Editorial: Digital learning innovations: trends emerging scenario, challenges and opportunities

instructional design; (2) learning analytics and AI-assisted assessment; (3)ethical governance and data practices; and (4) institutional readiness and pedagogical transformation. Considered together, these themes move the discussion beyond generic claims about opportunity and challenge toward a more precise account of how AI reorganises educational work.One of the strongest themes in this Research Topic is the growing role of generative AI in instructional design and digital teaching. Recent advances in large language models and multimodal systems have made it possible to generate text explanations, visual materials, quizzes, prompts, and other instructional resources at speed. Within AI-enabled educational platforms, these generative systems are being incorporated into course design, content production, and adaptive teaching workflows.Several contributions examine how generative AI tools support educators and instructional designers in developing teaching materials and structuring digital learning activities. Han and Lim (2025) explore how novice instructional designers navigate generative AI as a scaffold and a disruptor, revealing tensions between efficiency gains and the erosion of design agency. Bhattacharya et al. (2025) examine educator attitudes toward augmented reality applications in school settings, documenting both enthusiasm and significant implementation barriers. Rakha (2025) investigates how QR code-mediated cooperative learning strategies reshape cognitive achievement and student attitudes in technology-rich courses. Manulat (2025) identifies key predictors of academic success in flexible learning environments using structural equation modelling, while Dyantyi-Gwanya et al. (2025) foreground the intersection of digital literacy and disciplinary proficiency among first-year students in South Africa. These studies show that generative systems can accelerate drafting, ideation, and revision, allowing instructors to redirect time toward mentoring, dialogue, and higher-order learning tasks. At the same time, they invite questions about authorship, pedagogical intent, and the conditions under which generated content becomes educationally reliable.Other studies consider how AI-enabled teaching applications may improve efficiency and student engagement. Margheri et al. (2025) offer a systematic review of child engagement during interaction with digital and robotic activities, mapping the conditions under which technology mediates sustained participation. Darmanova et al. (2025) provide a systematic review of technology use in middle and high school mathematics education, highlighting uneven outcomes across methodological and contextual variables. Ma (2025) examines the effect of Geometer Sketchpad on secondary school students' mathematical intelligence, illustrating how specific digital tools can enhance conceptual understanding in STEM contexts. Here, the value of generative AI lies not only in convenience but in its capacity to mediate interaction, scaffold participation, and diversify instructional formats. Yet this same mediation can foster over-reliance on machine-produced explanations or narrow pedagogical experimentation to what platforms can easily generate.Several contributions address AI pedagogy and teaching innovation more directly. Fredriksson (2025) analyses pre-service teachers' feedback strategies when working alongside generative AI, foregrounding the risks of over-delegation and the importance of maintaining critical interpretive authority. Rovira-Collado et al. (2025) investigate creativity and writing with generative AI in a master's degree teacher training programme, demonstrating how AI can function as a generative interlocutor when structured pedagogically. Mateo-Girona et al. (2025) demonstrate how didactically guided prompting can improve iterative revision and textual quality, suggesting that the design of AI interaction -not merely its availability -determines its educational value. Beyazhancer and Demir (2025) examine how AI applications in engineering education enhance techno-mathematical literacy and AI self-efficacy, illustrating the discipline-specific dimensions of AI pedagogical integration. Küçükuncular (2025) argues for learning with, rather than through, AI, proposing a co-design model for science education centred on critical AI literacy.Several contributions therefore call for pedagogical frameworks that support critical engagement with AI-generated material and protect academic integrity. Gaviria Rodríguez et al. (2025) analyse intention to use AI in accounting education through the Technology Acceptance Model and Theory of Planned Behaviour, showing how disciplinary context and institutional culture shape adoption. Muringa (2025) examines ethical dilemmas and institutional challenges arising from student AI adoption across South African universities, highlighting the absence of structured policy frameworks and the risks of unequal digital access. Lizano-Sánchez et al. (2025) document students' interactions with an AI assistant in a remote chemistry laboratory, revealing both the affordances and limitations of AI-mediated guidance in disciplinary settings. Read together, these articles suggest that generative AI is most productive when treated as an assistive and collaborative pedagogical technology rather than an autonomous instructional agent. This framing foregrounds augmentation without obscuring the risks of automation bias, epistemic dependency, and the outsourcing of interpretive labour.A second major