Volume 27, numéro 3, août 2026

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The proliferation of generative artificial intelligence (GenAI) presents a significant challenge to traditional notions of accountability in scholarly publishing. This report addresses the pressing question of who is responsible for the content of academic work when its creation involves AI assistance. It does so by introducing and extending Erving Goffman’s (1981a, b) concept of production format, a framework that deconstructs the singular speaker into three distinct roles: animator, author (herein re-labelled designer), and principal. By applying this framework to written, scholarly communication, this report analyzes two key problems: the accountability of publishers and platforms as relaying animators and the apparent displacement of the authorial role by GenAI. The analysis draws on recent empirical evidence of journal policy adoption and theoretical applications of Goffman’s work to the AI context. It pays particular attention to the implications for open access publishing, where the democratization of knowledge creation must be balanced with the imperative for rigorous accountability. The report concludes that while GenAI can assume a large proportion of the designer function of shaping textual form, the ultimate accountability for scholarly work must remain with a human principal. Detailed recommendations are provided for scholars, publishers (with specific focus on open access journals), and academic institutions to navigate the new landscape of AI-assisted authorship, ensuring transparency and preserving the integrity of academic discourse.

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The integration of artificial intelligence (AI) into scholarly peer review represents a fundamental transformation of academic publishing’s quality control mechanisms. This report critically examined the ethical considerations, institutional practices, and emerging technologies associated with AI-assisted peer review. Drawing on recent policy documents from major publishing organizations, empirical research on AI implementation, and critical scholarship on algorithmic bias, this analysis revealed significant tensions between efficiency gains and integrity preservation. While AI tools have demonstrated potential for addressing reviewer burnout and publication delays, their deployment raises critical concerns regarding confidentiality breaches, accountability gaps, algorithmic bias, and the erosion of expert judgment. Major organizations (e.g., International Committee of Medical Journal Editors) and leading publishers such as Elsevier and Taylor & Francis have emphasized disclosure where AI is used, human accountability, and strict confidentiality controls—often prohibiting uploading unpublished manuscripts into generative AI tools. However, empirical evidence has suggested nontrivial, and potentially growing, undisclosed large language model (LLM)-assisted text in peer review in some conference contexts. This report concluded that AI should serve as an augmentative rather than substitutive technology in peer review, with robust governance frameworks, transparent disclosure mechanisms, and continuous evaluation of equity implications essential for responsible implementation.

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This study explores the decline in student enrollment in rural elementary schools in Banyumas Regency, Indonesia, as a pressing challenge for educational equity in the digital era. Using a qualitative descriptive design, data were collected through interviews with principals, teachers, parents, and community leaders, as well as through classroom observations and document analysis. Thematic analysis with NVivo 15 identified three primary determinants of enrollment decline: demographic shifts due to declining birth rates and outward migration; structural disadvantages, including remote locations and limited infrastructure; and parental preferences for private or religious schools, which are perceived as superior in academic and technological readiness. While small class sizes offered more personalized instruction, negative consequences predominated, including decreased motivation, reduced pedagogical diversity, and heightened risks of school mergers or closures. The digital divide further intensified inequities, making rural schools less attractive than better-connected institutions. The findings highlight that declining enrollment reflects not only demographic change but also systemic digital inequity, requiring targeted policies to expand digital infrastructure and promote distributed learning.

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Artificial intelligence in online learning (AIOL) has attracted increasing attention in academia. This study applied BERTopic, a transformer-based topic modeling approach that leverages contextual embeddings, to examine the thematic structure and evolution of AIOL research. By analyzing 1,048 AIOL publications, this study sought to answer three research questions. What are the major research topics in AIOL? How have these topics evolved over time? What future research directions are recognized? Several research themes were identified, such as adaptive learning systems, sentiment analysis, and predictive analytics. Temporal analysis revealed a shift from early applications of traditional AI toward machine learning and deep learning approaches. The results also revealed an increasing emphasis on multimodal data integration for emotion recognition and personalized learning support. Based on the findings, a conceptual model has been proposed to guide AIOL research and practice by integrating four components: data, AI processing, adaptive learning, and learner development. Overall, the study offered a data-driven overview of AIOL research and demonstrated how topic modeling can be used to examine thematic development in emerging research fields.

