Frontiers in Education: Digital Learning Innovations
1 day 13 hours 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
4 days 6 hours 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
5 days 13 hours 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 week 1 day 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 week 4 days 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 week 4 days 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 week 5 days 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 week 6 days ago
Samar A. Ahmed
1 week 6 days 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
2 weeks 5 days ago
IntroductionTraditional teaching methods have been central to education systems worldwide. Still, digital innovation in higher education has transformed how education is delivered, introducing new teaching models and enhancing school management processes. However, most Higher Education Institutions (HEIs) face numerous limitations in effectively adopting digital systems, although these challenges are often context-specific.MethodsTo gain a deeper understanding of the scope and limitations of digitalization in higher education institutions, we conducted a comparative analysis of existing digital ecosystems across developed and developing countries.ResultsWe introduced a three-layer framework for digitalization comprising institutional strategies, technological tools, and pedagogical methods. These layers are further divided into several components that support innovation. Using data from multiple sources, we identified several current drivers of the adoption of digital innovation in universities, including student expectations and needs, technological advances, strategic leadership, and the rise of hybrid learning models. Additionally, we compared the rate of digitalization in HEIs between developed and developing countries; our findings indicated that innovation proceeds more smoothly and is easier to manage in developed nations. We highlighted strategies for scalable and sustainable innovation through successful case studies from well-known universities. To tackle systemic challenges in digitalization, their impacts, potential solutions, and levels of implementation, we employed a priority matrix approach. This approach showed that most challenges are quick wins when addressed and have a substantial impact on digitalization.DiscussionThis study emphasizes the importance of adaptive, inclusive, and strategic approaches that promote equity and encourage the adoption of global learning ecosystems by comparing digital innovation in developed and developing countries.
Taojing Wang
2 weeks 5 days ago
The COVID-19 pandemic forced higher education institutions in resource-constrained contexts to rapidly adopt online learning, yet little is known about how students in Sub-Saharan Africa experience and evaluate these platforms, particularly when facing severe infrastructure barriers. Understanding student satisfaction in these contexts is critical for ensuring educational equity and informing evidence-based policy. This study investigates student satisfaction with Google Classroom at the Faculty of Engineering—Lúrio University in Mozambique, where 220 students participated in a mixed-methods study during the 2020–2021 academic year. Using the Technology Acceptance Model (TAM) as a theoretical basis, the research examined overall satisfaction, demographic differences, challenges faced, and valued features of the platform. Results showed moderate satisfaction levels (M = 3.18, SD = 1.12 on a 5-point scale), with 35% of students expressing satisfaction and 28% expressing dissatisfaction. Internet connectivity quality was identified as the most significant factor affecting satisfaction (p < 0.001), while device type also influenced results (p < 0.05). Qualitative data identified six main challenges: connectivity issues, mobile data costs, social isolation, technical difficulties, limited interaction, and workload concerns. Students valued the platform’s flexibility, accessibility of materials, organization, and ease of use. The study emphasizes the need for infrastructure investment and targeted support systems for effective online learning, especially in resource-limited settings, providing useful insights for improving higher education quality during and after the pandemic.
Heráclito Rodrigues Comia
2 weeks 6 days ago
Despite growing interest in educational digital leadership and supervision, validated instruments measuring supervisors’ digital competencies for their performance excellence (SDC-PE) remain scarce, particularly in developing nations. This study developed and validated the SDC-PE scale through stakeholder perceptions of 318 principals and teachers across 78 senior high schools in 17 districts of South Sulawesi, Indonesia. The SDC-PE integrates classical supervision theories (Cogan, Goldhammer, Glickman) with ISTE Standards and DigCompEdu 2.2, yielding four dimensions: Strategic digital leadership (SDL), digital culture promotion (DGC), teacher digital development (TDD), and digital learning facilitation (DGL). Confirmatory composite analysis (CCA) using PLS-SEM demonstrated excellent psychometric properties: factor loadings (0.839–0.936), composite reliability (0.933–0.955), AVE (0.776–0.811), and model fit indices exceeding thresholds (CFI = 0.968, TLI = 0.960, RMSEA = 0.072). Discriminant validity was confirmed through Fornell-Larcker criterion and HTMT ratios (<0.85). The SDC-PE provides educational institutions with an empirically validated assessment tool for evaluating supervisor digital competencies, designing targeted professional development.
