Frontiers in Education: Digital Learning Innovations
8 hours 43 minutes ago
IntroductionThis study investigates how ChatGPT, a generative AI tool, can promote inclusive and sustainable e-learning by enhancing learner empowerment, satisfaction, and engagement.MethodsDrawing on the Technology Acceptance Model (TAM), Self-Determination Theory (SDT), and the Sustainable Education Framework, a structural model was developed to examine the mediating roles of student empowerment and student satisfaction, and the moderating role of inclusivity perception. Data were collected from 350 students across South Korean universities using a cross-sectional survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).ResultsThe findings indicate that perceived usefulness, perceived ease of use, and accessibility of ChatGPT significantly enhance student empowerment, which in turn increases satisfaction, continuance intention, and sustainable learning outcomes. Furthermore, inclusivity perception strengthens these relationships.DiscussionThe study contributes theoretically and aligns AI-enabled education with SDG-4.
Usman Rehman
1 week 5 days ago
IntroductionAs digital technologies continue to impact our education system, webinars have become an essential way to deliver timely, scalable training, especially in the post-COVID era. Despite widespread use, we still know relatively little about how effective webinars are especially in the field of agricultural education and extension. This study aims to fill that gap by exploring how socio-economic, personal, and learning-related factors shape participants' webinar experiences and outcomes.MethodsData was collected from 415 participants across India through a structured online survey. The impact of webinar was assessed using the first two levels of Kirkpatrick's evaluation model, focusing on participants' reactions and learning. To measure overall effectiveness, Webinar Effectiveness Index (WEI) was developed using the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) based on five key components like usefulness, lecture quality, knowledge gain, satisfaction, and learning impact. Fuzzy C-Means clustering was applied to identify patterns among learners, supported by correlation analysis to understand how satisfaction and learning outcomes relate. Costs and time requirements of webinars were also taken into account.ResultsOur analysis revealed eight distinct types of learners, each showing different levels of engagement and effectiveness. Some clusters performed consistently well, reporting strong knowledge gains and high satisfaction, while others displayed more varied and less favorable outcomes. We also found clear, positive Correlation between participants' satisfaction, their knowledge gain, and the overall impact of the learning experience. Additionally, webinars proved to be more economical and time-efficient than in-person seminars.Discussion/ConclusionThe study offers a practical, multi-dimensional approach to evaluating webinar effectiveness using soft computing tools. The findings highlight how learner diversity shapes digital learning outcomes and demonstrate the strong connections among satisfaction, learning, and perceived impact. Overall, the study provides useful guidance for designing webinars that are more engaging, inclusive, and cost-effective especially for large-scale capacity-building programs in agriculture and other fields where accessibility and scalability matter most.
Anirban Mukherjee
2 weeks ago
IntroductionThe application and influence of artificial intelligence (AI), and specifically Large Language Models (LLMs), in educational processes is widely discussed. However, there remains a gap in research on using LLMs as peer-like contributors in collaborative learning contexts.MethodsThis article reports a mixed-methods quasi-experimental study investigating how positioning ChatGPT as a peer-like feedback provider shapes student-teachers’ learning and collaboration during group lesson-design activities. The study employed a counterbalanced crossover structure for knowledge assessment and a sequential two-task design for authentic artifact production. A total of 102 teachers in training (M_age = 38.87, SD = 8.01), organized into 21 groups, completed two authentic design tasks within a single session.ResultsAcross the session, students progressively adapted to AI interaction, refining how they queried the model and how they evaluated and integrated its suggestions. Results indicate a Post-Withdrawal Sustained Performance (PWSP) effect: improvements observed during AI-available phases were not followed by a detectable decline in the immediately subsequent AI-withdrawn phase within the study timeframe. This pattern was clearest for technology-related knowledge and was consistent with stable artifact quality after AI removal. While ChatGPT support increased efficiency and contributed to technology-focused insights, qualitative evidence also pointed to tensions, including reduced peer-to-peer idea-building in some groups and concerns about creativity.DiscussionOverall, the findings suggest that integrating LLMs as a feedback team-mate can support collaborative design work without immediate post-withdrawal performance costs, particularly when learners are scaffolded to engage critically with AI output rather than accept it unreflectively. These results carry implications for the design of AI-enhanced collaborative activities, highlighting the need to balance AI efficiency gains with sustained opportunities for authentic peer dialogue.
