Volume 27, numéro 2, mai 2026 Themed Issue: AI in Education and Online Learning (K-12 and Higher Education)

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This study investigated the development and implementation of the YEP (Your Educational Path) system, an educational technology ecosystem, developed by Tatl Technology, and deployed across Ukraine during the COVID-19 pandemic and ongoing war. Using a qualitative case study approach, this research drew on official government data from a learning management system pilot program (2019–2023), usage analytics (2019–2024), and documentation from public-private stakeholders. The analysis evaluates the YEP ecosystem through four dimensions: functionality, scalability, policy alignment, and crisis resilience. Key findings included rapid adoption across 2,193 schools, engagement of over 1.8 million users, and integration of AI-driven diagnostics and mental health support tools by the end of 2023. These findings have contributed to global discourse on education in emergencies and suggested a replicable model for resilient digital schooling in conflict-affected contexts.

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This study examined an online professional development program integrating artificial intelligence (AI) literacy into mathematics instruction through unplugged, explainable machine-learning activities. Ten K–12 educators created explainable feature matrices to classify geometric shapes, making machine-learning algorithms visible and accessible without requiring complex software or technological tools. The intervention used ontological principles to bridge familiar mathematical concepts with algorithmic processes. Findings demonstrated positive changes across all constructs, with participants’ AI self-efficacy increasing from below-moderate to above-moderate levels. Sentiment analysis revealed dramatic shifts from negative to positive perceptions of AI in education, with 30% of participants initially using negative descriptors versus 0% post intervention. Thematic analysis revealed three key outcomes: (a) AI concepts became explainable and learnable, (b) participants gained enhanced understanding of classification processes, and (c) participants valued the practical applicability of unplugged approaches. The study demonstrates that effective AI literacy education can be delivered through conceptual understanding rather than technological implementation, providing an accessible pathway for K–12 AI integration regardless of resource constraints.

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While generative artificial intelligence (GAI) has emerged as a vital support tool for collaborative learning, further exploration is required to achieve effective human-machine symbiosis in online collaborative processes. Grounded in symbiosis theory, our study developed a role-based intervention strategy to empower learners and their artificial intelligence (AI) partners through clearly defined responsibilities and collaborative interaction rules. In a quasi-experimental pretest-posttest design involving 58 graduate students, we employed statistical analyses and lag sequential analysis to evaluate the impact of the role intervention on online collaborative learning. The results indicated that the role design (a) significantly enhanced the quality of collaborative knowledge construction, (b) facilitated transitions among higher-order collaborative behaviors, and (c) improved perceived usefulness and ease of use of GAI among learners, although it also led to a moderate increase in collaborative cognitive load. These findings validated the core value of symbiosis theory-based role design for optimizing human-AI collaboration. Our study offered both a theoretical perspective on human-machine co-development and valuable insights for instructors to integrate AI tools and design more effective online collaborative learning activities.

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This exploratory case study provides an in-depth analysis of the potential of generative artificial intelligence (GenAI) to enhance academic support in open and distance learning (ODL) systems. The study examined learner experiences with a GenAI-based academic support application in an online web publishing course over a semester, focusing on two phases: free use and structured use. Data were collected through semi-structured interviews and dialogue transcripts from 10 distance learners. Findings highlighted both continuity and transformation in learner practices. In both phases, GenAI was valued for time-saving and accurate responses aligned with course materials. Structured tasks in phase 2 encouraged more purposeful engagement, including systematic self-assessment and information verification. Despite technical challenges such as device incompatibility and occasional hallucinations, learners expressed motivation, satisfaction, and a demand for institutional integration. The results, while preliminary, suggest that GenAI-based academic support holds strong potential for broader implementation in large-scale open universities, offering a pathway to balancing quality, access, and cost in addressing the enduring challenges of mass higher education.

