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The convergence of educational technologies, ergonomics, and active learning frameworks offers a multidimensional approach to improving educational outcomes. This study examines the role of Learning Analytics (LA) in optimizing ergonomic educational spaces to support active learning within the higher education context of Kazakhstan, however the outcomes may equally be applied to neighboring countries. Addressing a gap between ergonomic design principles and data-driven educational practices, the study adopts a mixed-methods approach, combining quantitative and qualitative data collected from multiple institutions in Kazakhstan. Key Learning Analytics indicators were analyzed alongside parameters derived from ergonomic design frameworks to explore their relationship with active learning processes. The findings reveal statistically significant associations between selected Learning Analytics metrics and ergonomic features of learning environments, highlighting how data-informed spatial design can enhance student engagement and participation. These results underscore the importance of integrating technological and physical learning environments within a context characterized by ongoing higher education modernization and increasing adoption of digital tools. While the study provides empirically grounded insights relevant to institutional development in Kazakhstan, the findings are interpreted as context-sensitive rather than universally generalizable. Nevertheless, they offer potential implications for educational systems with similar structural and technological conditions (such as the countries like Uzbekistan, Kyrgyzstan, etc.)provided that adaptations are made to local or similar contexts. This study contributes to the growing body of research on Learning Analytics by extending its application beyond curriculum and assessment into the design of physical learning environments. It further emphasizes the need for context-aware, interdisciplinary strategies to support active learning in diverse educational settings.

IntroductionRapid advances in digital technology have significantly increased the auditing industry's demand for interdisciplinary talent. However, current digital-intelligent auditing courses in higher education still face prominent challenges, including fragmented content, weak connections between modules, and unclear relationships among knowledge points. To address these issues, this study introduces knowledge graph technology into the construction of an auditing curriculum system.MethodThis study first clarifies the core principles of curriculum development and proposes a systematic construction path from four dimensions: knowledge graph building, scope control, learner participation, and dynamic maintenance. Based on this framework, the curriculum content was optimized, the curriculum system was restructured, teaching methods were innovated, and practical teaching was strengthened. In addition, a controlled teaching experiment was conducted among auditing majors at a university to evaluate the effectiveness of the proposed approach.ResultsThe results show that the class adopting the knowledge graph-based curriculum achieved significant improvements in learning efficiency, academic performance, practical operational ability, and autonomous learning behavior compared with the class using the conventional approach.DiscussionThese findings indicate that knowledge graph-based curriculum design can effectively integrate interdisciplinary content, rationalize teaching logic, and enhance learning outcomes. This study provides a referable implementation model and practical evidence for the reform of digital-intelligent auditing courses in colleges and universities.

With the increasing integration of artificial intelligence (AI) across industries, there is a growing need to transform traditional teaching methods into more innovative, technology-driven, and practice-oriented approaches. Project-Based Learning (PBL) has emerged as an effective pedagogy that promotes active learning, connects theoretical concepts to real-world applications, and enhances critical thinking and problem-solving abilities. This study evaluates the effectiveness of a structured PBL framework implemented through the Technoscope program in an undergraduate engineering context using an integrated assessment approach. Data were collected from 58 to 60 students using a structured questionnaire based on a five-point Likert scale administered before and after the intervention. The instrument was validated using the Content Validity Index (CVI). In addition to student perceptions, project outcomes were assessed through rubric-based evaluation by domain experts to provide complementary performance insights. Descriptive and inferential analyses revealed a significant improvement in student outcomes, with mean scores increasing from 3.4 (SD = 0.7) under traditional teaching methods to 4.5 (SD = 0.4) following PBL implementation. Statistically significant gains were observed across key dimensions, including overall learning experience, conceptual understanding, creativity, and problem-solving skills (p < 0.001), with moderate to large effect sizes. A majority of students reported enhanced creativity (85.7%) and improved understanding of subject content (82.5%), while 60.3% expressed satisfaction with the overall learning experience. The overall mean score of 4.41 (SD = 0.86) indicates high engagement and positive learning experiences. Despite these findings, the results are primarily based on self-reported data and are limited by the absence of a control group and single-institution context. Future research should incorporate objective performance measures, longitudinal designs, and multi-institutional samples to strengthen the evidence base.

