Frontiers in Education: Digital Learning Innovations
1 day 16 hours 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
5 days 16 hours 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
5 days 16 hours 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 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 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 week 1 day ago
Charity M. Dacey
2 weeks 1 day 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
2 weeks 5 days 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
2 weeks 5 days 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
2 weeks 5 days 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
3 weeks 5 days 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
1 month 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
1 month 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
1 month ago
IntroductionPeer-learning recommendation remains an open challenge in e-learning systems, as most existing approaches—such as matrix factorization and neural collaborative filtering—rely on static interaction patterns. These methods often ignore contextual information including learner roles, content difficulty, and temporal engagement behavior. As a result, they struggle to form meaningful peer groups or provide adaptive learning paths that align with pedagogical needs.MethodsTo address these limitations, we propose a hybrid context-aware peer learning recommender that integrates collaborative filtering with interaction-based clustering. The framework incorporates adaptive peer group formation using multiple loss functions and multifactor BERT embeddings to capture content semantics. In addition, learner-specific characteristics such as difficulty level, job role, and software skills are explicitly modeled. These contextual and semantic features are dynamically used to cluster learners and generate personalized peer recommendations.Results and discussionExperiments conducted on an e-learning dataset demonstrate that the proposed model significantly outperforms sequential baseline approaches, as well as traditional matrix factorization and neural collaborative filtering models. The hybrid approach achieves an accuracy of 0.80, precision of 0.80, recall of 0.06, and an F1-score of 0.11. These results indicate improved personalization and contextual relevance in peer recommendations, enabling more adaptive and pedagogically suitable peer learning experiences.
Dazzle A. J.
1 month 1 week ago
Open Learner Models (OLMs) are increasingly positioned as pedagogical tools capable of enhancing self-regulated learning (SRL) within higher education. Despite considerable technological progress, the pedagogical mechanisms through which OLMs support students' regulatory behaviors, metacognitive processes and motivational engagement remain insufficiently theorized. This review synthesizes empirical studies exploring OLMs that intentionally integrate pedagogical strategies in order to deepen understanding of how these models influence SRL across its preparatory, performance and reflective phases. Drawing on thematic meta-synthesis of 26 studies, the review identifies a continuum of OLMs—Inspectable, Negotiable, Editable, and Persuasive/Adaptive—that reflects progressively greater levels of learner agency, transparency, and co-regulation. Across this continuum, the pedagogical design of OLMs emerges as a critical determinant of their educational value: inspectable models predominantly support monitoring and awareness; negotiable models foster active calibration and dialogic feedback; editable models strengthen autonomy and goal-setting; and adaptive models, frequently mediated by artificial intelligence, offer personalized scaffolding that sustains engagement and reflection. Together, these findings illuminate how pedagogically enriched OLMs can operate as metacognitive mediators rather than purely analytical systems, enabling students to interpret feedback, make informed learning decisions and assume increasing ownership of their learning trajectories. The review offers a theoretically grounded framework that informs the design of transparent, ethical, and learner-centered OLMs, with implications for instructional practice, digital pedagogy, and the broader field of educational research.Systematic review registrationCRD420251237223.
Jonathan L. Robles Mucho
1 month 1 week ago
IntroductionResearchers examined the effectiveness of a digital mapping project created in partnership with librarians for a history course. The project centered on the Israeli–Palestinian conflict and the 1948 war, aiming to improve students’ understanding of historical context while developing transferable skills such as critical thinking, research, and technological literacy. This initiative reflects broader trends in higher education that emphasize skill development and interdisciplinary collaboration.MethodsTo evaluate the project’s impact, a mixed-methods approach was used. Data collection involved pre- and post-surveys and analysis of course assignments. The sample included 17 undergraduate students enrolled in a history course, representing diverse majors such as History, Political Science, and Education. All enrolled students participated, with no exclusion criteria applied.ResultsFindings show that the digital mapping project greatly enhanced students’ understanding of the historical context, helped develop valuable skills, and boosted engagement compared to traditional assignments. Students’ responses were mainly positive, providing useful feedback for future projects and emphasizing the potential of digital tools in college education.DiscussionThe results indicate that integrating digital humanities into history courses can create engaging learning experiences and encourage deeper student involvement. These findings add to the growing evidence supporting the use of digital tools in humanities education. However, limitations include the small sample size and dependence on self-reported data, which may impact the ability to generalize the results.
Rami Zeedan
1 month 1 week ago
With the rapid advancement of artificial intelligence (AI) technology, its applications in medicine have grown increasingly widespread. In particular, AI has shown significant potential within residency education. This review examines current uses of AI in residency training, focusing on innovative educational models, curriculum design, outcome assessment, and existing challenges. By analyzing recent research across medical specialties, the discussion explores the role of AI in personalized learning, examination assistance, and clinical decision support. Furthermore, future strategies for integrating AI into residency education are proposed. This article aims to provide a comprehensive theoretical and practical reference for medical educators and policymakers, supporting the scientific application and continued development of AI in residency training.
Hongsen Zhang
1 month 1 week ago
AI, and especially generative AI, may have an effect on the number of gifted people in the world. But whether this effect will be positive or negative is up to us, collectively, and to each individual. This essay opens with a consideration of possible effects of AI on the giftedness of a society taken as a whole. Then it presents a philosophical conundrum, that of the Chinese Room, that is relevant to assessing the effects of AI. Next it reviews some recent empirical literature relevant to the question of AI’s effects on cognition and cognitive gifts. Then it discusses these effects. Finally, it draws some conclusions, in particular, that whether AI increases or decreases giftedness in a society is not preordained, but rather reflects a choice.
Robert J. Sternberg
1 month 1 week ago
Samar A. Ahmed
1 month 1 week ago
Technological development is reshaping teaching and learning environments, particularly through the integration of artificial intelligence (AI) tools such as ChatGPT in higher education. The objective of this study was to analyze university students’ intention to use ChatGPT, considering the gender variable. A quantitative methodology was employed, using an ad hoc questionnaire administered to a sample of 368 students from the Faculty of Education Sciences at the University of Málaga. Data analysis was conducted using the Jamovi software v2.3.26.0, applying techniques such as descriptive statistics, hypothesis testing, exploratory and confirmatory factor analysis, and ANOVA. The results showed widespread acceptance of ChatGPT, with no significant gender differences, suggesting a homogeneous adoption of this technology. These findings contrast with previous research indicating a gender gap in technology use. The importance of integrating AI into educational processes in a critical and pedagogically sound manner is emphasized, as well as the need to ensure equitable access. This study contributes to the understanding of how ChatGPT is perceived and used in academic contexts at the university level. It is recommended that the study be replicated in other contexts to validate the findings and broaden their applicability.
Enrique Sánchez-Rivas