Blockchain and AI Innovations: Driving Sustainability in Modern Educational Frameworks

The logistics and facility management industries are currently experiencing a paradigm shift driven by the rapid advancement of Artificial Intelligence (AI) and blockchain technology. These innovative technologies are poised to revolutionize various aspects of logistics operations, particularly in the context of sea cargo handling, where the demands for efficiency, security, and transparency are increasingly critical. As global trade continues to expand and the complexity of supply chains grows, the need for more advanced and reliable systems becomes ever more pressing. This research paper delves into the transformative potential of integrating AI and blockchain technologies within logistics and facility management, with a focused lens on sea cargo handling services. Sea cargo handling, a vital component of international trade, has traditionally relied on manual processes and centralized systems that often struggle with inefficiencies, delays, and security vulnerabilities. The integration of AI and blockchain offers a robust solution to these challenges by enhancing operational efficiency through automation and predictive analytics, while simultaneously ensuring the security and transparency of transactions through the decentralized and immutable nature of blockchain. AI's ability to process vast amounts of data in real-time allows for improved decision-making, predictive maintenance, and the optimization of resource allocation. Blockchain, on the other hand, provides a secure, tamper-proof ledger that ensures the authenticity and integrity of cargo movements, from origin to destination, reducing the risk of fraud and enhancing compliance with international regulations. This paper presents a comprehensive exploration of the integration of AI and blockchain in the logistics sector, specifically within the realm of sea cargo handling. The study begins with an extensive literature review that examines the current state of AI and blockchain technologies in logistics and facility management, highlighting both the potential benefits and the existing challenges. The review covers various applications of AI, such as machine learning algorithms for demand forecasting and route optimization, and explores how blockchain can be used to create transparent and secure supply chains. It also addresses the synergy between these technologies, proposing a combined approach that leverages the strengths of both AI and blockchain to create a more resilient and efficient logistics framework. Following the literature review, the paper outlines the methodologies employed to integrate AI and blockchain into sea cargo handling operations. This includes the development of AI models for optimizing cargo handling processes, predicting port congestion, and automating customs clearance procedures. The methodologies also cover the implementation of blockchain for tracking the provenance of goods, verifying transactions, and ensuring that all cargo movements are securely recorded and accessible to relevant stakeholders. The paper details the steps involved in deploying these technologies, from initial assessment and planning to the actual implementation and integration with existing systems.To provide a practical perspective, the paper includes a detailed case study of a major port that has successfully implemented AI and blockchain technologies in its sea cargo handling operations. This case study illustrates the tangible benefits of this integration, such as significant improvements in operational efficiency, enhanced security measures, and cost reductions. It also highlights the challenges encountered during the implementation process, such as the need for extensive training and the complexities of integrating new technologies with legacy systems. The case study serves as a valuable example for other ports and logistics companies considering similar technological upgrades, offering insights into the best practices and potential pitfalls.This research paper underscores the transformative impact of AI and blockchain on logistics and facility management, particularly in sea cargo handling services. By integrating these technologies, logistics operations can achieve higher levels of efficiency, security, and transparency, ultimately leading to more reliable and cost-effective supply chains. However, the paper also cautions that the successful implementation of these technologies requires careful planning, a clear understanding of the specific operational context, and a willingness to invest in the necessary infrastructure and training. As the logistics industry continues to evolve, the integration of AI and blockchain will likely become a standard practice, paving the way for a more advanced and secure global trade network.

The integration of artificial intelligence (AI) into the new product development (NPD) process has been shown to significantly enhance innovation success. Drawing on situated AI theory, this study investigates two core senior management behaviours—emphasis on AI adoption and reward systems for AI adoption – and their roles in catalysing AI-driven innovation and facilitating AI integration across the seven NPD stages. Based on empirical analysis of 558 AI-driven NPD projects, the findings reveal that both senior management’s emphasis on AI adoption and reward systems positively impact innovation performance. Notably, senior management’s emphasis on AI adoption, rather than reward systems, significantly improves innovation quality, innovation speed, and product innovativeness. The results further indicate that senior management’s emphasis on AI adoption facilitates AI integration across six of the seven NPD stages, with the exception of the product testing stage. In contrast, reward systems for AI adoption positively influence AI usage in four stages, primarily within the early (idea development, business analysis, product design) and late (operations management) phases of the NPD process. This research advances situated AI theory to elucidate the mechanisms through which senior management behaviours drive AI-driven innovation success and adoption, and guides manager in effectively leveraging AI’s potential within the NPD process.