The digital transformation of higher education has redefined pedagogical paradigms, particularly in postgraduate science programs, where disciplinary content demands adaptive, technology-enriched instructional frameworks. Grounded in the Technological Pedagogical Content Knowledge (TPACK) framework, this study examines the impact of integrated online pedagogies on the development of competence in science postgraduate students. Using a thematic qualitative literature review design, which is guided by PRISMA 2020 reporting principles and employing a thematic synthesis of peer-reviewed literature, the research interrogates current scholarship on TPACK-based practices in virtual science education. The review elucidates four dominant themes: pedagogical design innovation, educator technological preparedness, learner engagement, and competence enhancement. Evidence suggests that strategic TPACK integration promotes conceptual understanding, research capabilities, and the application of scientific knowledge in an authentic process. Conversely, challenges such as digital inequity, insufficient professional development, and misalignment constrain effective implementation. The study contributes to digital pedagogy scholarship by advancing a synthesised perspective on how TPACK-informed online teaching mediates higher-order learning and supports evidence-based pedagogical reform. Implications are offered for educators, curriculum developers, and policymakers seeking to cultivate technologically competent, research-oriented science graduates through purposeful digital pedagogy.

The growing presence of generative artificial intelligence (GenAI) in educational practice raises questions about how learners and educators live with AI and reshapes questions about how learning unfolds across contemporary learning environments. Although research on AI in education has expanded quickly, much of this work continues to frame GenAI in technocentric and static terms, as a tool to be evaluated, rather than as a relational and developmental presence within learners' trajectories. Drawing on constructionist learning theory and post-Cartesian metatheoretical perspectives, this mini review suggests that such framings are insufficient for understanding how learning is enacted in AI-mediated contexts. We propose that GenAI must be conceptualized as a co-constructive presence within sociotechnical learning ecologies. Considering constructionism within a metatheoretical framework embracing process-relational ontology, we highlight implications for studying learning as an emergent and contextually situated process. Methodologically, this reframing calls for process-sensitive approaches that attend to temporality, interaction, and the negotiation of agency and identity. Such approaches include embodied and design-based research, post-phenomenological methods, and positioning analysis, each suited to capturing how learners come to live with GenAI over time.

Grounded in a metacognitive and distributed-scaffolding framework, this quasi-experimental study examined classroom-level patterns associated with two configurations of reading strategy instruction and with business-as-usual instruction in a university EFL context. Sixty undergraduate students enrolled in an advanced reading course at a public university in Jordan participated in the study. To preserve natural classroom composition, three intact course sections with 20 students each were assigned at the class level to one of three conditions: teacher-mediated AI-assisted strategy instruction, teacher-led strategy instruction, or business-as-usual instruction without explicit strategy training. The AI-assisted section used ChatGPT as a scaffold for previewing, predicting, monitoring, questioning, inferencing, and summarizing within a technology-equipped classroom and with one short weekly AI-supported task; the teacher-led section addressed the same strategies through instructor modeling and guided practice; the comparison section followed the regular course routine. Reading comprehension was measured with an adapted 20-item, 60-point test, and metacognitive awareness was measured with a study administration version of the Metacognitive Awareness of Reading Strategies Inventory. Descriptive statistics were the primary analytic lens. Student-level ANCOVA and t-test results are reported as exploratory summaries because each condition was represented by a single intact section. The two explicit-strategy sections showed stronger reading-comprehension patterns than the business-as-usual section, and the AI-assisted section showed the highest adjusted posttest mean. For metacognitive awareness, both explicit-strategy sections improved from pretest to posttest, and the AI-assisted section showed the largest descriptive gain. The findings suggest that a teacher-managed AI-supported instructional package may extend explicit strategy instruction without displacing teacher judgment, but they should be interpreted as section-level comparative evidence rather than as isolated treatment effects. The study contributes a semester-long, classroom-level comparison in university EFL reading and clarifies how AI can be positioned as a complement to explicit strategy teaching.

