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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.

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.

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.

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.

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.

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.

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.

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.

This field note examines the recent regulatory framework for distance higher education in Brazil, analyzing the implications of Decree 12,456/2025 and Ordinance 378/2025. Through critical analysis, we assessed the alignment of these measures and their potential impacts on educational quality, accessibility, and institutional accountability. We examined the measures designed to balance the expansion of access with the assurance of quality, including mandated percentages of in-person and synchronous activities, redefined faculty roles, and restrictions on institutional sharing. While acknowledging the potential to enhance academic rigor and curb low-quality programs, the analysis highlighted significant implementation challenges. These include increased operational costs, potential impacts on tuition, and concerns that restricting teacher education to blended or in-person modalities may exacerbate teacher shortages in remote areas. The study concluded that the new framework’s ability to reduce inequalities and improve employability depends on financial support, vigilant oversight, and further research, offering a valuable case study for global debates on regulating digital higher education.

The Handbook of Open Universities Around the World, edited by Sanjaya Mishra and Santosh Panda, offers both a panoramic survey and a reflective critique of what openness truly means in higher education today. Drawing together insights from more than 100 scholars and practitioners, the editors have curated an extraordinary compilation that maps the histories, organizational structures, and innovations of 47 open universities across Africa, the Americas, Asia, Europe, and Oceania. The result is not only a celebration of institutional achievement but also an invitation to confront difficult questions about equity, sustainability, and the future of open learning.

Open universities were originally conceived as democratic institutions designed to remove barriers of geography, class, gender, and prior schooling. They opened doors to learners traditionally excluded from mainstream education systems. In the current era of rapid digital transformation, when artificial intelligence (AI) and data-driven technologies are reshaping how education is delivered and experienced, the notion of openness demands fresh examination. The Handbook situates itself precisely at this critical juncture, bridging historical foundations with emerging digital realities.

This meta-analysis synthesized empirical findings on the influence of ChatGPT on learning achievement. An electronic database search using PRISMA guidelines was conducted with relevant keywords to identify eligible research studies published between November 2022 and December 2024. A total of 22 eligible publications that met our inclusion criteria were reviewed. The overall effect size of ChatGPT's influence on learning achievement was moderate (g= 0.573), suggesting that ChatGPT has the potential to improve learning outcomes. Most participants in the studies were undergraduates (70.9%). However, subgroup analysis revealed that the effect size for middle and high school students (g= 0.928) was larger than that for undergraduates (g= 0.538), although the difference was not statistically significant. This finding highlights the importance for instructors and educational practitioners to consider the applications of ChatGPT in middle and high school settings. No significant statistical differences were found among the three learning domains: cognitive (g= 0.612), affective (g= 0.481), and metacognitive (g= 0.619). Given that nearly half of the studies focused on the cognitive domain, it is important to diversify the application of generative AI across a variety of subjects in different learning domains. The most frequently used instructional approaches with ChatGPT applications were lectures (22.1%) and self-regulated learning (16.3%). The largest effect sizes were observed for self-regulated learning (g= 1.115) and case-based learning (g= 0.836), while the smallest effect size was for game-based learning (g= 0.092, ns). This study was conducted within two years of ChatGPT's emergence, limiting in our ability to analyze a large number of publications. Nevertheless, this study offers meaningful implications for future research on the application of ChatGPT for educational purposes.

Given the growing demand for flexible and accessible health education, massive open online courses (MOOCs) have been recognized as instrumental in expanding undergraduate learning. This systematic review was conducted to investigate the use of MOOCs in undergraduate health education, focusing on publication trends, geographic distribution, and key research variables. A total of 31 peer-reviewed articles were reviewed, and data were sourced from six international databases: Web of Science, Scopus, ERIC, EBSCOHost, ScienceDirect, and PubMed. It was found that MOOCs have been integrated into undergraduate health education since 2014, with a notable increase in publications observed after 2022. The highest number of studies was published in China. Student satisfaction was identified as the most frequently studied variable, and medical education was reported as the dominant field. Quantitative research were predominantly used, with sample sizes between 101 and 300 participants. Questionnaires were commonly employed as a data collection tool, and many studies were based on custom-developed MOOCs for their research. Courses were typically between 4 and 6 weeks duration. Improved clinical skills were frequently reported as outcomes, while the lack of practical experience in MOOC-based learning was identified as a major limitation. More practice-oriented teaching approaches were recommended by most studies. To enhance the effectiveness of MOOCs in health education, more innovative and practical implementation strategies are needed. Future research is encouraged to address these gaps and strengthen the impact of MOOCs on undergraduate health programs. The growing role of MOOCs in health education is highlighted, particularly the need to integrate practical components for greater educational impact.

Digital literacy is central to collaborative teaching in technology-mediated environments, particularly open and distributed learning. Guided by the Community of Inquiry and TPACK (Technological Pedagogical Content Knowledge) frameworks, this systematic review examines how digital literacy enables educators to codesign instruction, sustain interaction, and support reflective practice while addressing structural and contextual barriers. Following PRISMA 2020, comprehensive searches in Scopus and the Web of Science identified 32 peer-reviewed articles published in 2024. Thematic synthesis produced three strands: (a) integration of digital literacy in education, highlighting links to teaching presence, professional development, and instructional design; (b) digital literacy in response to educational challenges, demonstrating its role in resilience, equity, and socio-emotional support across remote and hybrid contexts; and (c) advancing learning through digital competencies, detailing gains in collaboration, critical inquiry, and innovative use of augmented reality, virtual reality, data analytics, and emerging AI tools alongside ethical considerations. Evidence indicates that digital literacy functions as a pedagogical capacity rather than solely a technical skill and yields the strongest outcomes when aligned with institutional culture, curriculum design, and continuous professional learning. Policy recommendations include sustained investment in equitable infrastructure, structured capacity building aligned with UNESCO’s Digital Literacy Global Framework and ICT (Information and Communication Technology) Competency Framework for Teachers, and explicit attention to ethics and inclusion. Future research should adopt longitudinal and comparative designs to trace the impact on educator identity, collaboration, and learner outcomes.

