Volume 4 Issue 2: September 2025

As educational policies increasingly advocate integrating generative artificial intelligence (AI) into teaching practices, important questions arise about its effect on students’ cognitive development. By observing how students use large language models (LLMs), we can see their potential to disrupt traditional learning frameworks, such as Bloom’s Taxonomy. This article explores how AI influences students’ work habits, knowledge acquisition, and cognitive skills based on two years of observations of 70 first‑year computer science students in an 180‑hour programming course. The results suggest that generative AI could undermine the hierarchical structure of Bloom’s Taxonomy by enabling students to bypass essential cognitive processes, such as comprehension, application, and analysis. This allows them to replace their personal efforts with AI‑generated results. These findings raise concerns about the erosion of critical thinking and problem‑solving skills, which could reshape educational goals established since the 1960s. Rather than taking a position for or against the use of AI, the article aims to stimulate debate about its long‑term implications for developing and managing students’ knowledge and skills. The article highlights the need for teachers and policymakers to address ethical challenges and strategies that ensure AI enhances, rather than replaces, cognitive engagement in learning. After an introduction, Chapter 2 provides an overview of Bloom’s taxonomy. Chapter 3 explains the principles of how LLMs work. The next chapter describes how students use LLMs based on behavioral observations. Before concluding with the importance of policy decisions to be made in the coming years, Chapter 6 discusses how LLMs can influence teaching methods.

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This study presents a systematic literature review on the implications of the use of generative artificial intelligence (GAI) in finance. With the rapid advancement of GAI technologies and hybrid adversarial‑variational frameworks, GAI’s integration into the financial industry has gained significant importance. Despite the growing body of research, comprehensive analyses of GAI’s potential applications, opportunities, and challenges in finance remain limited. The objective of our study is to synthesize the existing literature on the implications of GAI in finance and propose future research directions. The methodology involves a five‑step systematic literature review process, including identification, selection, relevance and quality assessment, data extraction, and data synthesis of relevant articles published between 2020 and 2025. The evaluation based on 42 selected articles highlights several applications of GAI in finance, which include synthetic data‑driven financial innovation, time‑series forecasting and algorithmic trading, risk modeling and stress testing, as well as GAI‑driven budgeting tools. Potential opportunities for GAI use in finance embrace enhanced operational efficiency, optimized customer service, innovation and sustainability capabilities, strengthened financial compliance, and improved data processing and analytical capabilities. Nevertheless, challenges such as technical risks, regulatory risks, ethics and moral concerns, market risks, operation and maintenance risks, and mental risks are also identified. Finally, we propose a research agenda focusing on both process‑related and content‑related recommendations to address these challenges and guide future research on the implications of GAI in finance.

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The increasing adoption of cloud computing (CC) has introduced significant security and privacy concerns, demanding intelligent and adaptive solutions. This review explores the application of machine learning (ML) algorithms—both supervised and unsupervised—in addressing these challenges within cloud environments. A total of 87 peer‑reviewed studies published between 2014 and 2025 were analyzed to assess the effectiveness of various ML techniques. Supervised Machine Learning (SML) algorithms such as Artificial Neural Networks (ANNs), Support Vector Machines (SVM), K‑Nearest Neighbors (K‑NN), Naive Bayes, and C4.5 Decision Trees are examined for their effectiveness in intrusion detection, anomaly classification, and threat mitigation. Concurrently, Unsupervised Machine Learning (UML) algorithms, including Unsupervised Neural Networks (UNNs), K‑Means clustering, and Singular Value Decomposition (SVD), are analyzed for their capacity to detect unknown threats and extract latent patterns from unlabeled data. Key trends reveal a growing preference for hybrid models, the superior accuracy of deep learning in anomaly detection, and the emerging use of context‑aware frameworks. The review shows a comparative analysis of these approaches, highlighting their advantages, limitations, and application scenarios in cloud security. Future research directions are proposed, emphasizing hybrid learning models, enhanced datasets, and context‑aware security frameworks. The findings underscore the transformative potential of ML in fortifying cloud infrastructures against evolving cyber threats.