IntroductionAI can automate technical activities, but it cannot teach skills that only people have. This study proposes the TISE-VALORIZE framework and presents a preliminary empirical examination of its association with student performance outcomes in digital engineering education.MethodsA posttest-only quasi-experimental study with non-equivalent cross-cohort groups involved 138 undergraduate engineering students: one intervention group implementing TISE-VALORIZE (n = 53) and two control groups receiving conventional instruction (n = 42; n = 43). Student performance was evaluated through Structured Academic Activities assessed using a Bloom's Taxonomy-aligned rubric.MethodsA posttest-only quasi-experimental study with non-equivalent cross-cohort groups involved 138 undergraduate engineering students: one intervention group implementing TISE-VALORIZE (n = 53) and two control groups receiving conventional instruction (n = 42; n = 43). Student performance was evaluated through Structured Academic Activities assessed using a Bloom's Taxonomy-aligned rubric.DiscussionThese preliminary results suggest that the framework may support more consistent learning outcomes. However, direct measures of cognitive load, motivation, and related psychological processes were not included in the present study. These findings provide preliminary support for the framework's potential to improve performance consistency, while larger-scale studies with direct measures of cognitive and motivational processes are needed.

This study presents a scoping review on the use of digital technologies in higher education from 2019 to 2023, aiming to analyse their incorporation into formative processes and their impact on teaching and assessment in higher education. Using the PRISMA ScR methodology, 11 empirical studies were selected from the Web of Science and Scopus databases, following 4 eligibility criteria. The results reveal that digital technologies enhance autonomy and self-regulation in learning, as well as competency-based assessment. The use of tools such as gamification apps, virtual learning environments, and software for creating multimedia content is highlighted. The conclusions emphasize the importance of continuing research in critical areas such as digital inclusion and academic integrity, particularly in the university context.

The rapid integration of artificial intelligence (AI) into educational environments has created an urgent need for students to acquire the competencies required to effectively employ AI applications in teaching and learning contexts. However, limited evidence exists regarding the extent to which higher education students possess these competencies and the role of educational technology courses in developing them, particularly within the Kuwaiti context. Accordingly, this study examines the level of AI-related competencies among students at the College of Basic Education in the State of Kuwait and investigates the effectiveness of educational technology courses in enhancing these competencies. A descriptive-analytical methodology was adopted, employing a 35-item questionnaire distributed across three domains: cognitive, performance-based, and applied competencies. The sample comprised 445 students, including 83 students majoring in educational technology and 362 students from other specializations. The findings indicated that students demonstrated a moderate overall level of AI competencies (53.8%), with statistically significant differences favoring students enrolled in educational technology programs. These findings highlight the importance of revising educational technology curricula to incorporate advanced AI applications, as well as introducing dedicated AI modules across academic disciplines.

IntroductionThe current literature on Virtual Reality (VR) reports promising findings in teaching productive language skills; however, important gaps remain. Evaluating these differences and drawing general conclusions across different conditions will inform future studies examining the impact of VR on the improvement of productive language skills. Therefore, this research aims to comprehensively examine the effects of VR-supported interventions on productive language skills through a meta-analysis.MethodsThis meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Experimental and quasi-experimental studies published between 2015 and 2025 were included in the scope.ResultsA summary of 21 studies involving 2,503 participants showed that VR applications have a moderately positive and significant effect on productive language skills (g = 0.538). This result means that VR interventions significantly support language learners’ productive language skills. The moderator analysis showed that the moderators’ language type, target language, target language skills, learner educational level, intervention duration, and intervention setting have no significant effect. However, the “control treatment” moderator was statistically significant.DiscussionAs a conclusion, the research suggests that VR significantly affects the development of productive language skills through interactive, context-sensitive environments.