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The literature has highlighted the importance of engaging students in online learning through multiple forms of interaction: student-content, student-instructor, and student-student interactions. While the quality of interaction plays a significant role in either enhancing or undermining students’ motivation to learn, anxiety has remained a persistent challenge. Limited research has examined the indirect relationships between each of the three forms of interaction and motivation through the lens of anxiety. This study aimed to investigate the mediating role of anxiety in the relationships between the forms of interaction quality and student motivation in university-level online courses. Data were collected from 140 students via an online survey, and multiple procedures ensured instrument validity. Structural equation modeling was used to evaluate research hypotheses. The research model revealed a good fit with the collected data based on the recommended goodness-of-fit indices. Findings revealed that anxiety fully mediated the relationships between student-content and student-instructor interactions and motivation, and partially mediated the relationship between student-student interaction and motivation. These results suggested that instructors should carefully consider the quality of all three types of interaction when designing and delivering online courses, with the goal of reducing students’ anxiety and supporting their motivation to learn.

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In the context of widespread digitalization in higher education, enhancing teacher digital competence (TDC) has become a critical priority. However, institutional efforts to advance TDC, including the development of open educational resources (OER), are often hindered by the prevalence of training models that lack pedagogical depth and rely on transmissive methods. Current literature on open educational practices (OEP) has highlighted the transformative potential of openness in teaching, yet it also suggests a disconnect between the availability of resources and their actual adoption. Building upon this gap, this article describes the creation of a theoretical framework intended to guide the design of OER-based training that effectively integrates OEP for faculty development. The method employed was a qualitative, exploratory approach, constituting the first phase of a design-based research (DBR) project. The framework was constructed in two stages: first, defining its conceptual foundations through a rigorous literature review, and second, validating and refining these elements through input from experts in educational technology. The primary result is a validated theoretical framework for OER-based digital training. The conclusions emphasize that this framework is distinct from others because it serves as a meta-reflective element, focusing directly on the teacher’s own professional development and practice, rather than only on student-facing teaching processes. While the framework can guide institutional digital transformation, its initial design was developed within a specific university context, following the DBR approach. Future research will involve implementing, revising, and validating the framework in other contexts.

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This study investigates how the format of asynchronous online class discussions (whole class vs. small group) affects the structure and content of student interactions. Data were collected from 8 weeks of discussion forums in a graduate-level, asynchronous, online course at a comprehensive university in the southeastern United States. Social network analysis (SNA) metrics, such as response rate, reciprocity, transitivity, diameter, density, in-degree, and closeness centrality, were used to evaluate interaction structure. Linguistic Inquiry and Word Count (LIWC) software was used to analyze the linguistic features of the interaction content. Findings revealed differences between the formats: whole-class discussions exhibited greater diameter and variability in response rate, emphasizing formal and analytical communication. In contrast, small-group discussions displayed higher reciprocity and transitivity, as well as more support and socioemotional engagement. Findings underscore the importance of designing and facilitating collaborative online learning experiences by strategically employing discussion formats to meet specific learning objectives. Implications for practice include incorporating both whole-class and small-group discussions to leverage the strengths of each format, such as creativity and critical thinking in larger groups and mutuality and support in small groups. Future research should examine these effects across different educational levels and subject areas, considering instructional contexts and personal learner characteristics.

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During the COVID-19 pandemic, various institutions took measures to prevent the spread of the virus. Educational institutions contributed to these efforts by shifting from face-to-face to online learning. One such approach was blended learning, which served as a transitional model between the two. Blended learning has become an integral part of higher education, particularly in English as a foreign language (EFL) instruction. This study investigated Turkish EFL learners’ perceptions of blended learning by comparing two different student cohorts from 2021–2022 and 2024–2025 using a cross-sectional research design. Data were collected through classroom observations and semi-structured interviews with 24 students in total. Although more than 75% of the students acknowledged the flexibility and accessibility of blended learning, persistent concerns remained regarding engagement, socialization, and assessment integrity in online settings. Over time, there has been a slight increase in student acceptance of blended learning, particularly in the areas of autonomous learning and digital literacy. However, many students still expressed a preference for face-to-face instruction, especially for productive skills like speaking and collaborative activities. The study contributed to the growing body of literature by offering a comparative perspective on the challenges and advantages of blended learning in EFL education and providing recommendations for optimizing its effectiveness.