theme in the Research Topic is the expansion of learning analytics and AIassisted assessment in higher education. Learning analytics systems process behavioural and performance data generated through AI-enabled educational platforms in order to identify patterns in engagement, progression, and difficulty. The promise of these systems lies in their ability to support earlier intervention and more responsive teaching.Several articles demonstrate how AI-powered analytics can identify patterns in student learning processes and support timely intervention. Marcial (2025) proposes a matrix for evaluating AI-powered learning platforms based on the UNESCO ethical impact assessment tool, providing a governance framework that links analytical capacity to institutional accountability. Xue et al. ( 2025 AI-assisted assessment is also reshaping evaluation practices. Alghamdi and Alghizzi (2025) examine educators' reflections on AI-automated feedback in higher education, documenting both the potential for personalised responsiveness and the pitfalls of reduced educator presence in formative assessment. Baldrich et al. (2025) provide empirical evidence on how AI tools are reshaping reading and writing practices in academic literacy, raising important questions about the redistribution of interpretive labour between students, educators, and platforms. Al Mulhim (2025) investigates the role of chatbots in mitigating student resistance to educational technology integration, showing how AI-mediated interaction can lower adoption barriers while introducing new dependencies. Olivares-De la Fuente et al. ( 2025) conduct a systematic review of Twitter and YouTube as digital tools in higher education, mapping how social media platforms complement or compete with AI-enabled assessment and engagement systems. These developments may improve responsiveness, but they also raise questions about the standardisation of judgement, the pedagogical consequences of continuous prediction, and the extent to which assessment is being reorganised around platform logics.The key issue is not simply whether these systems are efficient, but what kinds of educational judgement they privilege. When predictive models and automated feedback become central to assessment, concerns emerge around interpretability, validity, and the reproduction of existing inequalities. The collection therefore invites closer scrutiny of how AI-assisted assessment may subtly shift authority away from educators and toward technical systems whose assumptions are not always visible.Ethical governance emerges in this collection not as a separate concern but as a structural condition of AI adoption in education. As AI-enabled educational platforms process large volumes of student data, institutions must confront questions of consent, ownership, and acceptable use. The studies gathered here show that data-intensive educational systems do more than personalise learning; they can also intensify surveillance pedagogies, expand administrative monitoring, and normalise forms of behavioural capture that exceed legitimate educational need.García-López and Trujillo-Liñán (2025) conduct a systematic review of the ethical and regulatory challenges of generative AI in education, identifying significant governance gaps across institutional contexts and calling for comprehensive regulatory frameworks grounded in data ethics and learning sciences. Bouakaz and Khalid (2025) offer a sociological exploration of AI in learning environments, examining how algorithmic systems reproduce and intensify existing social inequalities in educational settings. Lakhe Shrestha et al. ( 2025) examine the integration of generative AI in English language teachers' professional development through the TPACK framework, identifying GenAI dependency and ethical challenges as key concerns. Algorithmic bias remains a central issue, particularly when AI systems are trained on incomplete, unrepresentative, or historically skewed datasets. In educational contexts, such bias can shape feedback, recommendations, and classifications across lines of gender, language, disability, and socio-economic status. Vinueza-Morales et al. (2025) provide a bibliometric analysis of trends and technologies in programming education, offering a research landscape that contextualises algorithmic and pedagogical risk in digital education. The contributions in this cluster underscore the importance of inclusive design, accountable governance, and critical oversight of the assumptions built into AI systems.A related concern is explainability. When AI-generated recommendations or classifications influence student feedback or educational pathways, educators and learners need intelligible reasons rather than opaque outputs. Responsible governance therefore requires more than abstract commitments to fairness and transparency; it requires institutional capacity to question models, audit their effects, and align technical systems with educational values.Beyond technical innovation, successful integration of AI-enabled educational platforms depends on institutional readiness and pedagogical transformation. Several studies examine how universities prepare faculty to work with generative AI, analytics, and automated systems in context-sensitive ways. Professional development is especially important because educators need not only technical familiarity but also conceptual tools for judging when AI support is pedagogically useful, when it risks deskilling professional practice, and when it conflicts with institutional or disciplinary norms. 