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This study examines the potential applications of large language models (LLMs) and intelligent agents in educational environments, with a particular focus on their role in enhancing the quality of teaching and learning processes. It provides a comprehensive overview of LLMs, emphasizing their capabilities in natural language analysis and generation. Furthermore, the study examines the potential for collaboration between LLMs and intelligent agents. While LLMs offer a foundation for AI capabilities, intelligent agents utilize these technologies to perform autonomous and context-aware actions within educational systems. A comparative analysis of various intelligent agent platforms, including Autogen, Langra, Crew AI, LM Studio, and Olama, constitutes a central component of this research. This study addresses the criteria that informed the selection of Crew AI for a case study, with a particular focus on its adaptability, ease of integration, and task execution capabilities in comparison to the other platforms. The research includes an analysis of the platform's performance in a controlled educational environment, highlighting the advantages of Crew AI in system functionality. These results demonstrate the necessity for a strategic and well-structured approach to integrating LLMs and intelligent agents, as their successful implementation can foster new competencies, enhance stakeholder engagement, and offer innovative teaching and learning experiences.

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Higher Education Institutions (HEIs) face increasing pressure to transform fragmented information environments into cohesive, data-driven ecosystems that support strategic and operational decision-making. This study proposes a comprehensive framework for implementing Business Intelligence (BI) in HEIs, evolving from a validated roadmap to an integrated ecosystem perspective. Grounded in the Design Science Research methodology, the work combines a systematic literature review, the design of a flexible BI architecture, and an in-depth case study at the University of Tr & aacute;s-os-Montes and Alto Douro (UTAD). The framework addresses critical factors such as strategic alignment, data governance, and system interoperability, and demonstrates how dashboards and analytics can enhance institutional intelligence and evidence-based management. Results from the UTAD case confirm the framework's capacity to overcome technical and organisational barriers, enabling the transition from isolated systems to intelligent, interconnected data infrastructures. This research contributes to the literature by bridging theoretical guidelines and practical implementation, providing a scalable reference model to guide BI-driven digital transformation in higher education. It also demonstrates the tangible institutional value of integrated BI ecosystems in supporting more informed, timely, and efficient decision-making.

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Motivation The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practical considerations and by ethico-legal and data governance restrictions. Gaining access to such data can be time-consuming due to the governance requirements associated with data access requests to institutions in different jurisdictions.Results DataSHIELD is a software solution that enables remote analysis without the need for data transfer (federated analysis). DataSHIELD is a scientifically mature, open-source data access and analysis platform aligned with the 'Five Safes' framework, the international framework governing safe research access to data. It allows real-time analysis while mitigating disclosure risk through an active multi-layer system of disclosure-preventing mechanisms. This combination of real-time remote statistical analysis, disclosure prevention mechanisms, and federation capabilities makes DataSHIELD a solution for addressing many of the technical and regulatory challenges in performing the large-scale statistical analysis of health and biomedical data. This paper describes the key components that comprise the disclosure protection system of DataSHIELD. These broadly fall into three classes: (i) system protection elements, (ii) analysis protection elements, and (iii) governance protection elements.Availability and implementation Information about the DataSHIELD software is available in https://datashield.org/ and https://github.com/datashield.

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