Frontiers in Education: Digital Learning Innovations

This study explores the potential of eye-tracking technology combined with artificial intelligence (AI) to identify early reading disabilities (RD). Findings indicate significant differences in fixation patterns between elementary students with and without RD, suggesting eye-tracking as a promising screening tool for classroom use. Students’ reading failure is a systemic problem across the United States. Early reading interventions show promise in addressing reading disabilities (RD); however, traditional measures are costly, time-consuming, and unreliable. Recent researchers conclude that AI tools, such as eye-tracking, could detect RD for earlier intervention. This study investigated the potential impact of eye-tracking data during a Lexplore reading screening to determine if differences existed between the (a) average fixation time and (b) proportions of fixations to total stimuli duration while reading with 12 elementary students with RD and 17 students without RD. Although Lexplore incorporates AI and previously trained machine learning algorithms, the present study's goals were not to train, modify, or validate AI models. Instead, researchers analyzed pre-generated eye tracking output using traditional statistical methods to examine group differences regarding reading difficulties. This study showed statistically significant differences between groups in both average and proportions of eye fixations. The findings from this exploratory study indicate a need for further investigations regarding eye-tracking devices to screen, identify, and monitor elementary students potentially at risk for RD.

IntroductionMassive Open Online Courses have expanded access to higher education, yet persistent challenges related to learner engagement and interaction continue to limit their educational effectiveness. This study investigates lecturer readiness to facilitate Massive Open Online Courses by conceptualizing readiness as a form of interactive communication competence.MethodAdopting a convergent parallel mixed-methods design, the study draws on survey data and open-ended responses from 124 university lecturers in Indonesia.ResultsThe quantitative results indicate that lecturer readiness comprises multiple interrelated dimensions, with pedagogical, social, and communication-related competencies emerging as more salient than technical competence alone. Cluster analysis further reveals distinct readiness profiles, demonstrating substantial variation in lecturers’ capacity to facilitate interaction at scale, while experience-based analyses show that prior engagement with Massive Open Online Courses is associated with higher pedagogical and social readiness. Qualitative findings complement these patterns by showing that lecturers perceive online collaborative learning as beneficial for learning quality and flexibility, alongside constraints related to participation, facilitation workload, and institutional support.DiscussionOverall, the findings suggest that effective facilitation in Massive Open Online Courses is fundamentally communication-centered, underscoring the importance of interactive communication competence in shaping lecturer readiness for large-scale online learning.

AI-Chatbot writing tools like ChatGPT are becoming rampant in academic writing due to complex academic writing standards. However, the adoption of ChatGPT in academic writing poses challenges, including potential impairments to academics’ critical thinking, creativity, and originality, as well as ethical and policy concerns, particularly within Africa's diverse cultural and linguistic landscape. Therefore, this study seeks to examine the benefits and challenges of adopting ChatGPT in an academic environment from the perspective of African academics. Using a quantitative research method via online questionnaires administered to academics across African universities, the study found that most respondents believe that the benefits of using ChatGPT in academic writing outweigh its risks. The study also identifies the necessity for policies that recognize the context, ethical education, and processes for responding to academic ill-ethicality or biases, to support responsible use. It was found that issues surrounding ethics and building frameworks that positioned ChatGPT more integrative, responsive, communicative, and collaborative are more likely to lead to sustainable engagement with and use of ChatGPT, as opposed to addressing the more ethical issues.

Background/objectiveGenerative artificial intelligence is profoundly transforming the field of nursing. Nursing education needs to make corresponding progress to cultivate nursing personnel who can adapt to the technological environment. Conduct a scoping review on the application of generative artificial intelligence in cultivating research literacy among nursing graduate students to provide a reference for future paradigm shifts in graduate education.MethodFollowing the methodological framework of scoping reviews, relevant studies were systematically retrieved from Chinese and English databases. The search period spanned from the inception of the databases to January 10, 2026. Two researchers independently screened and extracted data, and summarized and analyzed the included literature.ResultsA total of 12 articles were included. The application of Gen AI in nursing graduate research literacy training primarily encompasses paper writing and revision, enhancing innovative and critical thinking skills, and improving learning and research efficiency. Nevertheless, caution is still required regarding information accuracy and ethical safety.ConclusionGen AI may play a positive role in cultivating research literacy among nursing graduate students, but corresponding research is still in its early stages. Future research should strengthen experimental studies, provide empirical research containing data, actively integrate cutting-edge technologies, promote their in-depth application in this field, while ensuring the safety and effectiveness of the technology, thereby effectively promoting innovation and development in nursing.

This systematic review examines how K-12 online learning has evolved in the post-COVID era, using the Science of Learning and Development (SoLD) framework to analyze whole-child development across five guiding principles. We searched ERIC, Google Scholar, and top educational technology journals for peer-reviewed studies published between 2020 and 2024. After a multi-stage screening process, we identified 27 studies that focused on K-12 online student learning in the post-COVID context. We then coded these studies using the SoLD framework to identify which principles and practices appeared most often and which were most often overlooked. The findings showed that research concentrated on four of the five SoLD principles: Positive Developmental Relationships; Environments Filled with Safety and Belonging; Rich Learning Experiences and Knowledge Development; and Development of Skills, Habits, and Mindsets. However, no studies examined Integrated Support Systems, and several important components of each principle were not captured in the corpus of research. Overall, the evidence suggests that online student learning post-COVID has led to meaningful changes in how teachers design online instruction, how families engage with schools in online settings, and how students develop necessary skills while engaging in online education. However, research has not yet examined how these elements work together as an integrated system to improve student learning.

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.