Daniele Agostini
2 weeks 6 days ago
Despite the proven efficacy of corpus linguistics in language education, its adoption in English for Specific Purposes (ESP) classrooms remains limited due to the technical complexity of corpus tools, particularly in non-English departments. Addressing this “usability gap,” this study evaluates the effectiveness of the Digital Corpus-based Instruction (DCI) model, a novel instructional framework that synergizes corpus data, QR-code technology, and Mobile-Assisted Language Learning (MALL). The study aims to determine if providing corpus data through familiar mobile interfaces can improve lexical competence among non-English majors in low-resource settings. Employing a quasi-experimental design with a convergent parallel mixed-methods approach, the study involved 30 non-English-major undergraduates in an Indonesian university setting. Quantitative data from standardized pre- and post-tests, focusing on vocabulary matching, collocation awareness, reading, and professional writing, were triangulated with qualitative insights from classroom observations, Focus Group Discussions (FGDs), and interviews to capture behavioral and cognitive shifts. The results revealed a statistically significant improvement in the experimental group (p < 0.001), with a very large effect size (Cohen's d > 1.93). Significant gains were observed in collocation awareness (from 2.30 to 5.13) and professional writing accuracy (from 7.83 to 10.10). Qualitative findings corroborated these metrics, indicating a pedagogical transformation from passive learning to active, data-driven inquiry and increased professional confidence. This study contributes to the field by demonstrating that DCI effectively reduces the cognitive load of corpus analysis, offering a scalable, pedagogically viable solution for enhancing ESP competence in higher education contexts.
Misnawati Misnawati
3 weeks 1 day ago
Nidhu Neena Varghese
3 weeks 5 days ago
In the era of digital transformation, the application of remote sensing (RS) data in geography education and its effective integration into the teaching and learning process has become one of the pressing educational challenges, particularly in fostering students’ spatial thinking competencies. The purpose of the study is to identify the possibilities of incorporating RS data into school geography, to develop an effective teaching methodology based on its application, and to test it in practice. The research employed a review of international publications, surveys, correlation and comparative statistical analysis, modeling, analysis of regulatory documents, and pedagogical experimentation. The survey involved 124 geography teachers from different regions of Kazakhstan, while the experimental study included 52 eleventh-grade students from one secondary school selected through random sampling (26 students in the experimental group and 26 students in the control group). The survey results revealed that 62% of teachers use satellite imagery during lessons; however, 69% of them utilize RS data primarily for visualization purposes. The results of the pedagogical experiment demonstrated that the average academic achievement of students in the experimental group increased by 18.2%, whereas the improvement in the control group was 3.6%. Furthermore, the use of RS data positively influenced students’ geospatial thinking, research skills, and analytical abilities. The study further established that the structural-content model of RS-based teaching materials developed as a result of the research can contribute to improving existing methodologies. The proposed model is designed to modernize school geography curricula, enhance teachers’ methodological capacity, improve students’ learning outcomes, and promote geospatial literacy. The findings of the study demonstrate that the systematic integration of RS data into school geography education can serve as a scientific and methodological foundation for modernizing educational content, fostering students’ contemporary scientific and technological competencies, and improving the overall quality of geographic education.