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This research examines the development and evaluation of a wearable haptic device and a digital maze game designed for children with visual impairments. The study focused on addressing the specific needs of visually impaired users, employing open‑source hardware and software to maintain a low cost while achieving high functionality. The study involves the development of a prototype system and its evaluation through user testing with children with visual impairments. Qualitative and quantitative data were collected to assess usability, accessibility, and overall players’ experience, leading to significant recommendations for game improvement. Key findings highlight the potential for two‑player adaptation to enhance social interaction, the value of incorporating multiple difficulty levels and varied haptic feedback in maze design, and the critical role of customizable auditory elements in providing individual preferences and sensory understanding. Furthermore, the adaptability of the haptic device for application in various game scenarios beyond the maze, such as virtual boating or urban navigation, demonstrates the broad potential of haptic technology in creating accessible and engaging digital experiences. This research emphasizes the significance of user‑centered design principles and the strategic integration of multisensory feedback in creating inclusive and enjoyable digital games for visually impaired children.

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The paper ascertained the perspective of lecturers in improving instructional delivery using blended learning in College of Education in South‑South and South East Nigeria. Two specific purposes guided the study. They were further developed into two research questions and two hypotheses. The paper adopted a descriptive survey design. The population of the study comprised 425 business education lecturers in College of Education, in South‑South and South East Nigeria. The entire population of 425 lecturers was used as a sample for this study. The instrument for data collection was questionnaire and interview schedules. The instrument was validated and tested for internal consistency using Cronbach Alpha reliability coefficient. The coefficient obtained was 0.83. Out of 425 copies of the questionnaire administered, only 321 representing 93% was retrieved and used for data analysis. Mean and standard deviation was used to answer the research questions and t‑ test statistic to test the hypotheses at 0.05 level of significance. Results indicate that training of personnel and provision of facilities are necessary in using blended learning to improve instructional delivery. Also, gender was not significant in the perception of lecturers in both training needs and provision of facilities to improve instructional delivery using blended learning. It was concluded that effective training and provision of the right facilities to support blended learning will help to improve instructional delivery in tertiary institutions. It was recommended that adequate training should be given to educators to enable them to use blended learning in improving teaching and learning.

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The paper introduces a web‑based platform for conducting multiplayer common‑pool resource (CPR) games, replicating the experimental framework of Ahn et al. Built using technologies such as PHP, MySQL, HTML5, and JavaScript the platform facilitates remote and synchronous gameplay in structured groups with configurable settings. Players extract tokens from a shared resource over multiple rounds, with payoffs determined by token‑order pricing and optional costly punishment stages. The system supports dynamic group formation, randomized player order, and round‑based synchronization to ensure fairness. It includes real‑time coordination via AJAX polling; a responsive Bootstrap interface; administrative controls, and live monitoring through Grafana dashboards. Data integrity is maintained using server‑side validation, prepared S.Q.L. statements, and transactional database updates, with all actions logged in a normalized relational schema. The platform addresses technical challenges such as session timeouts, synchronization delays, and dropouts using heartbeat callbacks, waiting screens, and robust state checks. Designed for extensibility, it can support different game‑theoretic designs and is suitable for both lab and remote experiments. Future enhancements include WebSocket‑based real‑time communication and Docker‑based deployment for reproducibility. By offering a customizable, secure, and scalable CPR experiment environment with real‑time feedback and comprehensive logging, this platform serves as a valuable tool for advancing research in experimental economics, particularly in cooperation, punishment mechanism, and resource governance. The platform demonstrates reliable performance with low‑latency synchronization and minimal dropout‑related issues. Compared to tools like oTree and LIONESS Lab, it offers a more lightweight and modular alternative optimized for small‑scale deployment, remote teaching, and rapid experimental design.

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The current research aims to investigate the transformative impact of artificial intelligence (AI) on marketing. AI‑driven marketing strategies are widely utilized in practice but their influence on customers is not always clear, leaving a glaring knowledge gap. Drawing on the Technology Acceptance Model, the current research analyses how AI‑driven marketing strategies influence customer positioning and perception, with a particular emphasis on customer trust, engagement, and relevant ethical considerations. To maximize the ecological validity of data mining and analysis, we collect multi‑layered data from renowned academic platforms, industry reports and consultancy records, in line with the institutional ethical guidelines. Research findings are meaningful in three ways. To begin with, while AI‑driven marketing strategies offer promising tools for enhancing customer engagement, they also raise severe concerns related to transparency and ethics, such as threats surrounding data privacy, fairness, as well as the opacity of AI algorithms in implementation. Next, by comparing AI‑driven and non‑AI‑driven marketing strategies, we can uncover the conditions under which AI promotes trust and improves the customer experience. Finally, research findings have advanced knowledge by providing evidence‑based insights into the mechanisms through which AI affects customer perception. Overall, new research discoveries have benefited both entrepreneurs and marketing professionals, providing sensible insights to policy making (e.g., AI‑driven marketing strategies) and ethical practices (e.g., ethical standards and procedures in marketing). Research limitation and suggestions for future research are discussed.

