Frontiers in Education: Digital Learning Innovations
15 hours 57 minutes ago
IntroductionBlended learning has emerged as a key strategy in management education, combining face-to-face teaching with digital tools to enhance flexibility, engagement, and skill development. When integrated with international cooperation projects, it enables pedagogical innovation and better prepares students for the complexities of global business environments.MethodsThis study employs quantitative analysis of course performance and engagement data to assess the impacts of blended learning combined with international collaboration. It draws on contemporary practices such as Collaborative Online International Learning (COIL), virtual exchange, and interdisciplinary teamwork to evaluate curriculum design, student outcomes, and institutional strategies.ResultsFindings demonstrate that blended learning facilitates the development of cross-cultural communication, ethical reasoning, and adaptive problem-solving skills. Technology-based experiential learning components like virtual internships, case simulations, and immersive platforms significantly enhance student engagement. However, challenges including digital inequities, resistance to pedagogical change, and the design complexity of effective blended environments persist.DiscussionAddressing these challenges requires targeted faculty training, robust infrastructure, and supportive policies. Future opportunities include leveraging artificial intelligence for personalized learning, gamification for increased engagement, and data analytics for real-time feedback. The study also highlights the critical role of academia–industry partnerships in creating impactful learning experiences. Ultimately, integrating blended learning with international collaboration advances management education toward more sustainable, inclusive, and practice-oriented models suited for a globally interconnected economy.
Juergen Bleicher
1 day 15 hours ago
This mixed-method experimental study investigates the effectiveness and typology of interactional feedback in supporting digital game-based language learning, focusing on two groups of primary language teachers. The experimental group employed interactional feedback within digital game-based language learning, while the comparison group used interactional feedback in traditional teaching methods. Results showed that the experimental group experienced statistically significant improvements in interactional feedback across three assessment points, whereas the comparison group showed no significant changes. The teachers in the experimental group employed a range of interactional feedback strategies, with notable improvements in clarification requests, recasts, and metalinguistic cues. Conversely, the comparison group primarily relied on repetition and direct correction, showing limited variation in their feedback approaches. Results also revealed that teachers in the experimental group significantly shifted their conceptions of interactional feedback, focusing not only on addressing interaction issues but also on recognizing the importance of uptake, output modification, and fostering student engagement through enriched screen time and enhanced teacher-student interactions. Further research should be encouraged and supported to develop digital games that prioritize teacher-student interaction, specifically by integrating interactional feedback as a standard feature. Collaborations between industry and researchers could play a crucial role in creating designs that effectively enhance interaction.
Abdulmajeed Alghamdi
1 week ago
IntroductionSustaining learners' continued use of AI chatbots for Mandarin instruction is a key challenge for EdTech developers, educators, and Confucius Institutes. Building on technology-continuance perspectives, this study examines how cognitive appraisals (performance expectancy, effort expectancy, facilitating conditions, social influence) shape learning motivation, learning satisfaction, and continuance intention among Chinese language learners.MethodsA cross-sectional survey using convenience sampling was administered to learners at 16 Confucius Institutes across eight Southeast Asian countries (N = 737), all with prior experience using AI chatbots for Mandarin learning. A hypothesized model was tested via structural equation modeling (SEM) to assess direct effects on motivation, satisfaction, and continuance intention, as well as indirect (mediated) effects via motivation and satisfaction.ResultsPerformance expectancy, effort expectancy, social influence, and facilitating conditions each had significant positive effects on learning motivation and learning satisfaction. For continuance intention, performance expectancy, effort expectancy, and facilitating conditions showed significant direct effects, whereas the direct effect of social influence was non-significant. Learning motivation and learning satisfaction acted as critical mediators, transmitting the effects of social influence and technological perceptions to continuance intention, thereby strengthening sustained engagement.DiscussionFindings support a unified cognitive-affective model of technology continuance in AI-mediated language learning. To enhance sustained chatbot use, stakeholders should: (1) raise perceived usefulness through curriculum-aligned tasks and feedback, (2) reduce effort via intuitive design and scaffolding, (3) improve facilitating conditions (training, access, support), and (4) cultivate motivation and satisfaction through adaptive, engaging learning experiences. Although social influence alone does not directly drive continuance, it indirectly promotes sustained use by elevating motivation and satisfaction.