Shakhislam Laiskhanov
1 month ago
IntroductionGenerative artificial intelligence (GenAI) is rapidly reshaping higher education. However, evidence remains limited regarding its pedagogical utility and learning benefits in university dance learning environments.MethodsThis study employed a quasi-experimental design with 60 university students who were randomly assigned to either an experimental group using a GenAI-based teaching tool (GEN Dance) or a control group using a conventional multimedia tool. GEN Dance supported real-time, interactive dance learning activities.ResultsThe GenAI-supported condition (GEN Dance) demonstrated statistically significant advantages over the conventional multimedia condition across all three assessed learning-related domains.DiscussionThese findings suggest that GenAI can enhance learning outcomes in higher education dance contexts and support more interactive instructional experiences. This study extends the emerging literature on GenAI-enabled teaching and provides empirical evidence for the integration of GenAI tools in university dance learning environments.
Songni Xu
1 month ago
IntroductionModern e-learning platforms are increasingly using personalized elements to better address diverse learners and enhance learning outcomes. To investigate the effects on the development of students' metacognitive thinking, this study develops an adaptive e-learning environment based on two personalized factors: learning styles and knowledge levels.MethodsThe study, which used a quasi-experimental design with undergraduate students, revealed that there was no obvious effect on metacognitive growth when content was personalized for learning styles.ResultsThis reflects the increasing concern among academics about the usefulness of learning styles in personalized learning. On the other hand, metacognitive engagement was found to be strongly influenced by prior knowledge.DiscussionThe findings imply a shift to evidence-based adaptive design, arguing that to successfully promote higher-order cognitive abilities, e-learning environments should give priority to empirically verified factors like knowledge expertise.
Sami A. Issa
1 month 1 week ago
IntroductionILS are AI-enhanced educational technologies increasingly implemented in K-12 education.MethodsWe analyzed 72 peer-reviewed articles from Scopus and WoS (2014–2024) following PRISMA 2020 guidelines using systematic evidence synthesis and thematic analysis.ResultsEighty-nine percentage of overall positive outcomes; ITS dominate (46%); regional and disciplinary variations identified.Discussion/conclusionCritical effectiveness factors identified; gaps in CT development and non-STEM research noted; longitudinal research needed.
Edison Marino Cerón Salazar
1 month 1 week ago
This study explores the pedagogical bases of ICT integration in pre-service chemistry teacher education within the “University-School Bridge” framework. The research aimed to examine how digital technologies and pedagogical strategies collectively influence the professional development of future chemistry teachers. Using a quantitative quasi-experimental longitudinal design with pre-, mid-, and post-tests and statistical analysis via ANOVA, the study evaluated changes in students’ chemistry knowledge, digital literacy, and pedagogical competencies. Participants included 34 pre-service teachers and 224 secondary school pupils. The finding revealed significant improvement across all variables, with large effect sizes for overall performance (η2 = 0.65), and pedagogical methods (η2 = 0.41). The integration of digital tools such as Moodle, Kundelik.kz, and Quizlet, combined with classroom-based mentoring, was associated with higher levels of reflective practice and professional readiness. The results also support the University-School Bridge model as being associated with improved alignment between theoretical preparation and school-based practice. These outcomes highlight the importance of continuous, data-driven, and practice-oriented, adaptive, and research-minded educators.
Aigerim Bekbenova
1 month 1 week ago
IntroductionBalancing potency and feasibility is essential for implementing digital technologies in school settings, where time and attention are limited. We introduce precision streamlining—a novel intervention design strategy that reduces overall duration while preserving effectiveness through targeted personalization. Instead of delivering all content to all students, this approach uses personalization to reduce the total material delivered—focusing only on what is most impactful for each individual.MethodsWe applied this strategy to an existing attention training program for adolescents, reducing its length by 45%. We then tested the streamlined version across 13 public high schools.ResultsIn Study 1, the shortened intervention produced significant improvements in emotional regulation, growth mindset about attention, self-efficacy, and classroom focus. Study 2 replicated these findings in a larger and more diverse sample. Study 3 entailed a direct comparison of students who either received the original, longer version or the shortened version (N = 1715). While the original intervention produced stronger effects on self-efficacy and emotional regulation, the shortened version still yielded significant gains across all four outcomes.DiscussionThese findings support precision streamlining as a promising design strategy. They also demonstrate that a brief attention training program can offer meaningful benefits for student learning and well-being—while remaining feasible to implement in time-constrained school settings.