The integration of Artificial Intelligence (AI) into personal finance and wealth management has fundamentally reshaped financial behaviors and decision-making processes. The primary objective of this study is to evaluate the role of AI in influencing personal financial behaviors and wealth management outcomes. Specifically, it aims to determine how AI adoption, investment, and usage impact personal savings and net worth. This study adopts a quantitative approach, utilizing secondary data from trusted sources such as Our World in Data and the Federal Reserve Bank of St. Louis. The dataset spans from 2010 to 2022, capturing trends over a significant period of AI development and adoption. A multivariate regression model is employed to examine the relationships between the dependent variables, Personal Savings Rate and Change in Net Worth, and independent variables such as AI adoption rate, AI investment, and household debt-to-income ratio. Descriptive statistics, correlation analysis, and stationarity tests are conducted to ensure data reliability and model validity. Diagnostic checks, including heteroskedasticity tests and Durbin-Watson statistics, further validate the robustness of the results. The study reveals that AI adoption positively influences personal savings by encouraging disciplined financial behaviors, consistent with the findings of prior research. However, its impact on wealth accumulation is less direct, with AI investment showing a surprising negative association with changes in net worth. This indicates inefficiencies in resource allocation or lag effects in the benefits of large-scale AI investments. Traditional economic factors, such as household debt and spending habits, continue to play significant roles in shaping financial outcomes, highlighting the enduring influence of non-technological determinants. The study also underscores the role of macroeconomic variables, such as unemployment, in moderating AI’s impact, with precautionary savings behaviors emerging during periods of economic uncertainty. Based on the findings, several actionable recommendations emerge. For individuals, the adoption of AI-driven tools that promote financial literacy and track spending can enhance savings and improve overall financial health. Financial institutions should prioritize user-centric designs in AI platforms, ensuring accessibility and functionality for diverse demographics. Policymakers are encouraged to support initiatives that bridge disparities in AI adoption, such as digital literacy programs and affordable access to financial technologies. Moreover, strategic investment in AI tools that address wealth management complexities, such as portfolio optimization and risk assessment, is critical for improving long-term financial outcomes. Originality This study contributes to the growing body of literature on AI in finance by offering a dual focus on personal savings and wealth management. Unlike previous studies that often treat these domains independently, this research provides an integrated perspective, highlighting both the synergies and divergences in AI’s impact. The findings on the nuanced relationship between AI investment and financial outcomes offer a fresh lens for evaluating the effectiveness of technological advancements. Furthermore, the study’s emphasis on traditional economic factors alongside AI-related variables underscores its originality in bridging the gap between technological innovation and foundational economic principles. This approach provides a robust framework for future research and practical applications in finance.

Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research

Artificial Intelligence (AI) has the potential to reshape family medicine by enhancing clinical, educational, administrative, and research operations. Despite AI's transformative potential, its adoption is inconsistent, and strategic frameworks remain limited. This study explores current AI adoption, organizational policies, integration priorities, and budget allocations within family medicine departments. A survey of 218 family medicine department chairs in the US and Canada was conducted via SurveyMonkey from August 13 to September 20, 2024, as part of the Council of Academic Family Medicine (CAFM) Educational Research Alliance (CERA) omnibus project. Survey questions assessed current and planned AI utilization, presence of formal departmental or organizational policies (defined as written guidelines, strategic plans, or frameworks), integration priorities, and budget allocations. Data were analyzed using Chi-square tests, Wilcoxon Rank Sum tests, and Kruskal-Wallis tests, with a primary focus on bivariate comparisons. The survey achieved a 50.9% response rate (111/218). Current AI use was reported by 56.9% (62/109), while 37.6% (41/109) indicated formal organizational policies. Primary goals for AI integration included improving clinical operations (52.3%), administrative streamlining (16.5%), educational applications (11.9%), and research (4.6%). Budget allocations were minimal (median, 0%; mean 2.4%), though departmental budgets likely underestimate actual institutional investment in AI. Departments reporting AI use had significantly more full-time equivalent faculty (median, 40.0 vs 25.5, P = .023). Geographic and chair demographics were not significantly associated with differences in AI adoption. AI integration in family medicine departments is viewed as essential, though current adoption is limited by uncertain strategic planning and minimal departmental budget allocations, potentially reflecting reliance on centralized institutional information technology (IT) investments. While AI is widely viewed as important, structured policy frameworks and implementation strategies are still developing. Further research is essential to guide policy development and strategic investment to ensure AI's safe, efficient, and effective integration into family medicine.

