With the increase in global life expectancy and the advance of technology, the creation of age-friendly environments is a priority in the design of new products for elderly people healthcare. This paper presents a proposal for a real-time health monitoring system of older adults living in geriatric residences. This system was developed to help caregivers to have a better control in monitoring the health of their patients and have closer communication with their patients’ family members. To validate the feasibility and effectiveness of this proposal, a prototype was built, using a biometric bracelet connected to a mobile application, which allows real-time visualization of all the information generated by the sensors (heart rate, body temperature, and blood oxygenation) in the bracelet. Using these data, caregivers can make decisions about the health status of their patients. The evaluation found that the users perceived the system to be easy to learn and use, providing initial evidence that our proposal could improve the quality of the adult’s healthcare.

This systematic review examines how machine learning (ML) and generative AI (GenAI) have been integrated into learning analytics (LA) in higher education (2018–2025). Following PRISMA 2020, we screened 9590 records and included 101 English-language, peer-reviewed empirical studies that applied ML or GenAI within LA contexts. Records came from 12 databases (last search 15 March 2025), and the results were synthesized via thematic clustering. ML approaches dominate LA tasks, such as engagement prediction, dropout-risk modelling, and academic-performance forecasting, whereas GenAI—mainly transformer models like GPT-4 and BERT—is emerging in real-time feedback, adaptive learning, and sentiment analysis. Studies spanned world regions. Most ML papers (n = 75) examined engagement or dropout, while GenAI papers (n = 26) focused on adaptive feedback and sentiment analysis. No formal risk-of-bias assessment was conducted due to heterogeneity. While ML methods are well-established, GenAI applications remain experimental and face challenges related to transparency, pedagogical grounding, and implementation feasibility. This review offers a comparative synthesis of paradigms and outlines future directions for responsible, inclusive, theory-informed AI use in education.

Blockchain is one of the latest technologies attracting increasing attention from different actors in diverse fields, including the educational sector. The objective of this study is to offer an overview of the current state of the art related to blockchain in education that may serve as a reference for future initiatives in this field. For this, a systematic review of reference journals was carried out. Eleven databases were systematically searched and eligible papers that focused on blockchain in education that made significant contributions, and not only generic statements about the topic, were selected. As a result, 28 articles were analyzed. Lack of precision, and selection and analysis bias were then minimized by involving three researchers. The analysis of the selected papers provided invaluable insight and answered the research questions posed about the current state of the application of blockchain in education, about which of its characteristics can benefit this sector, and about the challenges that must be addressed. Blockchain may become a relevant technology in the educational field, and therefore many proofs of concept are being developed. However, there are still some relevant technological, regulatory and academic issues to be addressed to pave the way for the mainstream adoption of this technology.

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%.

The balance between game difficulty and player skill in the evolving landscape of the video game industry is a significant factor in player engagement. This study introduces a deep learning (DL) approach to enhance gameplay by dynamically adjusting game difficulty based on a player’s skill level. Our methodology aims to prevent player disengagement, which can occur if the game difficulty significantly exceeds or falls short of the player’s skill level. Our evaluation indicates that such dynamic adjustment leads to improved gameplay and increased player involvement, with 90% of the players reporting high game enjoyment and immersion levels.

Blockchain technologies are awakening in recent years the interest of different actors in various sectors and, among them, the education field, which is studying the application of these technologies to improve information traceability, accountability, and integrity, while guaranteeing its privacy, transparency, robustness, trustworthiness, and authenticity. Different interesting proposals and projects were launched and are currently being developed. Nevertheless, there are still issues not adequately addressed, such as scalability, privacy, and compliance with international regulations such as the General Data Protection Regulation in Europe. This paper analyzes the application of blockchain technologies and related challenges to issue and verify educational data and proposes an innovative solution to tackle them. The proposed model supports the issuance, storage, and verification of different types of academic information, both formal and informal, and complies with applicable regulations, protecting the privacy of users’ personal data. This proposal also addresses the scalability challenges and paves the way for a global academic certification system.