This study provides a zoom-out perspective of higher education students’ experiences related to the emergency remote learning (ERL) following the first lockdown due to the COVID-19 pandemic as captured by a national, in-depth survey administered to all higher education institutions in Cyprus (different fields of study and educational levels). Quantitative and qualitative analyses of the data collected from 1051 students provide valuable information and insights regarding learners’ prior technology background and level of preparedness for online learning, the challenges and benefits of ERL and how they would like their online learning experience to be improved in case of future ERL. The results underline that students’ knowledge of and self-efficacy in using e-learning tools do not directly equate to being a digital learner equipped with necessary digital skills such as self-regulation to fully benefit from online learning. The educational disparities caused by inequalities in access and accessibility to high-quality education laid bare by the pandemic stressed the need for online environments that would afford quality learning for all learners. Online learning demands are discussed in the article, as well as implications for research, practice and policy making.

Purpose: This study investigates how artificial intelligence (AI) integrates into school leadership by examining organisational benefits and ethical challenges. As AI permeates educational administration, school leaders must navigate risks and opportunities in data privacy, fairness, and accountability. Design/methodology/approach: A PRISMA-aligned structured literature review was conducted on publications from 2019 to 2025. Searches were performed in Scopus, Web of Science, ERIC, and Google Scholar, focussing on K–12 school leadership, with selective higher education sources included only for transferable governance mechanisms (e.g. policy, procurement, documentation/explainability, and auditability). Studies were screened for leadership relevance and ethical-legal engagement. Findings were synthesised using reflexive thematic analysis and conceptual mapping, yielding a final corpus of 50 publications. Findings: AI affords benefits for school leadership, including administrative efficiency, decision support and, under data governance, more equitable resource allocation. However, adoption introduces ethical-legal challenges. Key concerns include algorithmic bias, opacity in decision-making, and diffuse accountability. Many systems lack robust oversight, clear roles, and targeted training for ethical implementation. Practical implications: School leaders should embed AI in distributed leadership, mandate explainability and audits in procurement, invest in privacy/data literacy, and align analytics with instructional priorities to secure equity, lawful processing, and reviewable accountability. A one-page governance map (Leaders' Governance Guide) is provided, mapping use cases to risks, safeguards, and an equity note. Originality/value: The review links benefits and risks to accountability and legal implications, proposing a leadership governance frame to support equitable, transparent AI in schools.

In education, e-learning is highly adopted to improve the learning experience and increase learning efficiency and engagement. Yet, an explosion of online learning materials has overwhelmed learners, especially when trying to achieve their learning goals. In this scope, recommender systems are used to guide learners in their learning process by filtering out the available resources to best match their needs, i.e. to offer personalized content and learning paths. Concurrently, process mining has emerged as a valuable tool for comprehending learner behavior during the learning journey. To synergize these disciplines and optimize learning outcomes, our paper introduces an ontology-based framework that aims to recommend an adaptive learning path, driven by a learner’s learning objective, personalized to his learning style, and enriched by the past learning experience of other learners extracted via process mining. The learning path considers pedagogical standards by employing Bloom’s taxonomy within its structure. The framework establishes an Ontological Foundation, to model the Learner, Domain, and Learning Path. Choosing Computer Science as a domain, we construct a knowledge base using synthesized data. For past learning experience, we analyze Moodle log data from 2018 to 2022, encompassing 471 students in the Computer Science and Engineering Department at Frederick University, Cyprus.

The case of the Robotics Academy @ Frederick University: 21st Century Skills Developed through a Non-formal Educational Setting