Frontiers in Education: Digital Learning Innovations
12 hours ago
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.
Lusanda Mavenge
12 hours ago
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.
Jimin Lee
12 hours ago
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.
Hesham Aldamen
1 day 10 hours ago
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.
Meliana Christianti Johan
6 days 10 hours ago
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.
Marcela Núñez-Solís
1 week ago
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.
Ayda Abdulkareem AL-Eidan
1 week 1 day ago
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.
Bilal Şimşek
1 week 2 days ago
Sami A. Issa
1 week 3 days ago
José Gijón Puerta
1 week 3 days ago
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.
Khalid A. Bin Abdulrahman
1 week 6 days ago
Shakhislam Laiskhanov
1 week 6 days ago
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.
Sam A. Leif
1 week 6 days ago
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.
Yanbin Huang
1 week 6 days ago
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.
Bookyung Shin
2 weeks 1 day ago
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.
Matthew Christian Agustin
2 weeks 1 day ago
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.
Gulzhan Zholaushievna Niyazova
2 weeks 2 days ago
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.
Michael Haug
2 weeks 3 days ago
IntroductionThis study examines heterogeneity in the mediating role of self-efficacy between prior AI training and adoption intentions among pre-service mathematics teachers.MethodsUsing data from 79 pre-service teachers at the University of the Free State, South Africa, Bayesian moderated mediation analysis was employed to assess whether this pathway operates uniformly across demographic subgroups.ResultsFindings revealed pronounced heterogeneity: the indirect effect was strong for female participants (indirect effect = 0.311, P(>0) = 94.8%) but negligible for males (indirect effect = −0.064, P(>0) = 37.8%). Additionally, self-efficacy predicted intentions more strongly among untrained (β = 0.746) than trained teachers (β = 0.195).DiscussionThese results suggest that training may homogenise intention formation and that self-efficacy operates differently across subgroups. The findings challenge uniform models of technology adoption and highlight the need for differentiated, context-sensitive teacher education strategies
Moeketsi Mosia
2 weeks 3 days ago
AI-based technologies have successfully proliferated across various levels of education, from higher to elementary education. The most apparent implementation of AI in education can be traced directly to higher education, which has a number of potential application areas. Despite its popularity at the higher education level, its application in the context of elementary education remains scarce in the literature. Investigating the challenges associated with implementing AI-based technologies in elementary education is equally important as that of the widely tackled field in higher education. Along this line, this paper intends to explore the challenges brought about by AI in elementary education using the fuzzy best-worst method. A case study in a cluster of elementary education institutions in Cebu City, Philippines, is conducted, and interesting results reveal that stakeholders prioritize addressing the generalizability of the data mining model prior to the actual adoption of AI in elementary education.
Catherine Gabia
2 weeks 3 days ago
IntroductionThe inclusion of a system dynamics course in our medical school curriculum was designed to encourage systems thinking through computational modeling. From anecdotal observations, it soon became evident that something more profound was occurring—rather than simply learning, our students appeared to be constructing knowledge by building computational models in a way that is consistent with Papert's constructionism.ApproachIn the absence of a reliable tool to identify constructionism, we examine the seminal literature of Forrester's system dynamics and Papert's constructionism by extracting key excerpts to look for evidence supporting the hypothesis that computational modeling may constitute a constructionist activity.ObservationsThe literature suggests that there is substantial convergence between the educational approach of constructionism and the activity of constructing models in system dynamics.DiscussionAn examination of the seminal literature suggests that system dynamics modeling has features that are consistent with a constructionist approach. By extension, other approaches such as agent-based modeling also embody constructionist principles, and the expanding integration of artificial intelligence into computational modeling may present opportunities for novel approaches to constructionist learning. Formal real-world educational studies will be required to accumulate empirical learner data in order to confirm the constructionist nature of systems modeling.
David M. Rubin