BackgroundArtificial intelligence (AI) is increasingly reshaping medical education through personalized learning, adaptive assessments, and advanced simulations. This systematic narrative review synthesizes the theoretical development of AI in medical training, focusing on educational models, frameworks, learning outcomes, and stakeholder considerations.A literature search of PubMed, Scopus, Web of Science, and Google Scholar (January 2000–March 2025) identified 1,288 records, of which 48 studies met the inclusion criteria and were included in qualitative thematic synthesis. No statistical meta-analysis was conducted due to methodological heterogeneity.ResultsFive major AI domains emerged: Intelligent Tutoring Systems, Simulation-Based Medical Education, Adaptive Learning, Generative AI, and Explainable AI. These domains align with established instructional theories and contribute to improved engagement and learning efficiency. However, concerns persist regarding learner deskilling, academic integrity, and algorithmic bias. AI integration influences multiple stakeholders, including trainees, educators, clinicians, policymakers, and patients. The field has progressed from rule-based approaches to data-driven machine learning models, enabling personalized instruction. Responsible implementation necessitates addressing pedagogical, ethical, and practical challenges, while also reducing the global digital divide.ConclusionsThis systematic review provides guidance for educators, researchers, and policymakers on integrating AI effectively and ethically into medical education.

IntroductionExpertise in video games is commonly stratified using self-reported experience, which is prone to bias and measurement error. This study introduces a behavioral observation approach to provide a more objective and psychometrically sound method for differentiating expertise. Specifically, the Behavioral Observation Matrix-Proxemics (BOM-Proxemics) was developed to assess expertise through observable in-game behaviors grounded in digital proxemics theory.MethodsUsing recorded gameplay from Apex Legends, 102 players were evaluated as novice, competent, or expert based on in-game observed behaviors. The BOM-Proxemics measured behaviors across four domains: Spatial Positioning, Spatial Realization, Spatial Appropriation, and Spatial Interactivity. Reliability and validity were evaluated through inter-observer agreement, internal consistency, and criterion-related analyses. Predictive relationships were examined using ordinal logistic regression, and group differences were analyzed using ANOVA.ResultsThe BOM-Proxemics demonstrated strong reliability and validity, including excellent inter-observer agreement and high internal consistency. Total scores were a significant positive predictor of in-game rank. Among subscales, Spatial Positioning, Spatial Appropriation, and Spatial Interactivity significantly predicted rank, while Spatial Realization did not. Significant differences in scores were observed across all expertise groups, with pairwise comparisons indicating clear separation between novice, competent, and expert players.Discussion/ConclusionFindings support the BOM-Proxemics as a psychometrically sound, behavior-based measure of video game expertise. The results demonstrate the instrument's ability to differentiate expertise levels and predict performance outcomes, offering a viable alternative to self-report measures. Implications include advancing expertise research through direct observation and extending behavior-based assessment approaches to other complex digital environments.

Foreign language speaking anxiety remains a major affective barrier in EFL oral communication, particularly in classroom speaking activities. This study investigated the effects of an AI-supported speaking intervention on EFL learners' oral proficiency, speaking anxiety and learner's perception across different task types. Adopting a mixed-method quasi-experimental design, 83 Chinese university students participated in an AI-supported speaking intervention, with follow-up interviews conducted with 12 participants. Quantitative results showed that the experimental group outperformed the control group in oral proficiency (t = 2.81, p = 0.006). No significant difference was found in overall foreign language speaking anxiety between groups. Task-based analyses revealed that AI conversational agent reduced situational speaking anxiety in pair work and presentation tasks (p < 0.001), but not in debate or storytelling. Qualitative findings further showed that learners perceived AI conversational agents as providing scaffolded and adaptive support through personalized feedback, prompting, and repetition in a non-judgmental environment, which facilitated oral development and reduced performance-related anxiety. Overall, the findings suggest that AI conversational agents support oral performance and selectively alleviate situational anxiety, while their effectiveness remains contingent on task structure and pedagogical design.