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This study investigated the impact of generative artificial intelligence (GenAI) supported by blended instruction on the argumentative writing skills of first-year students in an English as a foreign language (EFL) teacher education program in a state university in Türkiye. The study was designed as a qualitative case study supported by quantitative data. The study involved nine English language teaching students who initially received traditional academic writing instruction. They completed a pre-test. They participated in a 4-week online writing course integrating GenAI tools within a blended learning environment. Data were collected through pre- and post-tests as well as semi-structured interviews and analyzed using thematic analysis. Findings indicate that GenAI contributed to key stages of the writing process, particularly in idea generation, text organization, argument development, and critical thinking. Participants reported increased confidence and engagement, benefiting from immediate, personalized feedback and flexible learning opportunities. However, concerns regarding reliability and overdependence also emerged. The study suggests that with proper teacher guidance, GenAI can function as a pedagogical scaffold in blended academic writing instruction, supporting learners’ higher-order thinking and autonomy. These insights contribute to understanding how emerging AI technologies can be effectively integrated into EFL contexts to enhance complex writing skills.

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This field note provides an example of the use of an education/researcher artificial intelligence program to provide an overview of the Interaction Equivalency Theory. This theory was first presented as an example in Anderson, T. (2003), "Getting the mix right again: An updated and theoretical rationale for interaction", in the International Review of Research in Open and Distributed Learning, 4(2). The AI tool provides a useful synopsis and overview of the value of this theory for distance education students and researchers.

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Generative artificial intelligence (AI) is transforming education, creating opportunities for personalization, efficiency, and engagement while also raising concerns about misinformation, overreliance, and the erosion of critical thinking. To navigate these tensions, this article argues for the necessity of a coherent theoretical framework to guide the educational adoption of AI. Drawing on the Community of Inquiry (CoI) framework and its construct of shared metacognition, we outline how collaborative inquiry can integrate AI in ways that preserve human agency and sustain deep and meaningful learning.

We examine the potential for AI to assume multiple roles within a community of inquiry—supporting instructional design, guiding learners as an independent resource, assisting instructors through analytics, participating in discussions, and sustaining dialogical partnerships with students. While these roles highlight the capacity of AI to enrich learning communities, they also underscore risks of passivity, diminished authenticity, and overdependence if reflective inquiry is bypassed.

We argue that shared metacognition—collective monitoring and management of thinking—offers a responsible pathway for educators and learners to engage critically with AI-generated outputs, ensuring that technology strengthens rather than supplants collaborative inquiry. In conclusion, we contend that AI can contribute to worthwhile educational experiences only when framed within a coherent conceptual perspective that emphasizes skeptical engagement, collaborative reflection, and the preservation of human purpose. In this regard, the CoI framework has considerable potential to provide understanding and guidance in the adoption of AI tools.

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This scoping review examines how artificial intelligence (AI) has been conceptualized and applied in adaptive learning and learning analytics in K–12 online and distance education between 2020 and 2025. Following Arksey and O’Malley’s framework and reported in accordance with PRISMA-ScR, we analyzed 21 empirical studies to explore thematic patterns, methodological trends, and research gaps. Most studies reported gains for learners in engagement, motivation, and self-regulation. However, reported benefits were unevenly distributed and often favored better-resourced learners, particularly in contexts where teacher mediation and institutional support were modest. AI was explicitly integrated in two-thirds of the studies, yet definitional inconsistencies blurred distinctions between genuine intelligence and automated adaptation. Quantitative designs were predominant, largely focusing on performance outcomes as derived from system logs and test data. While a small but growing number of mixed-methods studies have focused on learner experience and teacher mediation, the field remains constrained by methodological consistency and insufficient clarity regarding AI mechanisms. The findings highlight the importance of clearer conceptual frameworks, research designs that are participatory and context-sensitive, and ethical approaches that center teacher expertise and learner participation. This review argues that the transformative potential of AI for adaptive learning depends less on technological sophistication than on equitable, pedagogically informed integration between human judgment and automated systems.