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Open educational resources (OER) have emerged as key tools in democratizing education, and the levels of student awareness of OER, as well as engagement and use in many Canadian institutions, are increasing. This study explores whether involving undergraduates in the creation of OER improves their understanding of open licensing, their perceptions of OER utility, and their ability to independently access such resources. A pre-/post-activity study was conducted with 57 undergraduate students enrolled in an advanced biochemistry course. As part of the course’s activities, students collaboratively contributed to the creation of a new open resource aiming to increase understanding of enzyme mechanisms, to be used by themselves as well as their peers. Data were collected using structured surveys and analyzed via the Wilcoxon signed-rank test across three domains: Creative Commons knowledge, OER perception, and OER access. Statistically significant improvements were observed in students’ understanding of open licensing, particularly their knowledge of Creative Commons licenses (e.g., understanding license types; p < .001), perceptions of OER credibility (e.g., perceived accuracy and source citation; p = .0049), and behaviors related to accessing diverse types of OER materials (p = .0397). Thematic analysis supported these findings, highlighting learning gains and increased engagement in areas previously unfamiliar to students. Participation in the creation of OER fostered a deeper conceptual understanding and more favorable attitudes toward open education. However, changes in access behavior were modest, indicating the need for institutional scaffolding to fully support learner autonomy in open environments.

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Although research on distance education has increased substantially in recent years, comprehensive analyses capturing the overall thematic development of the field remain limited. Modeling studies are necessary to address this gap and to uncover long-term research trends. This study aimed to reveal the trends of research conducted in the field of distance education in the last two decades by using machine learning methodology and bibliometric analysis. The unique aspect of the study was its use of the Latent Dirichlet Allocation (LDA) algorithm, a machine learning method for analysing large-scale data. Within the scope of the study, 54,444 articles in the Web of Science database were analysed. Bibliometric analysis revealed that distance education had gained significant momentum, especially in the post-pandemic period, with a wide range of applications in different disciplines such as education, management, and health. As a result of LDA analysis, 19 thematic topics were identified. Among these, digitalization, artificial intelligence, Web-based learning, and social interaction and collaboration stood out. Time series analysis showed an increasing trend for topics such as artificial intelligence and emergency distance education over the years, with less interest shown in topics such as Web-based learning and program design. The study emphasized that distance education is in a technology-driven transformation process and pointed to areas that will offer new opportunities for researchers. Furthermore, recommendations for decision-makers, such as investments in digital infrastructure, integration of artificial intelligence-based systems, and the establishment of quality standards, also emerged.

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This mixed-methods study developed and evaluated a comprehensive 5-week asynchronous in-service training program to enhance K–12 teachers’ online education competencies across pedagogical, social, managerial, and technical domains. Following needs assessment, 21 teachers in Türkiye participated using the Canvas learning management system (LMS). The competency-based program emphasized authentic learning experiences, requiring participants to design online lesson plans, use Web 2.0 tools, and implement the LMS in classroom contexts. Data collection employed triangulated methods including learning analytics and products, pre-/post-test assessments, and semi-structured interviews. Results demonstrated significant improvement in post-test scores (p < .05) and high engagement through practical activities. Learning analytics revealed varied engagement patterns, with participants completing authentic assignments. Qualitative findings highlighted gains in content development, instructional design skills, and digital competence. The study demonstrates that teachers require experiential learning as online students before becoming effective online instructors, emphasizing the necessity of systematic, needs-oriented training programs integrating theory with hands-on practice for comprehensive pedagogical transformation.

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This study investigates how rural teachers in China experience online professional development (OPD), focusing on the affective, motivational, and structural dimensions of their engagement. Drawing on in-depth interviews with 30 rural primary and secondary school teachers, this study employed sentiment analysis and thematic coding to explore participants’ emotional orientations, sources of motivation, and perceived barriers. While teachers generally expressed positive attitudes toward OPD, their actual experiences revealed notable tensions between optimism and disconnection. Three interrelated findings emerged: (a) a mismatch between favorable attitudes and unsatisfying learning encounters; (b) participation driven more by external mandates than by internalized professional goals; and (c) multiple constraints embedded in training design, institutional policies, sociocultural environments, and digital competence gaps. Based on these insights, the study proposes three conceptual constructs—affective-structural dissonance, controlled engagement, and layered constraint ecology—to explain the complex interplay between emotional readiness and systemic limitations. These findings contribute to the literature on distributed learning by emphasizing the need for motivation-sensitive and context-responsive OPD design, particularly in underserved rural settings.