Notetaking with digital devices during asynchronous online learning remains controversial. This study investigated how three writing devices—ink pens, digital pens, and laptops—interact with active (verbatim) vs. constructive (question) notetaking strategies during video lectures. During a within-design laboratory experiment, EEG data was recorded while 33 undergraduate students took notes for learning sessions using different notetaking devices and strategies, followed by immediate post-tests. Time-frequency analysis revealed significant differences in theta, alpha, beta, and gamma band power across devices, and significant interaction effects in beta, gamma, and theta/beta values. Both pen types showed higher alpha, beta, and gamma power and lower theta/beta ratios compared to keyboards, particularly in occipital regions associated with sustained visual attention. However, interaction effects indicate the importance of notetaking strategy, and immediate post-test performance showed no significant differences across conditions. The findings suggest that notetaking media influence learning processes and attention sustainment differently, though immediate performance outcomes remain similar. This has implications for designing asynchronous online learning environments and guiding notetaking practices in those settings.

The growing presence of artificial intelligence (AI) across educational and workplace environments is reshaping how learners encounter tasks, interpret feedback, and navigate uncertainty. To understand these changes, this manuscript grounds AI's influence in theories of self-regulated learning (SRL), which conceptualize learning as a cyclical process of planning, monitoring, strategic adjustment, and reflection. Rather than replacing these processes, AI reshapes the conditions under which they occur by making some cues more visible, introducing new forms of guidance, and occasionally preempting difficulty before learners have an opportunity to engage with it. These shifts reveal a conceptual gap: although research documents both benefits and risks of AI-mediated support, we lack a framework for understanding how AI participates in learners' regulatory cycles across educational and professional settings without eroding the autonomy that underpins SRL. To address this gap, this article proposes a unified model of AI as a co-regulator within self-regulated learning, grounded in Winne and Hadwin's COPES architecture. The model centers productive metacognitive friction as a mechanism for sustaining learner-driven regulation by structuring how learners encounter challenge and discrepancy. It advances a relationally grounded framework at the level of interactional structure, positioning AI as a co-regulator through five design principles that specify conditions under which AI can support regulatory cycles without displacing learner judgment. These principles are linked to an evaluation architecture that centers autonomy, interpretability, process integrity, and developmental growth as evaluative priorities traced through learner–AI interaction patterns. Implications are examined across educational practice, workplace learning, equity, and governance, and directions for collaborative research and design are outlined to investigate how relationally aligned AI can preserve and strengthen the regulatory processes at the heart of SRL.

BackgroundGamified, AI-enabled digital learning environments (GAI-DLEs), integrating adaptive or generative AI with game-based design, are increasingly used in school science education to support personalized learning. However, consolidated evidence on their effectiveness, implementation models, and regional distribution remains limited, particularly in Central Asia.MethodsFollowing PRISMA 2020 guidelines, a systematic search of Scopus and complementary databases identified peer-reviewed empirical studies published between 2015 and 2025. A total of 1,284 records were identified. After deduplication, 962 unique records were screened at the title and abstract level, and 150 full-text articles were assessed for eligibility. Ultimately, 81 studies met the inclusion criteria and were included in the final synthesis. The included evidence was examined through trend analysis, thematic synthesis, and geographic mapping. The meta-analysis included experimental and quasi-experimental studies from formal school settings. Standardized mean differences were estimated using random-effects models, with subgroup analyses by education level, AI technology type, publication period, study quality, and region.ResultsGAI-DLEs demonstrated a significant positive effect on science learning outcomes compared with non-AI instructional conditions (SMD = 1.01; 95% CI: 0.69–1.33; p < 0.001). Effects were stronger in secondary education (SMD = 1.12; 95% CI: 0.69–1.55) than in primary education (SMD = 0.80; 95% CI: 0.35–1.25). Studies employing adaptive or generative AI systems tended to report larger effect sizes. Evidence regarding student engagement was generally positive but showed substantial contextual heterogeneity.ConclusionGAI-DLEs show consistent potential to improve science learning in school contexts. However, the global evidence base remains geographically imbalanced, with Central Asia substantially underrepresented. Future research should adopt theory-driven and longitudinal designs to examine how specific combinations of AI functionalities, gamification mechanics, and classroom integration strategies produce scalable educational outcomes.