Alissa J. Mrazek
1 month 1 week ago
The emergence of Large Language Models (LLMs) has opened new possibilities for language learning through conversational interaction with chatbots. Yet, little empirical evidence exists on how students experience such interactions and how corrective feedback should be provided. Research suggests that immediate corrective feedback is generally more effective than delayed feedback. Nevertheless, learners' perception of this effectiveness and their preferences for feedback timing, particularly in the domain of Computer-Assisted Language Learning (CALL), remain underexplored. This study investigates the feasibility of providing immediate feedback and examines the impact of feedback timing on user experience and grammar learning gains in English. An in-the-wild experiment was conducted with 66 L2 English learners, who integrated chatbot sessions into their English course as an extracurricular activity over one semester. Participants were randomly assigned to two groups receiving feedback either during or after the conversation. Findings reveal no significant difference in learning gains, but immediate feedback enhanced user experience, leading to overall positive perceptions of the chatbot. Additionally, we explore users' perceptions of the chatbot's social role and personality, offering a roadmap for future enhancements. These results provide valuable insights into the potential of LLMs and chatbots for language learning.
Alireza M. Kamelabad
1 month 1 week ago
Charity M. Dacey
1 month 2 weeks ago
IntroductionThis study investigated the relationships among gifted behavior, Generative AI (GenAI) anxiety, and learning motivation among gifted EFL university students in Saudi Arabia, with an additional focus on the mediating role of GenAI anxiety and the moderating effect of gender.MethodsUsing a quantitative correlational design, data were collected from 304 purposively selected undergraduates classified as gifted based on institutional records and psychometric confirmation using Gifted Rating Scales. The participants answered the verified instruments of gifted behavior, GenAI anxiety, and academic motivation.ResultsAnalysis of structural equation modeling showed that gifted behavior was associated with lower GenAI anxiety and higher motivation, and that GenAI anxiety had a significant negative association with motivation. The mediation analysis indicated that the relationship between gifted behavior and motivation was partially mediated by GenAI anxiety. Gender was a significant moderator of the relationships between gifted behavior and motivation, as well as between GenAI anxiety and motivation, with both associations being stronger among females.DiscussionThe findings contribute to the growing body of research on the psychological impact of AI implementation in EFL learning, suggesting that gifted learners’ responses to GenAI technologies are characterized by distinct affective and motivational patterns. The research provides theoretical insights into giftedness and technology-related emotions, providing pedagogical implications for creating AI-based EFL learning environments that are sensitive to learners’ cognitive and affective profiles.
Amr Mahmoud Mohamed
1 month 3 weeks ago
Teaching Information and Communication Technologies (ICT) in higher education faces the challenge of engaging students accustomed to interactive and digital learning environments. Although gamification has emerged as a promising pedagogical strategy to enhance motivation, participation, and learning, there is still no consensus on which gamification elements and strategies should be prioritized in ICT courses. To address this gap, this study aimed to identify, through expert consensus, the most relevant gamification components for ICT teaching in higher education. A modified Delphi study with a mixed-methods design was conducted in two rounds: an open exploratory phase and a structured round using a five-point Likert scale. Twelve experts were selected based on a competence coefficient (K ≥ 0.75). Quantitative analyses included means, standard deviations, interquartile range (IQR), coefficient of variation (CV), and Kendall’s coefficient of concordance (W), complemented by qualitative thematic coding with an inter-coder reliability of κ = 0.82. Consensus thresholds followed current Delphi standards (≥ 80% agreement, IQR ≤ 1, CV ≤ 0.30, and W ≥ 0.60). The results indicate that experts prioritized challenges and quests (83% agreement; W = 0.66), followed by points and rewards (67%) and the complementary use of badges and leaderboards. Strong consensus was reached regarding the potential of gamification to strengthen problem-solving, critical thinking, and collaboration (100%; W = 0.90), and to increase active participation (100%) and promote deep understanding (83%). However, heterogeneity persisted in relation to the selection of technological platforms (W = 0.27) and specific strategies (W = 0.28), and the effects on academic performance were reported as inconsistent. Overall, the findings suggest that gamification is most effective when systematically integrated into ICT curricula, supported by teacher training and immediate feedback mechanisms. As no universal technological platform is recommended, the use of contextual selection criteria is essential. These results provide a conceptually validated reference framework for the design of gamified learning experiences in higher education and underscore the need for further empirical validation across diverse educational contexts.
