Digital transformation of higher education institutions (HEIs) is of paramount importance in today’s technology-driven world. However, there are numerous barriers that hinder the successful implementation of digital transformation initiatives in HEIs. In this article, we identify and classify these barriers based on a systematic literature review. The findings reveal twenty distinct barriers that inhibit digital transformation in higher education. These were then organised into six broad categories, namely environmental, strategic, organisational, technological, people-related and cultural. The study provides a comprehensive understanding of the barriers faced, facilitating the development of effective strategies and interventions. Our analysis provides valuable information for higher education institutions, policy makers and stakeholders involved in digital transformation initiatives.

This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence on the impact of these systems on student learning, and 3) identify current trends and open research questions in the field. After systematically searching online bibliographic databases, 105 articles were included in the review with 70 of them reporting concrete evaluation data on the learning impact of AICLS systems. Systems design analysis led us to propose a classification scheme with five dimensions: pedagogical objective, target of adaptation, modeling, technology, and design space. The reviewed articles indicate that AICLS systems increasingly introduce Artificial Intelligence and Web 2.0 techniques to support pretask interventions, in-task peer interactions, and learning domain-specific activities. Findings also suggest that AICLS systems may improve both learners' domain knowledge and collaboration skills. However, these benefits are subject to the learning design and the capability of AICLS to adapt and intervene in an unobtrusive way. Finally, providing peer interaction support seems to motivate students and improve collaboration and learning.

Digital transformation has emerged as a pervasive force in today’s rapidly evolving world, touching almost every sector and industry. In the higher education sector, in which education, research, and knowledge dissemination hold paramount significance for society, the notion of digital transformation per se becomes particularly important. This paper focuses specifically on higher education, acknowledging that it exists in a broader environment. In this work, qualitative methods were applied to explore the meaning and scope of digital transformation in higher education institutions. The systematic literature review formed the basis for in-depth interviews with experts in the discipline to explore the meaning and scope of digital transformation in higher education institutions. Then, for the purpose of preparing the case study, the concepts that emerged from the systematic literature review and interviews with experts were grouped together through a keyword-based pattern. Lastly, a case study was conducted at a Greek university with the aim of better understanding the way in which university members of a particular higher education institution perceive the meaning and scope of digital transformation. Our analysis provides valuable insights into higher education institutions for decision-makers and those involved in digital transformation initiatives, offering a comprehensive definition of digital transformation in higher education institutions. Finally, this paper can also serve as a basis for researchers who are interested in defining the meaning and scope of digital transformation in their own research fields.

As Large Language Models (LLMs) are core components in Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, concerns regarding hallucinations, redundancy, and unverifiable outputs have intensified, particularly in high-stakes domains, such as e-government. This study proposes a modular, multi-pipeline framework for statement-level faithfulness evaluation for characterizing hallucination and redundancy across both simple and agentic RAG pipelines. Using GPT-4.1, Claude Sonnet-4.0, and Gemini 2.5 Pro as LLM-based judges, this study examines how tool-specific attribution within agentic multi-tool architectures influences the interpretability and traceability of the generated content. By using a modular agentic RAG framework combining symbolic (GraphRAG), semantic (embedding), and real-time (web) retrieval, we benchmark hallucination and redundancy patterns, using state-of-the-art LLM judges. The study examines RAG and agent-based pipelines that attribute outputs to distinct tools, in contrast to traditional single-source RAG systems that rely on aggregated retrieval. Using e-government data sourced from the European Commission’s Press Corner, our evaluation framework assesses not only the frequency, but also the source-aware detectability of hallucinated content. The findings provide actionable insights into how source granularity and retrieval orchestration impact faithfulness evaluation across different pipeline architectures, while also suggesting new directions for explainability-aware RAG design. The study contributes a reproducible, modular framework for automated faithfulness assessment, with implications for transparency, governance compliance, and trustworthy AI deployment.

The profound digitization of public administration over recent decades has not eliminated information exchange via paper or electronic documents and certificates. We argue that a paradigm shift from document-centric to data-centric public service provision is needed and is feasible today with the exploitation of emerging technologies. We explore frameworks, architectures, benefits, and challenges in transforming document-centric administration processes into integrated, granular data exchange. A conceptual architecture for public service provision is proposed to extract preconditions from legislation, map the needed evidence to the service requirements, standardize evidence types, and integrate authoritative data sources. While promoting efficiency, privacy, and innovation, this shift faces technical and organizational challenges as the analysis of the “National Registry of Administrative Public Services” in Greece reveals. Further research on aligning policies, upholding trust, and coordinating institutional processes is warranted.

During the last decades, public authorities worldwide have invested heavily in electronic public services (PSs). This includes efforts for publishing information about PSs and for providing online PSs. Each of these efforts is based on an underlying PS Model, i.e. a data model developed for describing and/or developing PSs. Although many PS models exist, none has been universally accepted. This resulted in a fragmented landscape where resources are wasted, economies of scale cannot be achieved, and interoperability is hampered. Recently, the European Union (EU) launched the Core Public Service Vocabulary-Application Profile (CPSV-AP) as a reference PS model. However, CPSV-AP has not been thoroughly evaluated yet for comprehensiveness and compliance with EU policies. The aim of this work is to identify and analyze in a systematic way existing PS models and compare them with CPSV-AP. We conclude that CPSV-AP is comprehensive but can be further enriched with concepts from other PS models. We also provide evidence that the proposed enriched CPSV-AP better supports EU policies. Finally, we present a proof-of-concept pilot of the enriched model using linked data to demonstrate its technical feasibility and added value. Researchers, policy makers and practitioners could use the enriched model for conceptualizing and implementing electronic PSs.