Ansar Ansar
3 weeks 1 day ago
Within the global movement of cultural revival, the modern translation of poetic imagery (defined as the process of transmuting classical poetic symbols along with their emotional and cultural connotations into modern design elements) has emerged as a critical concern in Chinese design education, presenting a central tension between AI-driven efficiency and cultural depth. This study addresses three structural faults in current translation practices: fragmented symbolic extraction, weakened nostalgic drive, and over-reliance on AI tools. It establishes a tripartite efficacy evaluation framework encompassing emotion, cognition, and market dimensions, as well as a dual-cycle educational model featuring critical and iterative phases. A controlled experiment with 22 second-year product design majors (divided into an AI-assisted group and a traditional group) was conducted over a 4-weeks design psychology course, focusing on war, boudoir, and pastoral poetry themes. Results show that the AI-assisted group outperformed in emotional resonance (4.22 ± 0.38 vs. 3.54 ± 0.47) and market responsiveness (81.3% ± 8.2% vs. 64.1% ± 10.7%), while the traditional group maintained an advantage in cognitive completeness (83.7% ± 5.9% vs. 80.3% ± 5.1%). The dual-cycle model effectively reduced cultural misinterpretation rates in the AI group from 33% to 12%. Meanwhile, this study proposes the “Nostalgia-Congruent AI Guidelines (NCAI-G),” which regulates AI application from three aspects: symbolic fidelity, nostalgia coherence, and user safety. This study provides a reusable educational framework for balancing AI instrumental rationality and cultural value rationality, advancing traditional cultural design education toward quantitative evaluation-driven iteration.
Shijiang Hou
3 weeks 1 day ago
Student feedback literacy is vital for effective use of feedback. While traditional peer review activities provide opportunities for students to practice giving and receiving feedback, their effectiveness is sometimes undermined because of interpersonal factors, such as friendship and psychological safety. Generative artificial intelligence (GenAI) offers a promising new avenue by providing adaptive and instant feedback; however, its effectiveness compared to traditional peer interaction and the underlying mechanisms remain underexplored and warrant further investigation. This study used a mixed-methods design with first-year undergraduates to explore the effect of GenAI and human peer feedback on student feedback literacy development. The study also analyzed the role of students’ self-regulated learning (SRL) as a mechanism explaining how these two feedback sources contribute to enhancing feedback literacy. The results revealed that GenAI yielded a small but significant improvement in developing feedback literacy compared to human peers. Qualitative analysis clarified this finding by uncovering behavioral differences between the two groups, highlighting GenAI’s specific support for the SRL process, especially in goal setting, planning, critical evaluation, and immediate self-reflection. These findings suggest that GenAI is powerful in fostering feedback literacy because it facilitates self-regulatory behaviors essential for effective interaction with feedback. Educators can strategically integrate GenAI in classroom activities to scaffold self-regulatory behaviors, fostering student feedback literacy development.
Jiahe Gu
3 weeks 1 day ago
Maka Eradze
3 weeks 4 days ago
Virtual reality (VR) as a form of simulation-based learning can lead to better understanding of learners and increase motivation. Recent evidence shows effectiveness in teacher education in terms of skill growth for student teachers. In this regard, the perceived usefulness of a novel technology is a key factor affecting behavioral intention to use it. Therefore, this mixed-method study investigates the perception of the usefulness of a VR environment from the perspective of student teachers and explores to what extent the change in perception affects the intention to use it in later professional practice. To answer the questions, N = 57 student teachers from four countries assessed a VR environment designed for teaching mathematics. The VR environment deals with the spread of infectious diseases to address the mathematical issue of exponential growth. To assess its usefulness as well as its general potential, students filled in a questionnaire before and after the VR simulation and participated in an interview afterwards. The findings show a significant positive change in usefulness beliefs. Furthermore, perceived usefulness predicted intention to use the VR technology. Results from the interviews show the potential of the new technology in transcending certain boundaries of everyday teaching and emphasize on the affective component of a VR experience in schools. But also limitations for every day life and use of VR, for instance, with regard to specific age groups were mentioned. A main implication of this study is that an early experience of VR in teacher education underlines the willingness to use this technology in later professional life.
Florentine Hickethier
3 weeks 5 days ago
IntroductionTraditional approaches to teaching physics often struggle to engage students and to convey abstract concepts such as gas laws in a meaningful way. This challenge is particularly evident for learners accustomed to interactive and technology-mediated environments. Recent advances in embodied cognition and active learning suggest that multi-sensory interaction may enhance engagement and conceptual understanding. Visuo-haptic simulators represent a promising approach by combining visual and tactile feedback to support experiential learning.MethodsThis study developed a visuo-haptic simulator designed to support the exploration of Boyle's Law through interactive manipulation of pressure and volume variables. The simulator provided real-time visual feedback and proportional haptic resistance to represent changes in gas behavior. Thirty-nine undergraduate engineering students interacted with the simulator in controlled laboratory sessions. A mixed-methods approach was used to evaluate students' perceptions, combining the End-User Computing Satisfaction (EUCS) survey with semi-structured interviews.ResultsSurvey results indicated high levels of satisfaction in the dimensions of accuracy, ease of use, and timeliness, reflecting students' confidence in the simulator's responsiveness and reliability. Qualitative findings revealed strong engagement and motivation, with participants reporting that tactile feedback helped them intuitively understand the inverse relationship between pressure and volume. Some usability challenges related to interface layout were also identified.DiscussionThe findings suggest that visuo-haptic simulators can promote active engagement and support embodied understanding of abstract physics concepts by linking theoretical relationships to sensory experience. Students perceived the simulator as a valuable complement to traditional instruction and expressed interest in its application to other scientific topics. While learning outcomes were not directly measured, the results highlight the potential of visuo-haptic tools to enhance motivation and experiential learning in physics education. Future work will focus on assessing learning gains in classroom settings and extending the approach to additional thermodynamics concepts.
