The aim of this study is to critically examine the transformation of EU industrial policy, focusing on the shift from a predominantly market-oriented governance model to one characterized by strategic interdependence. We ask how this evolution influences the EU's ability to manage dependencies, secure critical inputs, and enhance its productive capabilities in an increasingly interconnected global economy. To achieve this, we employed a mixed-methods approach, combining qualitative analysis of EU policy documents and quantitative assessments of industrial performance metrics. Our findings indicate that the integration of traditional principles, such as the Single Market and competition, with strategic measures is vital for fostering resilience and competitiveness. We identify key challenges and opportunities that arise from this new approach, emphasizing the need for a balanced strategy that promotes both openness and strategic management. Overall, we conclude that the future of EU industrial policy will hinge on its capacity to navigate geopolitical complexities while ensuring sustainable growth and innovation within its industrial sectors. The study further examines how emerging technologies, skills, infrastructure and industrial ecosystems will shape Europe's future productive capacity. It considers whether EU intervention should focus on market-making, market-shaping, or a combination of both. Particular attention is given to the allocation of public resources according to their contribution to productivity, social welfare, strategic resilience and citizen utility per euro spent. We also assess the potential benefits and risks of a more integrated industrial governance structure, including the possibility of a strategic budget line under DG Industry linking people, organisations and technology. Finally, the study argues that European industrial policy should focus less on protecting existing firms and more on creating the conditions in which globally competitive industries and high-quality employment can emerge.

Climate change represents one of the defining challenges of the twenty-first century, with increasingly interconnected consequences for human wellbeing, economic security, social equity, ecosystems, and sustainable development. While education is widely recognised as essential for developing climate awareness and environmental responsibility, its potential as a strategic agent of climate mitigation, adaptation, and resilience remains insufficiently embedded within education policy and wider climate-governance frameworks. This paper argues for a fundamental repositioning of education from a predominantly knowledge-transmission function to an active component of climate action. It examines how education policy can strengthen climate literacy, influence sustainable behaviour, develop green skills, support community preparedness, promote climate justice, and enable learners to participate meaningfully in the transition towards low-carbon and climate-resilient societies. Particular attention is given to the interconnected impacts of climate change on people, economies, and the environment, and to the need for policy coherence across education, climate action, economic development, disaster-risk reduction, and environmental sustainability. The paper further argues that effective climate-responsive education requires curriculum transformation, strengthened teacher capability, interdisciplinary learning, community engagement, locally relevant knowledge, and meaningful integration of Indigenous and traditional ecological knowledge alongside scientific and technological approaches. These priorities are particularly significant for climate-vulnerable communities and Small Island Developing States, where environmental disruption, economic vulnerability, displacement risks, and inequalities can intensify the consequences of climate change. The paper proposes a policy-oriented framework that conceptualises education as a pathway connecting climate knowledge with agency, behavioural transformation, mitigation, adaptation, resilience, and sustainable development. It concludes that education should no longer be regarded merely as a sector that must adapt to climate change; rather, it should be recognised and strategically mobilised as a transformative climate-action institution capable of empowering people, strengthening economies, protecting environments, and contributing to more equitable and resilient futures.

The future has always been uncertain, but the accelerating convergence of artificial intelligence (AI), automation, biotechnology, climate change, geopolitical instability, demographic transformation, and the Fifth Industrial Revolution (5IR) has intensified the limits of prediction. Contemporary education discourse frequently emphasises future-ready skills, workforce preparedness, and the competencies that learners will supposedly require for emerging economies and occupations. However, such approaches risk positioning education around assumptions about a future that cannot be known with certainty. This conceptual paper critically examines the fundamental question: How can education prepare learners for a future that cannot be reliably predicted? Drawing on contemporary scholarship on futures literacy, uncertainty, human agency, lifelong learning, resilience, critical pedagogy, and human-centred education, the paper argues that the purpose of future-oriented education should move beyond forecasting specific technologies, occupations, and competencies towards cultivating the capacity to navigate, interpret, question, adapt to, and actively shape uncertain futures. The paper proposes a conceptual shift from future prediction to future preparedness, emphasising adaptability, critical thinking, creativity, ethical reasoning, collaboration, empathy, resilience, digital and AI literacy, and lifelong learning. Importantly, the paper distinguishes between preparing learners for a particular future and preparing them for uncertainty itself. It further argues that human agency must remain central in an increasingly AI-mediated educational landscape, where technological capability alone cannot determine desirable educational or societal futures. A contextual perspective is also advanced, highlighting the importance of cultural knowledge, Indigenous perspectives, relationality, and community resilience in developing meaningful approaches to uncertainty, particularly within small island developing states and Pacific education systems. The paper concludes that education cannot—and should not attempt to—predict the future with certainty. Rather, its enduring responsibility is to develop learners who possess the intellectual, ethical, social, and adaptive capacities to engage intelligently and confidently with futures that remain unknown. The paper consequently reframes future readiness not as the ability to know what comes next, but as the capacity to respond meaningfully to whatever comes next.

