Machine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

With the increasing relevance of information and communication technologies (ICT) and the global transition towards the knowledge society, school systems and higher education now face increasing challenges involved in preparing students for successful participation in the knowledge society. In many countries, this is leading to manifold efforts and changes in national policies concerning ICT integration into education systems. This paper outlines four challenges concerning the design, implementation, and evaluation of national policy: (1) creating future ready policy, (2) achieving systemic perspectives in stakeholders, (3) promoting commitment to learning from policy, and (4) developing and implementing supportive processes. The paper shares an international perspective on these four major challenges and proposes strategy perspectives and solutions, as well as tactics that could be applied to advance a nation’s educational system empowered by technology.

Thissystematic quantitative literature review spans 2013 to 2023 and includes 40research articles that meet all eligibility criteria. It explores theintersection of virtual reality (VR) and history education, serving as ascoping of the field, discovering key contributors, publication trends,research types, geographic distribution, participant demographics, educationallevels, research methods and designs, key findings, and thematic relationshipsamong key concepts. The key findings indicate a surge in VR-relatedhistory/social studies research, with Europe leading in contributions, followedby Asia, North America, South America, Africa, and Oceania. Students were theprimary research participants in the studies included in the review, with themajority of the studies empirical research. The majority of studies focused onhigher education and were conducted utilising quantitative methods andexperimental research design. The findings reveal that VR significantlyenhances learning experiences, historical thinking skills, and positiveattitudes toward VR and the history subject.

As artificial intelligence (AI) becomes increasingly woven into educational design and decision-making, its use within initial teacher education (ITE) exposes deep tensions between efficiency, equity, and professional agency. A critical action research study conducted across three iterations of a third-year ITE course investigated how pre-service teachers engaged with AI-supported lesson planning tools while learning to design for inclusion. Analysis of 123 lesson plans, reflective journals, and survey data revealed a striking pattern. Despite instruction in inclusive pedagogy, most participants reproduced fixed-tiered differentiation and deficit-based assumptions about learners’ abilities, a process conceptualised as micro-streaming. AI-generated recommendations often shaped these outcomes, subtly reinforcing hierarchies of capability under the guise of personalisation. Yet, through iterative reflection, dialogue, and critical framing, participants began to recognise and resist these influences, reframing differentiation as design for diversity rather than classification. The findings highlight the paradoxical role of AI in teacher education, as both an amplifier of inequity and a catalyst for critical consciousness and argue for the urgent integration of critical digital pedagogy within ITE programmes. AI can advance inclusive teaching only when educators are empowered to interrogate its epistemologies, question its biases, and reclaim professional judgement as the foundation of ethical pedagogy.

Purpose: This paper reports the first cycle of a design-based research (DBR) project exploring how generative artificial intelligence (AI) can support the participatory design of educational programmes preparing students for future work. Rather than focussing narrowly on career education, it examines AI as a catalyst for reimagining curriculum co-design that integrates stakeholder expertise, ethics, and pedagogy. The study produces the Sustainable AI Career Ecosystem Model (SAICEM), conceptualising AI as a socio-technical actor within educational ecosystems. Design/methodology/approach: Using a DBR methodology, teachers, industry partners and academic experts collaboratively designed a short course addressing emerging skills and ethical challenges. Cycle 1 focused on co-design, documenting collaboration and conceptual model development. Implementation and evaluation are planned for later cycles. Findings: The co-design process demonstrated that AI could act as both design partner and pedagogical actor. Tensions emerged between automation and teacher agency, curriculum legitimacy and ethics of representation. SAICEM synthesises these insights into principles for inclusive, transparent and sustainable AI integration. Practical implications: As Cycle 1 of a multi-phase project, this study establishes design principles for AI-enabled curricula that balance innovation, professional agency and ethical responsibility, supporting equitable and future-ready education. Social implications: The study promotes equitable and ethical AI education. Originality/value: SAICEM contributes a methodological and conceptual framework for participatory AI design that extends beyond career education to general educational innovation. It advances understanding of AI as a socio-technical actor while offering practical guidance for educators, policymakers and industry.