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The purpose of this study was to develop and validate an effective instrument to measure undergraduate students’ self-efficacy in engaging with information for online coursework. Recognizing the increasing importance of information literacy in online learning environments, the Online Information Literacy Self-Efficacy (OILS) instrument was designed to assess key competencies associated with students’ abilities to engage with information effectively. The validity and reliability of the OILS instrument were examined using exploratory factor analysis (EFA) and reliability analysis. The instrument initially comprised 30 items, grounded in two authoritative frameworks: the Association of College and Research Libraries (ACRL) Framework for Information Literacy and the Association of American Colleges and Universities (AAC&U) Information Literacy VALUE Rubric. Survey responses from 259 undergraduate students enrolled in online courses at a large Midwestern US R1 institution were analyzed using EFA and reliability analysis. EFA results revealed a four-factor structure explaining substantial variance in the item response patterns: scoping research topics, obtaining information, evaluating information quality and producing research documents, and crediting sources. These four factors demonstrated high reliability, with omega coefficients ranging from 0.891 to 0.960. One item was recommended for removal due to cross-loadings and skewness. Exploratory measurement invariance analyses suggested that the factor structure was comparable for students with and without prior online learning experience. The findings support the OILS instrument’s utility in measuring students’ confidence in applying information literacy skills in online learning contexts, offering valuable insights for educators and librarians to enhance instructional strategies and support student success.

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Open high schools fulfill a critical function by offering flexible educational pathways for students encountering diverse socioeconomic and personal challenges. Nevertheless, escalating dropout rates represent a significant concern, necessitating robust predictive models to facilitate early interventions. This study employed educational data mining (EDM) techniques to analyze the academic trajectories of 484,158 students enrolled in Turkish open high schools. We evaluated multiple classification algorithms—J48, decision tree, k-nearest neighbors (kNN), naïve Bayes, and random forest—across four distinct data preprocessing scenarios to predict student status (dropout/delayed graduation/graduation). The J48 algorithm demonstrated superior performance, achieving an accuracy of 80.47% and a kappa statistic of 0.61. Key findings reveal that academic and administrative features, notably total credit accumulation and initial enrollment type, are more important predictors than demographic variables. This research provides empirically grounded insights for the early identification of at-risk students. It offers data-driven recommendations to enhance student retention policies within open high school systems, contributing a large-scale, multi-class prediction analysis within a unique, under-researched national distance education context.

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The use of technology in healthcare education is rapidly expanding. Undergraduate nursing programs have integrated advanced technologies such as high-fidelity simulation, immersive virtual reality, augmented reality, and holographic simulations to support learning and improve retention in both in-person and distance learning. Meanwhile, little is known about the use of these clinical learning technologies in nurse practitioner education. This integrative review involved a systematic search of five databases: CINAHL, ERIC (ProQuest), MedLine (OVID), Google Scholar, and PubMed. Eleven studies met the inclusion and exclusion criteria and were analyzed for major themes. Five primary themes emerged, including (a) student uptake, (b) student learning, (c) change management, (d) accessibility, and (e) confidence. Findings suggested that integrating advanced clinical learning technologies into nurse practitioner programs should be prioritized. Going forward, focus should turn to establishing nurse practitioner program best practices for integrating clinical learning technologies, budgetary considerations, ongoing evaluation of learning outcomes, and theory development. Work in these areas may support the enhanced use of clinical learning technologies while providing educational institutions, nurse practitioner programs and public funders with the necessary data to inform future planning and further implementation of new technologies.

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With the increasing application of learning analytics in online learning communities, network analysis methods have emerged as powerful and efficient tools for deepening our understanding of collaborative learning processes, especially within the community of inquiry (CoI) framework, offering insights that extend beyond the examination of interaction patterns. This systematic review synthesizes findings from 43 empirical studies, with a focus on the application of social and epistemic network analysis methods (SNA and ENA) in CoI research in higher education contexts. Key findings highlight the value of SNA and ENA in investigating interaction patterns, refining CoI theory, examining community formation and evolution, analyzing discourse and knowledge construction, tracing the development of CoI presences, and identifying SNA measures relevant to CoI presences. This review also identifies the current challenges and opportunities in using SNA and ENA, as well as the potential for integrating both methods in future CoI research. By revealing both the strengths and limitations of existing studies, this review offers actionable implications for researchers, educators, and instructional designers aiming to enhance the design and analysis of online learning communities.