biases emerged as recurring themes. The review calls for the establishment of comprehensive ethical guidelines to ensure safe and equitable AI integration into neurosurgical practices. Ongoing research, educational initiatives, and a culture of responsible innovation are crucial for navigating the evolving landscape of AI-driven advancements in neurosurgery.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.

The COVID-19 pandemic has significantly impacted global healthcare systems, revealing vulnerabilities in disease surveillance, treatment capabilities, and emergency preparedness. The rapid transmission, severity, and economic disruption caused by the pandemic emphasized the urgent need for technological interventions. Artificial Intelligence (AI) emerged as a game-changer in pandemic response, providing automated solutions for early diagnosis, patient monitoring, drug discovery, and social distancing enforcement. AI-driven solutions played a crucial role in identifying infections early using advanced imaging techniques and predictive analytics. Machine learning models trained on radiographic images improved diagnostic accuracy, reducing the dependency on human expertise in overwhelmed healthcare facilities. AI-based epidemiological modeling assisted policymakers in predicting the spread of infections, enabling timely containment measures. Additionally, AI powered surveillance systems enforced social distancing regulations in public spaces, minimizing human intervention. In the healthcare sector, AI revolutionized patient care and hospital management by optimizing treatment plans, automating administrative tasks, and enhancing robotic assisted surgeries. AI-driven chatbots provided virtual consultations, reducing the burden on frontline healthcare workers. AI powered wearable devices enabled real-time patient monitoring, ensuring early detection of health deterioration. The integration of AI in drug discovery accelerated the search for COVID-19 treatments and vaccine development. AI analyzed vast biomedical datasets to identify potential antiviral compounds and optimize clinical trials, significantly reducing drug development timelines. Despite these advancements, several challenges persist, including data privacy concerns, bias infrastructure in AI models, and limitations. Ethical considerations, such as transparency in AI decision-making and fairness in healthcare AI applications, remain crucial for widespread adoption. This study explores the multifaceted role of AI in healthcare, particularly in pandemic management, highlighting its benefits, challenges, and future directions. AI has proven to be an indispensable tool in mitigating pandemics, and continuous innovations in AI applications will play a crucial role in enhancing healthcare resilience against future outbreaks.

Academic Summary Firms face intensifying pressure to innovate amid escalating US–China trade tensions. This study investigates how they integrate artificial intelligence (AI) to respond to such disruptions. We reconceptualize induced innovation theory as adversity‐induced innovation theory and test hypotheses using data from listed Chinese manufacturing firms, WTO tariff records, and customs data (2015–2022). Results indicate that higher tariff exposure increases AI integration, particularly among firms with stronger customer engagement needs. AI integration improves supply chain efficiency, sales performance, and market value. However, performance benefits follow an inverted U‐shape: extreme tariff exposure diminishes AI's compensatory effects. The study extends induced innovation theory to geopolitical adversities and advances stakeholder engagement theory by identifying customer engagement as a boundary condition for technology adoption. We establish causal relationships between tariff‐induced AI integration and firm performance through instrumental variable analysis and robustness checks. Findings inform how firms can strategically use technological innovation and customer collaboration to strengthen resilience under external shocks. Managers should view trade tensions as catalysts for AI integration rather than merely as obstacles. Managerial Summary Firms facing trade tensions should view them as catalysts for strategic AI integration rather than merely as obstacles. Our research shows that firms experiencing higher tariff exposure that invest in AI achieve measurable improvements in supply chain efficiency, sales performance, and market valuations. However, AI's compensatory benefits diminish at extreme tariff levels, revealing an optimal investment threshold that managers should consider. The benefits of AI integration are particularly strong for firms with high customer engagement needs, such as those with concentrated customer bases, substantial international sales, or significant customer‐specific investments. To maximize returns, managers should prioritize AI technologies that strengthen customer relationships by enhancing communication, responsiveness, and service personalization during trade disruptions. Successful implementation requires navigating technical and organizational challenges, including talent acquisition, data quality concerns, and integration complexities. Despite these hurdles, our findings confirm AI's strategic value as a compensatory innovation tool that not only mitigates tariff impacts but also transforms supplier‐customer relationships. By aligning AI initiatives with customer engagement objectives, firms can turn geopolitical adversity into a competitive advantage. Companies that integrate AI strategically can maintain operational continuity, preserve market position, and emerge stronger from trade disruptions.