Lorena Jaramillo-Mediavilla
1 month 3 weeks ago
This study introduces and empirically validates a comprehensive educational model designed to enhance digital competence and learning motivation among Master’s students in Kazakhstan. Using a quasi-experimental design with 236 participants from five pedagogical universities, the research examined how an integrated approach combining technology-enhanced pedagogy, microlearning, gamification, online assessment, and collaborative digital projects influenced students’ digital competence, learning motivation, and academic performance. Results demonstrated significant improvements in digital competence across all dimensions (effect size η2 = 0.46) for students in the experimental group, with particularly strong development in content creation and problem-solving competencies. The model also had a positive influence on learning motivation, self-efficacy, student engagement, and academic performance. Path analysis confirmed an integrated theoretical framework where technology-enhanced pedagogy influenced digital competence, which subsequently affected motivation, self-efficacy, engagement, and academic achievement. Students with elementary baseline digital skills showed the largest competence gains, indicating the model’s potential for addressing digital inequality in Kazakhstan’s higher education system. Follow-up assessments revealed durable effects, suggesting sustainable rather than transient changes. The research provides theoretical contributions to understanding the interrelationship between digital competence and motivation while offering practical strategies for modernizing Master’s programs in Kazakhstan and similar educational contexts.
Zakira Bakirova
1 month 3 weeks ago
IntroductionThis study examines the relationship between ultra-Orthodox teachers’ perceptions of non-formal education and their perceptions of 21st-century skills. These skills comprise a set of competencies essential for successful integration into today’s workforce and civic life. Teachers’ perceptions of these skills and their importance are critical for their successful implementation in the classroom. Several factors contribute to teachers’ positive perceptions of 21st-century skills, one of which is a positive view of non-formal education, which is a flexible, investigative, and creative educational method. This research explores this relationship by focusing on teachers from the ultra-Orthodox collectivist culture, a community that tends to show conflicting attitudes toward 21st-century skills and non-formal education.MethodsA group of 238 ultra-Orthodox teachers completed a questionnaire regarding perceptions of non-formal education, 21st-century skills and demographic data.ResultsThe findings indicate that positive perceptions of non-formal education are associated with all dimensions of 21st-century skills, with the strongest related to communication skills, which is aligned with cultural norms, while creativity showed the weakest association, which also reflects a broad cultural rejection of this skill.DiscussionThe study reinforces the positive connection between educational approaches and perceptions of 21st-century skills, including within a religious collectivist community.