Songyu Jiang
1 week 2 days ago
Vaccine-preventable diseases remain a global threat to public health, and despite the proven effectiveness of vaccines in protecting against these diseases, some people still avoid vaccinations for themselves or their children. Since barriers to protective health behaviors are diverse, overcoming them, and thereby reducing immunization gaps, necessitates diverse strategies. One such approach is to leverage health education through digital formats like online platforms and game-based learning, which can be implemented across geographical borders. We report a large-scale test of the World Health Organization’s Immune Patrol, a game-based middle school curriculum on vaccines and immunization. Using a matched quasi-experimental trial, classes in Armenia and the Republic of Moldova were exposed to local language versions of either five fully game-based lessons on topics including immunization, preventable diseases and digital source criticism, or non-game control materials on the same topics matching the digital and physical activities, without overt game elements. Significant learning outcomes were found across both countries, yet with differing gains among the classes within each country. Students assigned to game-based learning were slightly more motivated to learn about vaccine-preventable diseases, and to actively protect people around them through vaccination following the Immune Patrol experience. Qualitative observations highlight how practical and cultural differences can challenge homogenous implementation of learning packages like Immune Patrol across borders, but also how technology-enhanced collaborative classwork is welcomed in different ways across disparate classrooms. Results support the feasibility of global deployment of digital health education, but underscore the need for local support, and sensitivity to variations in culture, resources, teaching approaches and national education systems, when digital education initiatives like WHO’s Immune Patrol are set to transcend global borders.
Andreas Lieberoth
1 week 3 days ago
To address these critical gaps, although many research studies imply that argument mapping can enhance writers' abilities in argumentation and critical thinking, we need to consolidate the available evidence as the first step to better understand the scope and coverage of these tools. Therefore, this paper adopts a scoping review methodology guided by the PRISMA-ScR framework. Through this systematic scoping approach, we aim to map the breadth of research on argument visualization and learning by identifying key themes and fields and highlighting gaps in empirical studies. By synthesizing existing evidence across disciplines and contexts, we hope that the scoping review will provide a foundation for future research directions, particularly in the areas of experimental validation and pedagogical design. This consolidation is especially timely given the rapid expansion of online and hybrid learning environments where such tools could provide crucial cognitive scaffolding.
Daniel Chang
3 weeks 1 day ago
The development of Multimodal Large Language Model (MLLMs) offers new technological support for cultivating design thinking and innovation capability in medical education. However, the current training of medical professionals remains predominantly centered on knowledge memorization and one-way didactic instruction. The systematic integration of artificial intelligence and innovation methodologies is still insufficient, while challenges such as limited interdisciplinary integration and inefficient teaching iteration have constrained the cultivation of innovative literacy. To address these challenges, this study constructs the “MLLM+EDIPT” integration framework, which deeply couples the design thinking model from Stanford University's D.school with MLLM technology. It systematically elucidates the cognitive support mechanisms of MLLM across the stages of empathy, definition, ideation, prototyping, and testing. Targeting diverse stakeholders, including hospitals, universities, educators, and students, this study proposes a phased cultivation strategy and competency framework based on school-clinician collaboration. This framework emphasizes the full integration of the “human-centered” philosophy, leveraging AI to enhance situational awareness, feedback timeliness, and methodological rigor, thereby driving the transformation of teaching models from experience-driven to intelligent collaboration. Ultimately, this research aims to provide a theoretically grounded and practically viable pathway reference for the cultivation of top-tier innovative medical talents in the AI era.
Qing Liu
3 weeks 2 days ago
Available studies have examined game’s impact on music education from the learning outcome perspective. This sequential mixed-method design attempted to explore the impact of video-game music as Electone pedagogy on students’ learning process and outcome. The study aimed to explore learners’ motivation, engagement, skills, and creativity and teachers’ challenges in applying this approach. The participants involved 50 students and 2 Electone teachers. The quantitative and qualitative data were collected using t-tests and interviews. The quantitative results exhibited that incorporating the video game Honor of King could boost students’ levels of motivation, engagement, Electone skills, and creativity. The challenges to utilising this game were the necessity of aligning game mechanics with Electone theory. Students may occasionally prioritise gameplay over instrumental accuracy, which could potentially detract from the achievement of Electone learning objectives. This study also offers pedagogical implications on how to apply games as a pedagogical tool in the Electone course.
Jiachen Wei
3 weeks 3 days ago
This study investigated minority students’ learning in programming. The variables, including self-efficacy, computational thinking, and learning performance were the focus of this study. This study explored the relationships of creative self-efficacy, learning self-efficacy, computational thinking, and learning performance among minority undergraduate students. The influence of creative self-efficacy, learning self-efficacy, and computational thinking on learning performance was explored. The participants were minority students from a HBCU institution in the southeastern United States. Quantitative approaches were performed to analyze the collected data. The results indicated that self-efficacy, learning self-efficacy, and computational thinking were positively correlated with learning performance. Learning self-efficacy and computational thinking were significant predictors of learning performance among minority students.