The COVID-19 pandemic has significantly impacted global healthcare systems, revealing vulnerabilities in disease surveillance, treatment capabilities, and emergency preparedness. The rapid transmission, severity, and economic disruption caused by the pandemic emphasized the urgent need for technological interventions. Artificial Intelligence (AI) emerged as a game-changer in pandemic response, providing automated solutions for early diagnosis, patient monitoring, drug discovery, and social distancing enforcement. AI-driven solutions played a crucial role in identifying infections early using advanced imaging techniques and predictive analytics. Machine learning models trained on radiographic images improved diagnostic accuracy, reducing the dependency on human expertise in overwhelmed healthcare facilities. AI-based epidemiological modeling assisted policymakers in predicting the spread of infections, enabling timely containment measures. Additionally, AI powered surveillance systems enforced social distancing regulations in public spaces, minimizing human intervention. In the healthcare sector, AI revolutionized patient care and hospital management by optimizing treatment plans, automating administrative tasks, and enhancing robotic assisted surgeries. AI-driven chatbots provided virtual consultations, reducing the burden on frontline healthcare workers. AI powered wearable devices enabled real-time patient monitoring, ensuring early detection of health deterioration. The integration of AI in drug discovery accelerated the search for COVID-19 treatments and vaccine development. AI analyzed vast biomedical datasets to identify potential antiviral compounds and optimize clinical trials, significantly reducing drug development timelines. Despite these advancements, several challenges persist, including data privacy concerns, bias infrastructure in AI models, and limitations. Ethical considerations, such as transparency in AI decision-making and fairness in healthcare AI applications, remain crucial for widespread adoption. This study explores the multifaceted role of AI in healthcare, particularly in pandemic management, highlighting its benefits, challenges, and future directions. AI has proven to be an indispensable tool in mitigating pandemics, and continuous innovations in AI applications will play a crucial role in enhancing healthcare resilience against future outbreaks.

Artificial Intelligence (AI) has emerged as a transformative force in addressing global sustainability challenges, offering innovative solutions for energy optimization, environmental conservation, waste management, and smart urban development. This systematic review critically examines the role of AI in advancing sustainability initiatives by synthesizing peer-reviewed literature published between 2015 and 2024. The study explores how AI-driven technologies, including machine learning, deep learning, and big data analytics, contribute to improving energy efficiency, optimizing resource utilization, and enhancing decision-making processes in sustainability efforts. Key applications include AI-powered smart grids, predictive analytics for climate change mitigation, intelligent waste management systems, and real-time environmental monitoring. Despite its vast potential, the integration of AI into sustainability efforts presents significant challenges. Ethical concerns such as algorithmic bias, lack of transparency in decision-making, and data privacy issues remain critical obstacles. Additionally, high implementation costs, limited interdisciplinary collaboration, and the need for robust policy frameworks hinder the large-scale adoption of AI-driven sustainability solutions. This review underscores the importance of addressing these challenges through inclusive governance, interdisciplinary partnerships, and ethical AI development. Future research should focus on refining AI algorithms for higher accuracy, improving global data-sharing mechanisms, and developing cost-effective AI solutions that are accessible to developing regions. By leveraging AI responsibly and equitably, policymakers, researchers, and industry leaders can drive meaningful progress toward achieving the United Nations' Sustainable Development Goals (SDGs) and fostering a resilient, sustainable future for both present and future generations. Keywords: Artificial Intelligence, Sustainability, Sustainable Development, Energy Optimization, Smart Cities, Machine Learning, Environmental Conservation, Waste Management, Climate Change, Sustainable Development Goals (SDGs).