While teachers worldwide rapidly switched to emergency remote teaching almost overnight owing to the unprecedented global pandemic, the rise of artificial intelligence (AI) has further transformed language education paradigms. Although previous research has explored foreign language teaching anxiety (FLTA), the self-perceived online L2 speaking anxiety of teachers remains underexplored. Accordingly, this study has been designed on a wide scale to address this lacuna by focusing on the perceptions of anxiety of 179 non-native EFL teachers at the Ministry of Education and instructors in higher education contexts. Moreover, it aimed to reveal its provoking reasons and finally the reported reflections of educators’ apprehension on virtual classes. To that end, qualitative and quantitative data were gathered in a complementary fashion through semi-structured interviews and an online survey developed by the researcher. The study identified the lack of perceived competence, troubles with online technologies, and learners’ English proficiency as factors leading EFL educators to experience online L2 speaking anxiety despite their self-confidence. Their reported reflections also disclosed that self-confidence without competence would be of almost no use in language teaching. Finally, some significant differences were detected between the participants’ demographic variables and their online L2 speaking anxiety.

Online learning environments tend not to provide the social and pedagogical cues of physical classrooms, so evaluating student engagement and emotional states in real time becomes challenging. Current methods depend mainly upon facial expression recognition or textual sentiment analysis, constraining the depth and accuracy of behavioral interpretation. This research suggests a multimodal learning analytics framework that combines visual and textual data to infer learner emotions and engagement for improving the interpretability, responsiveness, and pedagogical value of learning analytics systems in digital education. Two datasets were created: (a) a facial expression dataset of 10,000 grayscale images annotated over five emotion categories and (b) an engagement dataset of 4,000 images annotated according to behavioral indicators. Concurrently, 1,667 learner feedback responses from massive open online courses were prepared for sentiment analysis. Convolutional neural networks (CNNs) were used for emotion and engagement classification, and a fine-tuned BERT (bidirectional encoder representations from transformers) model for sentiment analysis. A rule-based integration engine combined outputs to create multidimensional behavioural typologies. The CNN models reached >92% validation accuracy for both emotion detection and engagement detection tasks, whereas the BERT sentiment classifier achieved F1 = 0.87 and 88.1% accuracy. The multimodal integration procedure identified four unique learner behavior typologies (e.g., students who were cognitively engaged but visually disengaged). The framework offers an accurate, interpretable, and scalable real-time learning analytics solution. Compared with previous methods, it overcomes significant limitations and offers a useful resource for facilitating adaptive, data-based instruction interventions, especially in online and health science education.

This study examined how task characteristics (TC) and individual characteristics (IC) affect cognitive load (CL) and how artificial intelligence generated content (AIGC) moderates these effects in online learning. Participants included 435 undergraduate students (200 males and 235 females) enrolled in an introductory educational technology course. A structural model, conducted using Mplus software, was employed to test the relationships between each of TC and IC, and CL. Additional analyses explored the moderating role of AIGC on the relationship between TC and CL, the impact of AIGC on the relationship between IC and CL, as well as how these patterns differed by gender. Results revealed that TC positively affected CL, whereas IC exhibited a negative correlation. Moreover, AIGC negatively affected the relationship between TC and CL, but it enhanced the relationship between IC and CL. The moderating role of AIGC differed by gender. Specifically, AIGC positively influenced the connection between IC and CL among males but not females, and it weakened the relationship between TC and CL among females but not males. The implications and limitations are also discussed.

This study investigated middle school students’ experiences with emergency remote education during the COVID-19 pandemic using natural language processing (NLP), sentiment analysis, and topic modeling techniques. A total of 2,739 valid responses from Turkish students (ages 9–15) were collected through open-ended survey questions regarding the perceived advantages and disadvantages of distance learning. Sentiment classification was performed using a semi-supervised machine learning approach, combining TF-IDF, Word2Vec, and FastText vectorization with five classification algorithms. The TF-IDF + support vector machines (SVM) combination yielded the highest performance (F1 = 0.85). Results show a total of 1,867 positive and 2,542 negative opinions, indicating that students generally adopted a more critical view of distance education. To explore the thematic structure of opinions, topic modeling was applied with six topics. Positive sentiments clustered around themes such as educational continuity, health protection, time savings, flexible scheduling, self-regulated learning, and digital literacy. Negative sentiments were dominated by themes including limited interaction, screen fatigue, perceived low quality, technical barriers, and structural inequalities. Findings suggest that while students appreciated the safety and flexibility of remote learning, they also faced significant pedagogical, physical, and technological challenges. The study contributes methodologically by demonstrating the effectiveness of AI-based text analysis and offers practical implications for designing more equitable and student-centered digital education models. These results underscore the importance of integrating NLP and machine learning tools into educational research to uncover deeper insights from student-generated content at scale.