Yu-Tung Kuo
1 month ago
Razan Khasawneh
1 month ago
This study investigates how Chinese undergraduate music students’ perceptions of AI-generated content (AIGC) are affected by generative artificial intelligence (GAI). To explain students’ acceptance of generative AI, the study integrates the Stimulus–Organism–Response (SOR) framework with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A mixed-method approach was employed, involving 600 university students through quantitative surveys and qualitative interviews. The analysis explores students’ responses to the uncanny valley effect, perceived usefulness and ease of use of anthropomorphic features, and intention to adopt the technology. Findings indicate that although the human-like aspects of AIGC cause discomfort, quantitative data show that students find components like voice interaction and emotional expression helpful for learning music. Qualitative evidence further reveals adaptive strategies to mitigate discomfort, including integrating AIGC with peer review. The study concludes that AIGC holds significant potential for enhancing music education but underscores the need to address the uncanny valley effect to foster greater emotional engagement. To better accommodate diverse student needs, future research should investigate potential long-term effects and support the development of customized AIGC tools.
Jingyuan Tan
1 month ago
IntroductionThe dominant paradigm of generative artificial intelligence (AI) in education positions it as an omniscient oracle, a model that risks hindering genuine learning by fostering cognitive offloading.ObjectiveThis study proposes a fundamental shift from “AI as Oracle” model to a “Cognitive Mirror” paradigm, which reconceptualizes AI as a teachable novice engineered to reflect the quality of a learner’s explanation. The core innovation is the repurposing of AI safety guardrails as didactic mechanisms to deliberately sculpt AI’s ignorance, creating a “pedagogically useful deficit.” This conceptual shift enables a detailed implementation of the “learning by teaching” principle.MethodWithin this paradigm, a framework driven by a Teaching Quality Index is introduced. This metric assesses the learner’s explanation and activates an instructional guidance level to modulate the AI’s responses, from feigning confusion to asking clarifying questions.ResultsGrounded in learning science principles, such as the Protégé Effect and Reflective Practice, this approach positions the AI as a metacognitive partner. It may support a shift from knowledge transfer to knowledge construction, and a re-orientation from answer correctness to explanation quality in the contexts we describe.ConclusionBy re-centering human agency, the “Cognitive Mirror” externalizes the learner’s thought processes, making their misconceptions objects of repair. This study discusses the implications on assessment, addresses critical risks, including algorithmic bias, and outlines a research agenda for a symbiotic human-AI coexistence that promotes effortful work at the heart of deep learning.
Hayato Tomisu
1 month ago
IntroductionUsing digital technologies helps enhance learners' interest and participation in classes such as Integrated Natural and Social Sciences (IPAS). The purpose of this study is to create animated interactive learning multimedia using Scratch that aims to teach Indonesian cultural heritages that have received international acknowledgment in a stylized and engaging way.MethodsThe research and development (R&D) of the interactive learning multimedia, Scratch Wabimendu, was conducted using the ADDIE model, which consists of analysis, design, development, implementation, and evaluation. The effectiveness of the intervention was evaluated through a quasi-experimental study employing a non-equivalent control group design. Data were collected through in-depth interviews with three experts in game-based learning, education, and software development.ResultsExperts reported that the implementation of Scratch Wabimendu MIL (Multimedia Interactive Learning) resulted in increased engagement, motivation, and comprehension among learners. The findings also revealed that the media was successfully developed, and students who were exposed to it exhibited significant improvements in learning outcomes. Inferential analyses demonstrated these improvements, with normalized gains of 0.493 for the experimental group and 0.209 for the control group (p < 0.001), accompanied by a substantial effect size (Cohen's d > 1.726).DiscussionThus, Scratch Wabimendu has been well developed and serves as an effective instructional media for enhancing the learning outcomes of learners while fostering a greater understanding of Indonesia's cultural history and heritage.