2025) examine the challenges, opportunities, and recommendations for integrating AI into pre-clinical medical education, offering a discipline-specific model for institutional adoption that foregrounds governance and pedagogical design. Ocen et al. ( 2025) review innovations, opportunities, and challenges of AI in higher education institutions, identifying key barriers to adoption and strategies for building institutional capacity. Malik and Shah (2025) provide a historical and analytical account of AI-based robots as teachers, assessing the potential, concerns, and conditions under which AI systems might take on instructional roles without displacing educator agency. Buele and Llerena-Aguirre ( 2025 2025) evaluate a solidary academicpedagogical project in virtual graduate programmes, highlighting how institutional culture and collaborative design shape conditions for successful digital learning transformation. Huang et al. ( 2025) examine digital education for graduate students in control science, demonstrating how disciplinary specificity must inform AI integration strategies. Akbari (2025) provides comparative evidence on post-COVID preferences for face-to-face, blended, and e-learning methods among teachers and students in Iran, showing how institutional transformation must reckon with diverse learner preferences and infrastructural constraints.Digital inclusion is equally a structural condition of meaningful pedagogical transformation. Junger and Hanke (2025) examine the technical-pedagogical usability of digital inclusive reading support, demonstrating how assistive technologies must be co-developed with practitioners to become educationally effective. Barragán Moreno and Lozano Galindo (2025) model digital inequality in basic education using system dynamics simulation, revealing how infrastructure gaps compound over time and produce durable disadvantages. 2025) assess the impact of laptop integration on students' technology self-efficacy, offering evidence on how equity of device access shapes the conditions for AI-enabled learning. Røe et al. (2025) examine the integration of peerassisted learning and virtual reality gaming in health professions education, showing how digital tools can empower students as co-educators while developing their professional digital competencies.These studies make clear that institutional adoption is not a matter of plug-and-play deployment; it depends on governance, capacity building, equity of access, and local pedagogical negotiation. Taken together, these contributions show that pedagogical transformation requires more than infrastructure. It depends on whether institutions can develop cultures of critical adoption in which AI systems are evaluated not only for efficiency, but also for their effects on labour, expertise, student autonomy, and the public purposes of higher education.While the contributions in this Research Topic offer valuable insight into AI-enabled educational platforms, they also reveal a significant geographical asymmetry. Much of the literature still focuses on settings where digital infrastructure, institutional capacity, and platform access are relatively well established. Empirical work from the Global South remains limited, even though questions of language diversity, uneven connectivity, cost, and socio-economic inequality are likely to shape AI adoption in distinctive ways -as contributions from South Africa (Dyantyi-Gwanya et al., 2025;Muringa, 2025), Lebanon (Røe et al., 2025), and Latin America (Barragán Moreno and Lozano Galindo, 2025;Ortiz-Ordoñez et al., 2025) begin to demonstrate.Future research should therefore widen the geographical and socio-cultural scope of AI-ineducation scholarship. More work is needed on how AI systems operate in contexts marked by infrastructural unevenness, multilingual environments, and constrained institutional resources. Such research is essential not only for digital inclusion, but also for preventing educational AI from reproducing a narrow set of assumptions derived from better-resourced contexts.Several research directions follow from this gap. Longitudinal studies are needed to trace how AI-enabled educational platforms affect student engagement, teacher work, and educational judgement over time. Interdisciplinary collaboration among educators, technologists, psychologists, and policymakers remains crucial. Just as importantly, future work should examine changing human-AI relations in education without reducing them either to replacement narratives or to technological optimism. AI may augment pedagogical practice, but its long-term implications for educator autonomy, professional expertise, and institutional power require sustained scrutiny.The Research Topic "Digital Learning Innovations: Trends, Emerging Scenario, Challenges and Opportunities" offers a timely view of how AI-enabled educational platforms are reshaping higher education. The 33 contributions show that contemporary digital learning is being transformed not only through new tools, but through wider shifts in instructional design, assessment, governance, and institutional organisation. What emerges from the collection is a picture of AI in education as a socio-technical reordering of pedagogical work rather than a simple story of innovation.The value of this collection lies in showing that the future of AI in education will depend on the frameworks through which institutions govern adoption, support educators, and define educational purpose. If AI is to contribute meaningfully to higher education, it must be approached with conceptual clarity, critical scrutiny, and an explicit commitment to inclusive and educationally defensible practice.