IntroductionVirtual laboratories (vLabs) are increasingly used in engineering education to support preparation for complex experimental work. We report the implementation and exploratory evaluation of a serious-game-inspired vLab that mimics a biomechatronics device, the MyoRobot, within a Master's-level course.MethodsThe Unity-based desktop simulation combines a realistic 3D laboratory, an interactive technical anatomy model, and a pipetting and operation workflow that links virtual actions to plausible force-recording outcomes. In an initial cohort (N = 8), students prepared either using a written manual (n = 4) or primarily the vLab (n = 4). Open-ended questionnaires administered before and after the physical lab assessed perceived preparedness, confidence, engagement, and overall user experience.ResultsvLab users reported improved conceptual orientation and procedural confidence, along with greater independence in handling sensitive equipment. However, they also noted a higher perceived time investment, a reduced novelty effect during the physical lab, and a desire for more specific feedback.DiscussionWe interpret these findings as context-specific insights from a pilot study and derive design implications for realistic vLabs that aim to balance guidance, workload, and authenticity in engineering education.

Social network analysis, as one of the social learning analytics (SLA) methods, have been combined with other analytical methods to understand social learning processes from a research perspective. However, few studies have devised the SLA tools to provide learning interventions. Filling this gap, this design-based research devised a student-facing SLA tool with the multi-method analytics to demonstrate network representations in China’s higher education context, with an expectation to foster student engagement. A multi-method approach was used to examine the effect of this tool on fostering students’ social, topic, and cognitive engagement in online collaborative discussions. Results showed that the SLA tool did not increase student engagement significantly. But the social network worked better for facilitating students’ social, topic, and cognitive engagement, compared to the topic and cognitive networks. Based on the empirical results, this research provided tool design and pedagogical implications to improve design and implementation of SLA tool in higher education.

Despite an increased understanding of the importance of student data to inform higher education teaching, little is known about how university faculty make sense of and use student data dashboards to inform their instruction. Through the lens of sensemaking theory, we explore how instructors navigate these tools and what challenges they experience during this process. Our findings suggest that faculty recognize the importance of student data in developing their courses, particularly to foster an inclusive and effective learning environment. However, there are a number of obstacles that arise when using student data dashboards. Study participants highlighted the limitations of the student data available to them, as well as multiple layers of support that are needed to ensure an understanding and appropriate use of the data. This research also revealed a common sentiment regarding the university’s responsibility to partner with faculty on student data and instruction-related issues. Overall, this study uncovers how faculty can be better equipped and supported in using data analytics tools towards the goal of improving student learning experiences and outcomes.

Blended or online courses (BOC) present unique challenges for students compared to traditional face-to-face learning environments. Such challenges may have an impact upon student persistence. The objective of this study was to identify factors contributing to student persistence in BOC. Structural equation modeling was used to examine the relationships between the predictor variables (a) community of inquiry presences, (b) learner autonomy, and (c) satisfaction with the dependent variable student persistence. Convenience sampling was used and a total of 348 students, enrolled in BOC at a post-secondary institution in the French-speaking region of Quebec, Canada, completed an online questionnaire. The results showed that student persistence in BOC can be explained by teaching and cognitive presence, by learner autonomy, and by student satisfaction. The full model, including all predictor variables, explained 23.6% of the variation in student persistence.