Ika Ari Pratiwi
1 month ago
The proliferation of eBooks has significantly impacted traditional paper books. With the development of emerging technologies like artificial intelligence (AI), AI eBooks have an even greater impact on traditional paper books, influencing aspects such as reading and learning methods, the dissemination of textual content, and book design, especially in education. To explore the students' acceptance of AI eBooks, this study utilizes a latent class analysis (LCA), dividing the student sample into groups with weak and strong digital literacies. Subsequently, the entire sample, as well as subgroups of students with weak and strong digital literacies, are examined as distinct entities to construct a multi-class structural equation model (SEM) incorporating the technology acceptance model and theory of planned behavior. Three SEMs aim to investigate the acceptance of AI eBooks among student groups with varying levels of digital literacies. Results reveal significant heterogeneity in the acceptance of AI eBooks among the student population, with groups possessing weak and strong digital literacies accounting for 30.62% and 69.38%, respectively. For the entire student sample, perceived usefulness emerges as the most crucial factor influencing their acceptance of AI eBooks. For students with weak digital literacies, enhancing the practicality of AI eBooks could increase their acceptance levels; similarly, for those with strong digital literacies, improving the ease of use of AI eBooks could improve their acceptance.
Haijin Xie
1 month 1 week ago
IntroductionThe COVID-19 pandemic accelerated global online education, which faces “shallow learning” challenges. Deep learning is key to student competencies. Based on sociocultural theory, this study explored how teachers' teaching strategies affect online deep learning and the mediating role of learning interactions.MethodsStratified cluster sampling was used to select students from six central Chinese provinces, yielding 10,028 valid samples. The instruments included a revised NSSE scale (deep learning, α = 0.889), the PISA2018 questionnaire (teaching strategies, α = 0.790), and 2-item learning interaction test. Data were analyzed using descriptive statistics, regression, SEM, and Bootstrap tests; no significant common method bias existed.ResultsAll scales had good reliability and validity. SEM showed that teaching strategies positively predicted deep learning (β = 0.216) and learning interaction (β = 0.561), and that learning interaction positively predicted deep learning (β = 0.746). Learning interaction partially mediated the relationship (indirect effect = 0.418, 65.93% of the total effect). Gender had no moderating effect, and the effect of grade was negligible.DiscussionThe study supports sociocultural theory by extending offline research to online settings and clarifying the “teaching strategies → learning interaction → deep learning” mechanism. This suggests that teachers prioritize interactive online designs. Limitations include self-reported data, brief interaction scales, cross-sectional data, and regional generalizability.
Zhaojia Xu
1 month 1 week ago
College students often encounter numerous challenges throughout their academic journeys, making the guidance and support from educators indispensable. Recommendation systems can significantly reduce the difficulties students face when identifying suitable academic advisors. This paper proposes a AdVisor RecommenDation (AVRD) model based on textual data regarding college students' interests. AVRD first adopts Chinese Bidirectional Encoder Representations from Transformers (BERT) and unsupervised Simple Contrastive Learning of Sentence Embeddings (SimCSE) to train the corpus of advisors' records. The time decay factor is then introduced as the weight of the text record vectors, and the representation vectors of advisors are obtained using the weighted mean. Finally, the similarities between the advisor and student vectors are computed, and an advisor list is recommended to student according to the designed pooling and matching criteria. The questionnaire data from 170 college students are collected to evaluate the proposed model. Experimental results demonstrate the effectiveness of AVRD. The model outperforms other LLMs such as Qwen and DeepSeek by a significant margin, as well as the commonly used models like TF-IDF, LSA, and Word2Vec. Moreover, the ablation studies reveal that the SimCSE component of AVRD is crucial to the model's performance.
Xinmin Wang
1 month 1 week ago
Highly evolved and capable, ChatGPT is an intelligent chatbot with great implications for fostering active student learning due to its capacity to respond quickly to academic queries as well as to engage in dynamic interactions with the learner. In the present research which was conducted within the Saudi university context, we studied how intrinsic motivation and factors related to TAM (technology acceptance model) influenced undergraduate students’ acceptance of ChatGPT as a tool for learning actively. The study adopted a structural equation approach to investigate the extended TAM model in tertiary education. The results of the revealed that intrinsic motivation, perceived usefulness, and perceived ease of use were found to be significant predictors of behavioral intention. Finally, the study highlights that AI-based tools as user-friendly, beneficial, engaging and intriguing promote students’ active learning and enhance their involvement in the learning process and, thus, their acquisition of new knowledge.