Anat Barth
2 months ago
IntroductionThe use of large language models (LLMs) in education has rapidly expanded, generating interest in their potential to support teachers through task automation and instructional planning. This review synthesizes evidence on reported changes in time devoted to educational content generation and support for planning-related task automation.MethodsWe conducted a systematic review of peer-reviewed literature published between 2023 and 2025, focusing primarily on secondary and higher-education contexts. Study selection followed PRISMA 2020 guidelines. Risk of bias was assessed using ROBINS-I for non-randomized studies and CASP checklists for qualitative/mixed-methods studies and for secondary evidence syntheses.ResultsSixteen studies met inclusion criteria (13 primary empirical studies and 3 secondary syntheses). Across primary studies, LLM use was associated with reported time savings and perceived gains in clarity or usefulness of generated educational resources. However, outcomes and measures were heterogeneous and often self-reported, several risk-of-bias domains were rated as unclear, and evidence was concentrated in higher-education settings with small samples, limiting comparability and causal inference. Recurrent constraints included limited reproducibility, strong dependence on prompt design, and ethical or technical concerns.DiscussionLLMs may support educators, but conclusions should be interpreted cautiously. Effective integration requires clear pedagogical frameworks, human oversight, and standardized evaluation in real-world settings.Systematic review registrationOpen Science Framework (OSF). Unique identifier: v63nj. Public URL: https://osf.io/v63nj/
Giovanni Luna Chontal
2 months ago
IntroductionArtificial Intelligence (AI) is increasingly integrated into higher education to personalize instruction and support student engagement. However, the mediating role of teaching methods in this process remains underexplored.MethodsThis systematic review analyzed 73 peer-reviewed articles published between 2015 and early 2025, retrieved from Scopus and Web of Science, following PRISMA guidelines. Studies were screened based on predefined inclusion criteria and coded using a structured framework that examined AI types, engagement outcomes, and instructional strategies.ResultsFindings reveal that AI tools—such as chatbots, adaptive systems, and predictive analytics—enhance engagement most effectively when embedded within interactive pedagogies like flipped classrooms, project-based learning, and scaffolded feedback loops. To conceptualize this relationship, we introduce the PMAISE model (Pedagogical Mediation of AI for Student Engagement), which maps the alignment between AI technologies, pedagogical strategies, and the affective, behavioral, and cognitive dimensions of engagement. Concrete examples from recent studies demonstrate how teaching methods amplify or inhibit the effects of AI tools. The review also critically examines emerging concerns related to ethics, data privacy, and structural barriers to equitable AI adoption.DiscussionThis study offers a conceptual and practical framework for integrating AI into higher education in a context-sensitive, evidence-based, and pedagogically meaningful manner, highlighting the crucial role of thoughtful pedagogical mediation in maximizing AI’s educational benefits.
Dong Yu Long
2 months 1 week ago
Background and purpose of the studyThe rise of quantum technologies necessitates integrating foundational quantum mechanics (QM) concepts into secondary education. However, inherently abstract phenomena like quantum entanglement pose significant pedagogical challenges, as traditional formalism-based approaches are often inaccessible. This study introduces and delineates an innovative, scaffolded pedagogical model designed to foster robust conceptual understanding of entanglement in secondary STEM education, moving beyond reliance on mathematical formalism.The proposed pedagogical modelThe presented contribution is a detailed pedagogical sequence following a deliberate learning trajectory. It begins with a tangible analogy (magnetic interactions) as a conceptual anchor for correlation, then transitions to computational tools (Bloch sphere visualization, Qiskit simulations). These tools facilitate exploration of quantum concepts weakly addressed by the analogy (e.g., superposition) and allow more authentic engagement with quantum behavior. Underpinned by constructivism, cognitive load theory, and QM education research, the model strategically repurposes the analogy’s limitations as pedagogical opportunities to introduce and contrast key quantum features like non-locality and superposition with classical intuition. The sequence integrates exploration, guided use of representations, and critical comparative discussion.Conclusions and potential implicationsThis paper provides a theoretically grounded pedagogical model for introducing quantum entanglement in secondary STEM education, combining tangible and computational tools in a scaffolded manner. The approach offers potential advantages over traditional methods by providing concrete starting points and explicitly using classical limitations to illuminate quantum principles. While promising, rigorous empirical validation is the essential next step. Future research should investigate the model’s effectiveness in authentic classroom settings, informing curriculum design and teacher development for incorporating QM into secondary STEM.
David Castillo-Salazar