This study presents a conceptual replication of Moreno’s (Appl Cogn Psychol 21:765–781. 10.1002/acp.1348, 2007) study on the benefits of adhering to the segmentation principle when utilizing multimedia learning objects. Furthermore, this study expands upon the original by taking place in a low-immersive virtual reality environment, allowing for further understanding on the extent to which multimedia principles are still relevant. Both a synchronous and an asynchronous case are presented. Results indicate benefits for both cases in far transfer of learning. Furthermore, synchronous learners indicated a significant reduction in cognitive load and increased overall attitudes towards learning due to segmented instruction.

Online laboratories have gained a great deal of interest in recent years with benefits including reduced costs, support for increasing student numbers, increased flexibility and accessibility to practical work for students attending distance learning courses or with physical disabilities. However, designing teaching and learning activities for online laboratories introduces new challenges because many learning aspects that are inherent in conventional laboratories (e.g. safety, ethics, motor skills etc.) must be explicitly designed into online laboratories. This research aims to assist educators to design Science, Technology, Engineering and Mathematics (STEM) online laboratories that develop a broad range of learning objectives to meet students’ educational needs. In this paper a framework for STEM online laboratory learning objectives is introduced, building on previous approaches in the literature. The framework provides a structured approach to help course designers and educational technologists to design and assess the learning objectives and design characteristics of online experiments. The framework was used to map 23 online laboratories at a large distance learning university, and the results identified some trends and gaps in learning objective coverage. The results highlight the importance of defining the full breadth of learning objectives for online experiments at the design stage to ensure that the experiment is appropriately designed to allow students to achieve the desired learning outcomes. Furthermore, different online experiment designs are appropriate to different learning objectives, so care must be taken to select the most appropriate delivery mechanism for the online laboratory. It is proposed that the framework could be used by educators to support the design of new online laboratories as well as evaluating the laboratory learning objectives coverage in existing online laboratories.

Artificial intelligence-generated content feedback (AIGCF) has become increasingly valuable in the field of learning. Although research exists on AIGCF’s effectiveness, with some studies showing improved student writing and others showing minimal or negative effects, their overall impact remains unclear. This study aimed to examine the effect of AIGCF, exemplified by ChatGPT-4, on non-native English students’ writing quality and evaluate the quality of AIGCF itself. We conducted a single-group experiment with undergraduates. Thirty-two participants completed a series of writing tasks over ten weeks and received AIGCF for their work. We assessed the writing quality based on syntactic complexity, lexical complexity, accuracy, and fluency. We also evaluated the quality of AIGCF with respect to criteria-based feedback, clarity of improvement directions, accuracy, prioritization of essential features, and supportive tone. Preliminary findings suggested that AIGCF might be useful in influencing syntactic and lexical complexity, but its impact on improving accuracy and fluency was variable. The study revealed strengths and weaknesses in the quality of AIGCF, with criteria-based feedback emerging as a notable strength. The study also showed that the quality of feedback based on criteria and the clarity of suggestions for improvement got better over time. However, the prioritization of essential features, the accuracy of the feedback, and the tone of support decreased. It was concluded that the effectiveness of AIGC varies depending on the specific writing area. This study provided valuable insights into the potential of AIGCF in writing instruction and highlighted areas for future research.

This study investigated the relationships among socio-emotional climate, positive interdependence, and group outcome among pre-service teachers participating in an eight-week online collaborative instructional planning (CIP) project. Furthermore, it examined the moderating effects of group composition – gender and group history – on these relationships. Participants completed the Group Processes Scale assessing their perceptions of socio-emotional climate and positive interdependence. Considering the nested data structure, hierarchical linear modeling (HLM) analyses were conducted to predict socio-emotional climate, positive interdependence, and group outcome. The study revealed a significant and strong positive relationship between socio-emotional climate and positive interdependence, indicating that each mutually enhances the other in online CIP. Both socio-emotional climate and positive interdependence were significant predictors of group outcome, while their effects were especially evident in “all-male” groups, as well as in groups with no prior collaboration history, suggesting that group composition factors can amplify the benefits of group dynamics. These findings underscore the importance of fostering both positive interdependence and a strong socio-emotional climate, while strategically considering group composition to enhance the success of online CIP.