Sebastián Montes-Isunza
3 weeks 6 days ago
Daniel Chang
1 month ago
IntroductionAs artificial intelligence (AI) becomes increasingly embedded in educational environments, understanding its role in shaping learners’ self-regulated learning (SRL) and self-directed learning (SDL) has emerged as a central concern in contemporary learning science. While prior studies suggest that AI-driven systems may support planning, monitoring, and autonomy in learning, empirical evidence remains fragmented across contexts, learner groups, and instructional designs. This study synthesizes existing empirical research to systematically examine the magnitude and conditions under which AI-based interventions influence SRL, its dimensions and phases, SDL, and associated learning outcomes.MethodsA systematic meta-analysis was conducted following PRISMA guidelines, synthesizing evidence from 32 empirical studies comprising 92 effect sizes and a total of 3,029 participants. The analysis examined overall effects of AI-based interventions on SRL and SDL, disaggregated effects across SRL dimensions (cognitive/metacognitive, motivational/affective, and behavioral regulation) and SRL phases (forethought, performance, and self-reflection), as well as impacts on learning outcomes and academic achievement. Random-effects models were applied, and moderator analyses explored learner characteristics, contextual variables, and AI design features. Sensitivity analyses and publication bias assessments were performed to evaluate the robustness of findings.ResultsAI-based interventions demonstrated a large and statistically significant positive effect on overall SRL (g = 1.613, p = 0.032) and SDL (g = 1.111, p = 0.043), indicating substantial improvements in learners’ ability to plan, monitor, and regulate their learning while sustaining autonomy and persistence. At the dimensional level, AI produced moderate gains in cognitive/metacognitive regulation (g = 0.377, p = 0.0004) and motivational/affective regulation (g = 0.505, p = 0.013), whereas effects on behavioral regulation were inconsistent. Phase-level analyses revealed that AI interventions were most effective during the forethought phase, supporting goal setting, planning, and motivational readiness, with smaller but significant gains observed in self-reflection and variable effects during the performance phase. AI systems also yielded moderate improvements in learning outcomes and achievement (g = 0.350, p = 0.034). Moderator analyses indicated stronger SRL effects among older learners, longer intervention durations, and language learning contexts employing interactive AI systems, while gender differences were minimal. Sensitivity and publication bias tests confirmed the stability of results.DiscussionThe findings indicate that AI functions as an adaptive scaffold that meaningfully enhances learners’ self-regulatory and self-directed capacities across cognitive, motivational, and reflective processes. By strengthening forethought and planning mechanisms in particular, AI-based interventions support more autonomous, sustained, and effective learning behaviors that translate into measurable academic benefits. Variability in behavioral regulation outcomes highlights the need for more explicit action-level supports in AI design. Overall, the results showcase AI’s potential to promote equitable and scalable self-regulated learning across diverse educational contexts, while also pointing to the importance of aligning intervention design with learner characteristics and instructional goals.
Krishnashree Achuthan
1 month 2 weeks ago
While artificial intelligence (AI) is transforming many sectors, its integration into pre-service teacher education in higher education remains limited. This study investigates the iterative development and effects of a concise, two-session educational intervention designed to foster AI literacy among pre-service physics teachers. Following a design-based research approach, the intervention was implemented in two iterations at the University of Cologne (n = 31 across two cohorts). Structured according to the 5E instructional model, the intervention required students to use generative AI tools as didactic instruments to create lesson plans and reflect on their usage. AI literacy was measured using a validated 30-item test, while attitudes toward AI were assessed via a 4-point Likert survey. Results indicate only small, non-significant increases in overall AI literacy, with selective gains observed in competencies explicitly supported by hands-on activities and targeted scaffolding. However, attitudinal measures demonstrated that even brief interventions can strengthen participants’ openness toward AI and their perceived preparedness to use AI tools in teaching. Additionally, the iterative comparison highlighted format-sensitive effects. These findings suggest that while short design-based interventions can selectively activate elements of AI literacy and foster professional confidence, they are insufficient for broader skill Acquisition. Consequently, more sustained, context-rich engagements are likely required to achieve comprehensive and durable AI literacy development in pre-service teacher education.
Jannik Henze