The integration of Artificial Intelligence (AI) in food production is revolutionizing the industry by enhancing efficiency, improving food safety, and driving sustainability. Smart food factories powered by AI are optimizing production processes through automation, predictive maintenance, and real-time quality control. AI-driven supply chain management is reducing food waste, ensuring better resource allocation, and streamlining logistics. Furthermore, AI is playing a crucial role in developing personalized nutrition and alternative protein sources, catering to evolving consumer demands. Despite its numerous benefits, AI adoption in food manufacturing faces challenges such as high implementation costs, data privacy concerns, and workforce displacement. Overcoming these obstacles requires investment in AI training, regulatory frameworks, and ethical AI deployment. Looking ahead, advancements in robotics, block chain integration, and AI-powered 3D food printing will further shape the future of food production. By addressing these challenges and leveraging AI responsibly, the food industry can create safer, more efficient, and sustainable food production systems for the future.

The integration between blockchain and artificial intelligence (AI) has gained a lot of attention in recent years, especially since such integration can improve security, efficiency, and productivity of applications in business environments characterised by volatility, uncertainty, complexity, and ambiguity. In particular, supply chain is one of the areas that have been shown to benefit tremendously from blockchain and AI, by enhancing information and process resilience, enabling faster and more cost-efficient delivery of products, and augmenting products' traceability, among others. This paper performs a state-of-the-art review of blockchain and AI in the field of supply chains. More specifically, we sought to answer the following three principal questions: Q1-What are the current studies on the integration of blockchain and AI in supply chain?, Q2-What are the current blockchain and AI use cases in supply chain?, and Q3-What are the potential research directions for future studies involving the integration of blockchain and AI? The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI.

Recently, 6G-enabled Internet of Things (IoT) is gaining attention and addressing various challenges of real time application. The artificial intelligence plays a significant role for big data analytics and presents accurate data analysis in real time. However, designing big data analysis through artificial intelligence faces some issues in terms of security, privacy, training data, and centralized architecture. In this article, blockchain-based IoT framework with artificial intelligence is proposed which presents the integration of artificial intelligence and blockchain for IoT applications. The performance of the proposed architecture is evaluated in terms of qualitative and quantitative measurement. For qualitative measurement, how the integration of blockchain and artificial intelligence addresses various issues are described with the description of AI oriented BC and BC oriented AI. The performance evaluation of proposed AI-BC architecture is evaluated and compared with existing techniques in qualitative measurement. The experimental analysis shows that the proposed framework performs better in comparison with the existing state of art techniques.

The concept paper provides a detailed analysis of how strategic policy frameworks can facilitate the adoption and integration of artificial intelligence (AI) to drive economic and social development in Nigeria. This executive summary outlines the paper's key objectives, strategic frameworks, and anticipated outcomes, emphasizing the need for robust policies to harness the transformative power of AI. The primary objective of this paper is to identify and propose policy frameworks that can support the widespread adoption of AI across various sectors in Nigeria. It recognizes the potential of AI to revolutionize industries such as healthcare, agriculture, finance, and education, thereby significantly contributing to national development. The paper underscores the necessity for a structured approach to AI implementation, addressing the unique challenges and opportunities within the Nigerian context. Central to the paper is the exploration of policy frameworks that can facilitate AI adoption. It discusses the importance of establishing clear regulatory guidelines that ensure ethical AI use, protect data privacy, and promote transparency. The paper also highlights the need for policies that encourage investment in AI research and development, support startups and innovation hubs, and foster collaboration between the public and private sectors. The concept paper examines successful AI adoption models from other countries, drawing lessons that can be tailored to Nigeria's specific needs. It emphasizes the significance of creating a conducive environment for AI innovation, which includes investing in digital infrastructure, enhancing internet connectivity, and ensuring access to high-quality data. Moreover, it proposes the establishment of AI regulatory bodies to oversee the development and deployment of AI technologies, ensuring they align with national priorities and ethical standards. Addressing the practical challenges of AI adoption, the paper highlights issues such as the digital divide, lack of skilled workforce, and potential job displacement. It proposes strategies to overcome these challenges, including implementing educational reforms to incorporate AI and digital literacy into the curriculum, providing incentives for continuous professional development, and promoting AI awareness and literacy among the general population. The anticipated outcomes of implementing robust AI policy frameworks include improved efficiency and productivity across various sectors, enhanced service delivery, and the creation of new economic opportunities. These outcomes are expected to drive sustainable economic growth, improve the quality of life, and position Nigeria as a competitive player in the global AI landscape. The paper provides a comprehensive roadmap for integrating AI into the national development agenda. By establishing robust policies, investing in infrastructure, and fostering a culture of innovation, Nigeria can successfully leverage AI to achieve significant socio-economic progress. The paper calls for a collaborative effort from government, industry stakeholders, academia, and civil society to create an enabling environment for AI adoption and growth.