The growing use of generative AI writing assistants, such as ChatGPT, in ESL/EFL academic contexts has sparked debate about their influence on self-regulated learning (SRL). This systematic review examines 41 empirical studies published between 2021 and March 2026 to evaluate how university students engage with AI tools in relation to SRL. Guided by PRISMA 2020 standards, the review synthesises quantitative, qualitative, and mixed methods research through narrative analysis, drawing on Zimmerman’s (2000) three phase SRL model and metacognitive regulation theory. Three central patterns emerge. First, SRL phases are unevenly addressed: the performance phase dominates (92.7% of studies), while forethought (58.5%) and self-reflection (51.2%) receive less attention, suggesting an incomplete regulatory cycle. Second, AI demonstrates a dual role in metacognitive regulation, facilitating monitoring, evaluation, and revision (68.3%), yet also contributing to cognitive offloading and reduced learner agency (14.6%), with 17.1% of studies reporting mixed outcomes. Third, pedagogical design is critical: structured approaches that integrate AI literacy, guided feedback, and teacher mediation consistently enhance SRL, whereas unguided use yields weaker results. The evidence base remains limited by its concentration in Asian contexts (73.2%), reliance on self-report data, and absence of longitudinal designs, restricting claims about sustained competence. Overall, the review concludes that generative AI can strengthen self-regulated academic writing when embedded in intentional pedagogical frameworks that activate all SRL phases; without such alignment, AI may improve immediate performance but hinder the development of lasting regulatory skills.

Artificial Intelligence (AI) has increasingly become embedded in the everyday life of contemporary communities, influencing communication, education, healthcare, employment, public services, cultural participation, and decision-making. While AI systems offer significant opportunities for improving efficiency, accessibility, personalization, and human capabilities, their growing presence also raises fundamental questions concerning human values. Issues related to dignity, autonomy, privacy, fairness, transparency, accountability, inclusion, trust, and cultural diversity have become increasingly important as intelligent technologies interact with individuals and communities.

This review paper examines the relationship between artificial intelligence and human values within contemporary communities. The study focuses particularly on how AI systems influence community relationships, social participation, individual autonomy, cultural practices, trust, and collective decision-making. The paper adopts a qualitative conceptual review methodology based on the analysis and synthesis of relevant scholarly literature, international ethical frameworks, and contemporary research on human-centered and community-centered artificial intelligence. Particular attention is given to human-centered AI, community participation, value alignment, responsible AI governance, and the social consequences of intelligent technologies.

The analysis indicates that AI cannot be understood solely as a technical system because its design, deployment, and outcomes are closely connected with human institutions, cultural contexts, social relationships, and community expectations. Recent scholarship on community-centered AI emphasizes the importance of identifying stakeholder and community values rather than assuming that a universal understanding of human values can adequately represent diverse populations. Community participation can therefore contribute to more context-sensitive and socially legitimate AI systems. Taylor, O’Dell, and Murphy, for example, argue for community-oriented approaches that explicitly incorporate stakeholder values into AI development. Similarly, research on community-centred norm elicitation demonstrates the importance of engaging communities in questions of AI value alignment.

The review further finds that responsible AI requires more than technical accuracy. AI systems operating within communities should respect human dignity, protect privacy, reduce discriminatory outcomes, provide appropriate transparency, preserve meaningful human oversight, and support inclusive participation. International frameworks such as UNESCO’s Recommendation on the Ethics of Artificial Intelligence and the OECD AI Principles reinforce these values by placing human rights, fairness, transparency, safety, accountability, and human agency at the centre of responsible AI.

The paper concludes that the future relationship between AI and contemporary communities should be based on a human- and community-centred approach. AI should complement human capabilities rather than unnecessarily replace human judgment or weaken social relationships. Meaningful participation of communities, interdisciplinary collaboration, ethical governance, cultural sensitivity, and continuous evaluation are therefore essential for ensuring that AI contributes positively to contemporary social life.