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The swift advancement of global digital transformation has established smart cities as a prominent trend in modern urban planning. This research examines the essential role of social work in the development of smart cities, with a particular focus on the practical application of digital service platforms, exemplified by Taiwan’s “Smart Decision‑Making Action Platform for Social Workers.” Employing a mixed‑methods approach involving 762 social workers, this study addresses four fundamental questions: (1) in what ways can social work redefine its roles within the context of smart cities, (2) what are the specific impacts of digital technology integration on social work practices, (3) what successes and challenges are associated with current digital platforms, and (4) what strategies can be employed to develop integrated social work service systems for smart cities? The findings indicate that social workers play a vital bridging role within smart city ecosystems, resulting in a 35.7% increase in service efficiency and a 47.8% enhancement in practice safety. The integration of digital technology significantly improves working conditions, reduces risks, and enhances service quality while preserving a human‑centered approach. This study introduces the Smart City Social Work Integration Model (SCSWIM) and offers evidence‑based recommendations for policy formulation and the enhancement of professional practice in digitally‑enabled urban settings.

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Industry 4.0 marks a new phase of industrial transformation, driven by the integration of advanced technologies such as industrial robotics, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and cyber‑physical systems (CPS). The Republic of Korea and Singapore are global frontrunners in this domain, ranking first and second worldwide in robot density per 10,000 manufacturing workers. This paper explores how the strategic integration of robotics with key Industry 4.0 technologies contributes to smart manufacturing and enhanced industrial performance. Using a comparative case study approach, the research analyzes national policies, investments in R&D and education, 5G infrastructure, and support for innovation ecosystems that have enabled these countries to develop flexible, automated, and intelligent production systems. Findings indicate that both Korea and Singapore have successfully combined robotics with IoT, big data analytics, and cloud platforms to create efficient and adaptive manufacturing environments. The study emphasizes that robotization alone is not sufficient; its effectiveness depends on alignment with broader digital transformation strategies. Based on longitudinal data from 2013 to 2023, sourced from the International Federation of Robotics (IFR), the OECD, and national innovation agencies, the research highlights how coordinated implementation of Industry 4.0 technologies fosters sustainable and globally competitive manufacturing.

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The automotive industry is undergoing a profound transformation driven by electrification, digitalization, and evolving value chains. Small and medium‑sized enterprises (SMEs) in the supplier sector face particular challenges in adapting their business models to these changes. This study addresses this issue by developing the PIC approach, a structured process model consisting of three phases: Preparation, Implementation, and Control. Based on a systematic literature review, the model integrates theoretical insights with practical tools tailored to SMEs. A fictitious case study demonstrates the applicability of the approach and illustrates how companies can use it to assess their current state, define a strategic direction, and manage the transformation process. Continuous improvement is supported through the integration of continuous improvement process principles. While the PIC model is conceptual in nature, it offers a promising foundation for practical application. The objective of this approach is to support successful adaptation, helping users address challenges and mitigate risks arising from ongoing changes in the automotive sector. This work is carried out within the TraFoNetz project—a regional initiative in Germany’s Northern Black Forest that supports the transformation of SMEs in the automotive supply industry. Future research will focus on validating the approach in real‑world settings and refining it further.

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Physiotherapy has been a subject of research, targeting the facilitation of monitoring and remote assistance provided by the physiotherapist, whenever collocation of doctor and patient is not possible. This is known as tele‑physiotherapy, or tele‑rehabilitation. It has evolved significantly over the past few decades due to advancements in technology, but has also become a necessity during particular periods, for example, during the COVID‑19 pandemic. Initially, tele‑physiotherapy was mostly performed using video conferencing and mainstream telecommunication services; however, recent developments have seen the emergence of sophisticated tele‑physiotherapy platforms that integrate various technologies such as sensors, wearable devices, and digital health platforms. Moreover, the incorporation of Machine Learning (ML) and Artificial Intelligence (AI) enables real‑time analysis, treatment personalization, and the introduction of feedback mechanisms, thus improving the usability and efficiency of rehabilitation sessions. Tele‑physiotherapy solutions address the scope from a wide variety of aspects and demonstrate variable effectiveness. This review examines 19 studies and solutions and compares 14 of those proposals that offer a tele‑physiotherapy solution for patients. Although their services, complexity and functionality vary, the basic criterion for inclusion in the evaluation was that they offer a tele‑physiotherapy service as a part of their main scope. Some of those solutions are commercially available, some are at a research level, but all of them were primarily addressed from the aspect of usability.