The integration of artificial intelligence (AI) and advanced technologies in educational administration presents a transformative approach to enhancing both efficiency and educational quality. This abstract explores the critical role of AI and technology in streamlining administrative processes, optimizing resource allocation, and personalizing learning experiences to better meet the needs of students and educators. AI-powered systems facilitate data-driven decision-making, enabling administrators to efficiently manage student records, track academic progress, and identify at-risk students. Predictive analytics can forecast enrollment trends, helping institutions plan more effectively and allocate resources where they are most needed. Additionally, AI-driven scheduling and resource management systems optimize the utilization of facilities and staff, reducing operational costs and improving overall efficiency. One of the most significant impacts of integrating AI and technology in educational administration is the potential to enhance educational quality. Personalized learning platforms, powered by AI, adapt to individual student needs, providing customized educational content and support. These platforms can identify learning gaps and recommend targeted interventions, thereby improving student outcomes. Furthermore, AI can facilitate more effective communication between teachers, students, and parents through automated notifications and real-time updates on student performance. Moreover, AI and technology integration support administrative staff by automating routine tasks such as attendance tracking, grading, and report generation. This automation frees up time for educators and administrators to focus on more strategic and impactful activities, such as curriculum development and student engagement. Additionally, AI-driven analytics provide valuable insights into teaching effectiveness and student satisfaction, guiding continuous improvement efforts. However, the adoption of AI and technology in educational administration is not without challenges. Issues such as data privacy, cybersecurity, and the potential for algorithmic bias must be carefully managed to ensure ethical and equitable use. Institutions must also invest in training and support for staff to effectively utilize these advanced tools. In conclusion, the integration of AI and technology in educational administration holds immense potential to improve efficiency and educational quality. By leveraging these advanced tools, educational institutions can create more responsive, personalized, and effective learning environments, ultimately enhancing student success and institutional performance. Continued research and investment in this area are essential to fully realize the benefits and address the associated challenges.

Purpose: The integration of artificial intelligence (AI) and ICT technologies in veterinary units offers new opportunities for diagnostics, remote monitoring, and practice management. The aim of this article is to conduct a systematic literature review to analyze the applications, challenges, and future prospects of artificial intelligence (AI) and information and communication technologies (ICT) in veterinary industry Ipsum is simply dummy text of the printing and typesetting industry. Need for the study: Despite AI’s growing role in veterinary care, challenges such as algorithmic bias, regulatory concerns, and limited research on long-term impacts persist. Additionally, the adoption of AI in practice management remains underexplored, despite its potential to improve efficiency, automate workflows, and optimize resource allocation. A systematic review of existing literature is necessary to address these gaps. Methodology: This study conducts a systematic literature review of AI applications in veterinary medicine using Scopus, Web of Science, and Google Scholar. The analysis explores key themes, including diagnostics, telemedicine, data management, business efficiency, and regulatory challenges. Findings: AI enhances diagnostic accuracy, workflow automation, and predictive analytics, improving clinical decision-making and patient outcomes. In veterinary practice management, AI-driven automation optimizes scheduling, inventory control, and client communication. However, barriers such as technological resistance and regulatory uncertainty hinder widespread adoption. Emerging trends include interdisciplinary collaboration, blockchain for data security, and AI training in veterinary curricula. Practical Implications: AI adoption can transform veterinary practice management by enhancing efficiency, reducing administrative burdens, and improving profitability. Future research should explore long-term impacts, standardization, and client acceptance to ensure responsible and effective AI implementation in veterinary medicine. By addressing these challenges, AI and digital technologies can significantly advance both veterinary care and practice management, leading to improved patient outcomes and business performance.

