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.

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.

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.

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.