Abstract
Motifs are overrepresented and statistically significant sub-patterns in a network, whose identification is relevant to uncover its underlying functional units. Recently, its extraction has been performed on higher-order networks, but due to the complexity arising from polyadic interactions, and the similarity with known computationally hard problems, its practical application is limited. Our main contribution is a novel approach for hyper-subgraph census and higher-order motif discovery, allowing for motifs with sizes 3 or 4 to be found efficiently, in real-world scenarios. It is consistently an order of magnitude faster than a baseline state-of-art method, while using less memory and supporting a wider range of base algorithms.

Abstract
The education sector faces unprecedented challenges, from rapidly evolving technologies to diverse learner needs, placing immense pressure on educators to adapt and innovate. Traditional teaching methods need help to keep pace with the demands of modern education, leading to gaps in personalized learning and student engagement. Ethical concerns surrounding AI integration in education remain a significant hurdle, requiring careful navigation and responsible implementation. Integrating Artificial Intelligence in Education: Enhancing Teaching Practices for Future Learning offers a comprehensive solution by exploring how AI can address these challenges and revolutionize education. Through a collection of insightful contributions, it provides practical strategies for integrating AI into teaching practices, empowering educators to personalize learning experiences and enhance student engagement. By examining AI ethics and responsible education, the book equips educators with the knowledge needed to navigate the ethical complexities of AI integration. This book is a practical guide for educators, researchers, policymakers, and practitioners who want to harness the potential of AI in education. It provides a roadmap for leveraging AI technologies to create adaptive learning environments, automate classroom tasks, and enhance instructional design. With a strong focus on practical insights and ethical considerations, this book is a valuable resource for anyone looking to navigate the intersection of AI and education. © 2025 by IGI Global. All rights reserved.

Abstract
[No abstract available]

Abstract
Higher Education Institutions (HEIs) are increasingly incorporating artificial i ntelligence (AI) into their learning setup. In this paper, we analyze the results of a survey posed to 152 Higher Education (HE) students and 136 HE educators, of different scientific b ackgrounds, to emphasize the current incorporation of AI in the teaching and learning processes. The results reveal distinct viewpoints from both parties, reflecting diversified l evels o f e xperience, presumptions, and uneasiness. Thirty two percent of the teachers, completing the survey, confirms using AI. Approximately 50% reveal they notice their students using AI to (i) automate routine tasks in or out-ofclass, including check correctness of answers, obtaining real-time feedback; (ii) personalize learning tasks, such as write essays or projects and to illustrate them, and create presentations. A smaller percentage reveals students using AI to produce video content and contrast information learned in class. Alternative means, encompassing using AI at home, to study, to gather information, to sum up ideas in texts, are identified by most teachers as being employed by their students. Students using AI outnumber the teachers, though there are significant d ifferences in some responses, when compared to the teachers' perceptions, for the sames questions. Most of the students prefer AI to study at home, to obtain information to improve or to check an answer. Then a significant number does not exploit AI either to create presentations, write an essay or project, illustrate a project, producing videos, or to contrast information obtained in classes with that collected by AI tools. Regardless of these differences, both parties agree and strongly agree (with 79% of students and 86% of teachers) that AI will affect the HEIs educational process in the future. © 2024 IEEE.

Abstract
This study introduces the development of a client-based software layer within the FGPE project, aimed at enhancing the usability of the FGPE programming learning environment through client-side processing. The primary goal is to enable the evaluation of programming exercises and the application of gamification rules directly on the client-side, thereby facilitating offline functionality. This approach is particularly beneficial in regions with unreliable internet connectivity, as it allows continuous student interaction and feedback without the need for a constant server connection. The implementation promises to reduce server load significantly by shifting the evaluation workload to the client-side. This not only improves response times but also alleviates the burden on server resources, enhancing overall system efficiency. Two main strategies are explored: 1) caching the gamification service interface on the client-side, and 2) implementing a complete client-side gamification service that synchronizes with the server when online. Each approach is evaluated in terms of its impact on user experience, system performance, and potential security concerns. The findings suggest that while client-side processing offers considerable benefits in terms of scalability and user engagement, it also introduces challenges such as increased system complexity and potential data synchronization issues. The study concludes with recommendations for balancing these factors to optimize the design and implementation of client-based systems for educational environments.

Abstract
A growing concern with current teaching approaches underscores the need for innovative paradigms and tools in computer programming education, aiming to address disparate user profiles, enhance engagement, and cultivate deeper understanding among learners This article proposes an innovative approach to teaching programming, where students are challenged to write statements for solutions automatically generated. With this approach, rather than simply solving exercises, students are encouraged to develop code analysis and problem formulation skills. For this purpose, a Web application was developed to materialize these ideas, using the OpenAI API to generate exercises and evaluate statements written by the students. The transformation of this application in H5P and its integration in a LMS gamified workflow is explored for wider and more effective adoption. © Ricardo Queirós;