Abstract Smart classrooms leverage advanced technologies to create interactive, data-driven learning environments that enhance educational experiences. However, interoperability remains a significant challenge, as many of these systems operate independently. This paper presents the Smart Classroom Ontology (SClassO), the very first holistic model designed to unify the key components of smart classroom environments, including people, context, and resources. By providing a semantic framework, SClassO enables seamless integration across heterogeneous systems, bridging gaps between sensing devices, learning management platforms, and classroom analytics. A proof of concept implementation demonstrates its ability to collect and integrate real-world data from both sensor devices and information systems using standard protocols such as MQTT and RESTful APIs. Data is stored both in OWL/RDF format and in a relational database to evaluate performance trade-offs. Finally, the study explores key architectural challenges, focusing on storage capacity, system performance, security, and data privacy.

With the technological developments of recent years, the concept of the “Smart Classroom” has gained in importance and is increasingly becoming a plausible reality. This refers to a classroom in which real-time data collection facilitates decision-making to improve teaching and learning processes. This article presents a proposal for the smart classroom infrastructure that utilises based on the Internet of Things and Cognitive and Intelligent Systems. It consists of a number of perception units that focus on monitoring environmental variables such as light, air quality and noise, as well as variables related to the actors involved (students and teachers), especially those about emotions (which play a crucial role in educational processes). The information extracted from the data is stored and analysed by the classroom agent, which provides teachers with relevant information and suggestions in a timely manner. It is important to further develop our smart classroom infrastructure and test it in real environments to evaluate its potential and benefits in terms of the teaching and learning process, as well as to identify possible drawbacks and obstacles to implementation.

Abstract As society experiences accelerated ageing, understanding the complex biological processes of human ageing, which are affected by a large number of variables and factors, becomes increasingly crucial. Artificial intelligence (AI) presents a promising avenue for ageing research, offering the ability to detect patterns, make accurate predictions, and extract valuable insights from large volumes of complex, heterogeneous data. As ageing research increasingly leverages AI techniques, we present a timely systematic literature review to explore the current state-of-the-art in this field following a rigorous and transparent review methodology. As a result, a total of 77 articles have been identified, summarised, and categorised based on their characteristics. AI techniques, such as machine learning and deep learning, have been extensively used to analyse diverse datasets, comprising imaging, genetic, behavioural, and contextual data. Findings showcase the potential of AI in predicting age-related outcomes, developing ageing biomarkers, and determining factors associated with healthy ageing. However, challenges related to data quality, interpretability of AI models, and privacy and ethical considerations have also been identified. Despite the advancements, novel approaches suggest that there is still room for improvement to provide personalised AI-driven healthcare services and promote active ageing initiatives with the ultimate goal of enhancing the quality of life and well-being of older adults. Graphical abstract Overview of the literature review.

The proper exploitation of vast amounts of event data by means of process mining techniques enables the discovery, monitoring and improvement of business processes, allowing organizations to develop more efficient business intelligence systems. However, event data often contain personal and/or confidential information that, unless properly managed, may jeopardize people’s privacy while conducting process mining analysis. Despite its relevance, privacy aspects have barely been considered within process mining, and the field of privacy-preserving process mining is still in an embryonic stage. With the aim to protect people’s privacy, this article presents a novel privacy-preserving process mining method based on microaggregation techniques, called k-PPPM, that increases privacy in process mining through k-anonymity. Contrary to current solutions, mostly based on pseudonyms and encryption, this method averts the re-identification of targeted individuals from attacks based on the analysis of process models in combination with location-oriented attacks, such as Restricted Space Identification and Object Identification attacks. The proposed method provides adjustable parameters to tune different anonymization aspects. Six real-life event logs have been employed to evaluate the method in terms of process models quality and information loss.