The Indonesian government has not been able to realize sustainable forest management under the 1945 constitutional mandate, causing much forest damage. Indonesia urgently needs good forest governance. Therefore, it is essential to give a more significant role to local communities in managing forests in the future. The primary objective of this research project is to find sustainable forest management practices based on the customary law of the Bayan community. This research uses the literature review and case study method. An additional objective of this research is to study the problems that occur due to the absence of legal protection for local communities with regard to forest use and conflicts. This research demonstrates that the Indonesian government provides extensive support to the private forestry sector (for example through award of permits) and has ignored the rights of people who live around the forest. Forest management through customary law, such as Awiq-awiq in Bayan village, can provide great economic, social, and cultural benefits to local communities. Awiq-awiq regulates prohibitions, sanctions, and the procession of the customary assembly in association with forest management, and has been an effective way to use forests sustainably. The local community is highly compliant with awiq-awiq because they have been directly involved in drafting and reviewing this regulation. The customary law approach used by the Bayan community is an example of sustainable forest management practices that run well; and could be a valuable model for the Indonesian state and other forest conservation managers to consider.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) has revolutionized the financial services industry by enhancing customer engagement, fraud detection, predictive analytics, regulatory compliance, and marketing strategies. This study systematically reviews 83 scholarly studies, including peer-reviewed journal articles, industry reports, and financial institution case studies, to assess AI’s impact on financial CRM. The findings indicate that AI-powered chatbots, virtual assistants, and sentiment analysis tools have significantly improved customer interactions, reducing response times by 57% and operational costs by 38%, while increasing customer retention rates by 28%. AI-driven fraud detection systems have enhanced transaction monitoring, reducing false positives by 52% and improving fraud detection efficiency by 74%, leading to a 43% decrease in financial losses related to fraud. Predictive analytics has transformed credit risk assessment, improving loan approval accuracy by 67%, expediting loan processing by 29%, and reducing default rates by 23%. AI has also optimized regulatory compliance by automating Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, increasing compliance accuracy by 58% and reducing penalties by 37%. Additionally, AI-driven marketing strategies have strengthened customer targeting, increasing engagement by 53% and boosting product adoption rates by 31%, while Customer Lifetime Value (CLV) models have contributed to a 27% increase in long-term customer retention and a 22% improvement in per-customer profitability. This study provides a comprehensive analysis of AI-driven CRM’s measurable benefits in financial services, demonstrating its role in enhancing decision-making, streamlining operations, improving financial security, and fostering long-term customer loyalty. The findings contribute to the expanding literature on AI in financial CRM and offer strategic insights for financial institutions, policymakers, and technology developers aiming to optimize AI adoption for sustainable growth and competitive advantage.

The rapid advancement of Artificial Intelligence (AI) has fundamentally reshaped contemporary security systems, leading to significant improvements in surveillance, threat detection, and incident response across both physical and digital domains. By leveraging machine learning algorithms, neural networks, and predictive analytics, AI enhances the speed and precision of security operations while reducing human error and reliance on purely manual decision-making. In physical security contexts, AI-driven systems analyze real-time video feeds to detect anomalous behavior, identify individuals, and track suspicious movements with high accuracy. In digital security, AI supports malware detection, network intrusion prevention, and phishing mitigation through continuous learning from evolving threat patterns. Moreover, the integration of AI with big data analytics enables comprehensive risk assessment by processing large volumes of heterogeneous data, thereby facilitating early threat identification and proactive mitigation. Natural language processing techniques further contribute by detecting potential insider threats through the analysis of communication patterns and textual data. This paper adopts a qualitative, literature-based methodology grounded in thematic analysis. This paper draws on secondary data obtained from peer-reviewed journal articles, policy reports, and authoritative industry publications related to AI applications in security systems. The analysis synthesizes existing scholarship to identify dominant trends, operational benefits, and critical challenges associated with AI-driven security frameworks.The central objective of this paper is to examine how AI is transforming modern security systems while critically assessing the ethical, legal, and governance challenges that accompany its adoption. The findings indicate that although AI significantly enhances efficiency, predictive capability, and system resilience, persistent concerns related to data privacy, algorithmic bias, transparency, and over-reliance on automated decision-making continue to pose serious risks. This paper underscores the necessity of robust regulatory frameworks, transparent AI decision processes, and sustained human oversight to ensure responsible and trustworthy AI deployment. Ultimately, the paper argues that AI should be viewed not as a replacement for human judgment, but as an augmentative tool that strengthens security systems when embedded within ethical, legal, and organizational safeguards.