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This study aimed to evaluate a relatively new dataset developed to facilitate the primary diagnosis of breast cancer, collected by the University Hospital Centre of Coimbra in Portugal. Based on these assessments, the authors sought to develop a clear visual classifier to assist medical professionals in prediction and monitoring. This classifier utilizes routine blood test results along with physical data, offering a more straightforward and cost‑effective alternative to traditional mammographic studies. The Coimbra Breast Cancer Dataset (CBCD) includes the following attributes: Age, Body Mass Index (BMI), Glucose, Insulin, Homeostatic Model Assessment for Insulin Resistance (HOMA‑IR), Leptin, Adiponectin, Resistin, and Monocyte Chemoattractant Protein‑1 (MCP1). The visual classifier was designed using Java‑based machine learning algorithms within the Java‑based WEKA software (version 3.9.6). Its well‑designed interface enables clinicians, even those without expertise in machine learning, to use these algorithms effectively. The nine attributes of the CBCD were statistically categorized into three subsets based on their relevance to the overall model. This organization may help reduce the dimensionality of the diagnostic dataset while allowing specific classifiers to exhibit their unique preferences. A properly tuned JRip classifier demonstrated acceptable performance with the entire dataset and was effective in reducing it to six or even four attributes. The primary advantage of this classifier lies in its decision rules, which are easy for medical professionals to interpret and apply.

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The accelerating expansion of digital infrastructure, including data centers, communication networks, and Artificial Intelligence (AI) systems, is transforming economies and societies worldwide. However, this digital transformation carries a growing environmental cost, particularly in terms of carbon emissions, resource consumption, and lifecycle waste. This literature review critically explores the environmental footprint of digital infrastructure, with a primary focus on carbon emissions across the stages of manufacturing, operation, and disposal. While studies from China dominate the empirical base due to its rapid digitalization, the review incorporates comparative evidence from Europe and North America to strengthen its global applicability. Key sources of emissions are identified in energy‑intensive operations such as data center cooling and AI model training. To mitigate these impacts, this study examines integrated strategies including renewable energy deployment, nanotechnology‑based cooling innovations, Environmental, Social, and Governance‑driven policy frameworks, and circular economy applications. A revised research framework is proposed to guide future investigation into sustainable digitalization. Moreover, this review emphasizes the importance of public participation in smart city governance, advocating for co‑created urban solutions, open data platforms, and inclusive digital planning. By embedding solution pathways throughout the discussion, the paper presents a cohesive analysis that bridges technological innovation with climate and environmental priorities. Ultimately, this concludes with recommendations for cross‑sectoral collaboration among governments, industries, and communities to ensure that digital progress aligns with long‑term sustainability goals.

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The present study develops a flexible and interactive decision‑support application for portfolio optimization, grounded in Modern Portfolio Theory and implemented within the Mathematica computational environment. The tool enables users to construct, analyze, and evaluate investment portfolios dynamically, incorporating real-time sensitivity analysis. In accordance with contemporary portfolio theory, it integrates two principal optimization strategies: (a) the Minimum Variance Portfolio and (b) the Maximum Sharpe Ratio Portfolio. The computational framework ingests real stock market data (Yahoo Finance), from which returns and covariance matrices are calculated. The resulting data serves as inputs for solving the corresponding optimization problems under user‑defined constraints. A key feature of the tool is the ability to perform real‑time sensitivity analysis with respect to expected returns, as well as to interactively adjust the risk‑aversion coefficient, providing users with immediate visual and numerical feedback. Interpretability is enhanced through graphical representations of the Efficient Frontier, overlaid with the optimal portfolios and the Capital Market Line on a unified plot. These visualizations support both educational and practical financial decision‑making. Overall, the tool offers a novel contribution by offering a hands‑on, visually rich, and analytically rigorous environment for understanding and applying portfolio optimization methods using real‑world data.

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