Purpose This study aims to investigate the interplay between artificial intelligence (AI) integration, organizational digital culture, human resource management (HRM) practices and employee sustainable performance in luxury hotels in Malaysia. It seeks to elucidate how AI adoption influences organizational dynamics, shapes HRM practices and impacts employee sustainable performance over time. Design/methodology/approach Using a quantitative approach, survey questionnaires derived from prior research were utilized. Analysis using G*Power software determined an appropriate sample size, with psychometric evaluation validating scale development. Statistical analyses using Statistical Package for Social Sciences (SPSS) 28.0 and SmartPLS 4 confirmed data reliability and validity. Findings Out of the five hypotheses, three were supported. A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction. However, the relationship between AI adoption and organizational digital culture was not supported. On the other hand, HRM practices positively influenced employee sustainable performance. In addition, organizational digital culture was positively associated with employee sustainable performance, underscoring the role of digital fluency in driving workforce productivity. Conversely, AI failed to moderate the relationship between HRM practices and employee sustainable performance. Research limitations/implications The study’s focus on luxury hotels in Malaysia and its reliance on cross-sectional data, suggesting the need for longitudinal designs and diverse organizational contexts in future research. Comparative studies across sectors and countries could offer insights into variations in AI adoption practices and their impact on organizational performance. Originality/value This study contributes to theoretical frameworks by empirically examining complex relationships between AI integration, HRM practices, organizational digital culture and employee performance, emphasizing the importance of considering organizational context and cultural factors in understanding the implications of AI adoption for sustainable performance enhancement.

Many factors have led to recent breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML). Among these factors, Deep Neural Networks (DNN), activations functions, large volumes of collected data, and significant computational and storage resources. Despite these advances, innovation in AI still in its infancy and remains out of reach due to cybersecurity and data privacy issues, and difficulties to decentralize the machine learning processes among peer-to-peer networks to gain access to data and resources at the edge. Blockchain technology is gaining more attention beyond cryptocurrencies and Fintech. Distributed applications (DApps) with smart contracts are at the core of recent efforts to build decentralized Artificial Intelligence and Machine Learning applications. These are two different technologies but their integration advocates a paradigm shift of building secure, scalable and smart applications and services in decentralized and trustless environments.In this talk, we shed light on challenges and opportunities from AI/ML, blockchains 3.0 and cybersecurity perspectives and identify the need for fundamental research and new architectural design to build next generation of trustworthy AI/ML systems.

Purpose: The purpose of the study is to examine the challenges faced by businesses in integrating and effectively utilizing artificial intelligence (AI) technology. It aims to provide a comprehensive understanding of how AI technologies generate business value and the anticipated benefits they offer. The study also seeks to identify the facilitators and inhibitors of AI adoption and usage, explore different types of AI use in the organizational environment, and analyze their first- and second-order impacts.
 Methodology: The study employed the comprehensive literature review research design. The researchers conducted a systematic search using predefined criteria in databases such as Scopus and Web of Science. The search yielded 21 relevant papers that were analyzed and synthesized for this study. The data collection method relied on the examination of existing literature. Data analysis involved identifying key themes, trends, and insights from the selected papers. The researchers conducted a qualitative analysis to extract relevant findings and synthesized the information to derive meaningful conclusions.
 Findings: The study revealed several insights regarding the integration and use of AI in businesses. This indicated that organizations struggle with understanding how AI technologies can generate value and how to effectively incorporate them into their operations. Lack of comprehensive knowledge about AI and its value generation processes was identified as a major barrier. Additionally, the study highlighted the facilitators and inhibitors of AI adoption and usage. It identified various types of AI applications in the organizational environment and explored their impacts on business operations. The findings shed light on the challenges businesses face in leveraging AI technology and suggested areas for further research.
 Recommendations: To practitioners: The study emphasizes the importance of acquiring comprehensive knowledge about AI technologies and their potential value generation processes. To policy makers: The study highlights the need for supportive policies and regulations to foster AI adoption. It suggests creating an enabling environment that promotes AI research and development. Theory and Validation: The study may have been informed by existing theories related to AI adoption, organizational change, or innovation. Practice: To practitioners, the study underscores the importance of understanding the value and potential of AI technologies. Policy: To policy makers, the study emphasizes the need for policy frameworks that promote AI adoption and address associated challenges.

The integration of artificial intelligence (AI) into healthcare systems holds transformative potential to redefine healthcare delivery and accelerate progress toward Sustainable Development Goals (SDGs). However, the socioeconomic and ethical implications of AI adoption remain underexplored, particularly in real-world deployment contexts. This study aims to bridge this gap by examining AI’s impact on healthcare efficiency, cost savings, and workforce dynamics while addressing barriers to equitable access and ethical concerns such as data privacy, transparency, and algorithmic bias. A mixed-methods approach, including statistical modeling, economic analysis, and stakeholder interviews, was employed to evaluate AI’s potential to enhance healthcare delivery and mitigate disparities. Key findings demonstrate AI’s capacity to improve health outcomes, drive economic growth, and optimize resource allocation but underscore the need for robust governance frameworks to ensure ethical and inclusive AI adoption. These insights offer actionable recommendations for policymakers, healthcare professionals, and technology developers seeking to harness AI for sustainable healthcare transformation.