Muhammad Afzaal
1 month 1 week ago
This study evaluates the effectiveness of digital scenario-based English conversation teaching at the university level using Artificial Intelligence Generated Content (AIGC). This study aims to design a digital-scenario-based AIGC teaching model, to evaluate its effectiveness on the learning experience and communication skills of the students, and to identify the pedagogical and technical challenges related to it. Through the mixed-method approach that involves 130 first-year English majors at the Punjab University of Pakistan, the research applied a comparative experiment of 18 weeks (experimental group: AIGC Framework; Control Group: Traditional Methods). The results demonstrated that the AIGC model dynamically generated the pronunciation, language accuracy, and communication flow compared to the scenario-generated, interactive functions, and the personal response in real-time. Additionally, the model increased learning interest, work adaptability, and teacher-student interactions. However, challenges included the quality of incompatible material, limited emotional depth in AI interaction, technical adaptability barriers for less efficient students and risks of more dependence on technology. The study concludes that while AIGC provides transformational ability to learn individual, immersive language, its successful integration requires advanced teacher training, strong material review mechanisms, and analogous support for diverse learners. The recommendations highlight refining cultural relevance, ensuring moral deployment, and discovering multimodal AI integration for future educational innovation.
Muhammad Younas
1 month 2 weeks ago
IntroductionSecondary students often struggle to visualize complex biological structures, leading to low engagement and shallow understanding. These challenges are greater in resource-limited classrooms lacking laboratory equipment or modern teaching aids. To address this, we developed ScienceAR, a curriculum-aligned AR application that transforms textbook diagrams into interactive 3D models. This study evaluates its effectiveness in secondary school biology in Lahore, Pakistan.MethodsA quasi-experimental design was used with 60 ninth-grade students randomly assigned to an experimental group (n = 30) receiving AR-enhanced instruction or a control group (n = 30) receiving traditional instruction. The seven-day intervention covered challenging biology topics such as human anatomy. Data included pre- and post-tests, student surveys, teacher observations, and student feedback. Post-test scores were analyzed using t-tests and effect size.ResultsThe experimental group significantly outperformed the control group (81.0% vs. 76.1%, t(58) = 2.36, p = 0.022, Cohen's d = 0.61). Surveys showed higher ratings for enjoyment, motivation, confidence, and clarity, all above 4.0. Teachers reported greater attentiveness, questioning, and participation in AR lessons.DiscussionAR improved test performance, engagement, and attitudes toward biology. ScienceAR demonstrates potential as a low-cost, scalable instructional tool for underserved classrooms. Limitations include the short intervention and single-site design. Future research should explore long-term impacts, cross-subject applications, and teacher training for broader implementation.
Muhammad Awais Sattar
1 month 3 weeks ago
Emotional development in childhood is an aspect of undeniable importance, considering that this is a key stage for the acquisition of the emotional and social skills required for life in society. This study analyzes the effects of implementing a teacher training program for children's emotional development based on the establishment of emotional bonds and the validation of emotions in early childhood education. The strengthening of children's emotional development—focused on the establishment of emotional bonds and the validation of emotions—increases the number of interactions linked to these processes, especially those of emotional validation. The objective of this research was to analyze the effectiveness of a program developed to strengthen children's emotional development—focused on the establishment of emotional bonds and the validation of emotions. A quasi-experimental pretest-posttest design was used, focusing on comparing pre- and post-intervention results between an experimental and a control group in two international contexts (Spain and Costa Rica). The evaluation of the training program’s effect, carried out in both groups, included a pretest and posttest of the abbreviated and translated into Spanish scale of the Student-Teacher Relationship Scale (STRS), as well as a record of the frequency of emotional validation actions during three days of classroom work. The data were analyzed with descriptive statistical tests and various types of analysis to analyze the intervention effects using Mixed Effects Models. The pretest and posttest analyses showed a significant improvement in some of the aspects of affective bonding measured with the Student-Teacher Relationship Scale (STRS) and an increase in emotional validation actions present in teacher-child interactions. These findings suggest that a teacher training program focused on emotional validation and strengthening affective bonds can lead to greater closeness between teachers and children, as well as a reduction in classroom conflicts.
Carolina Vargas Pana
1 month 3 weeks ago
IntroductionThe current study investigated how students use artificial intelligence (AI) tools to enhance their learning in course activities, focusing on an advertising art course in a fine arts bachelor’s program at a Saudi Arabian university in Fall 2024.MethodsThis study used the Saudi Artificial Intelligence in Digital Learning (AIDL) framework and Technological Pedagogical Content Knowledge (TPACK) model to design AI activities in art classrooms. The research examined students’ perspectives on AI activities used in the course and the impact of using AI as a technological tool. A mixed-methods pilot design was used to produce an in-depth description of a small group with a specific duration and location. The sample of participants was purposely chosen: 30 female undergraduate students who registered for the course. Data were collected through the syllabus, observation, a survey, pre-and post-course tests, focus group discussions, and document and artifact analyses.ResultsSix themes emerged from the data. AIDL was revealed to be valuable and AI tools were deemed helpful in teaching and learning art and design concepts. The researcher integrated AI into their curriculum to help students understand the course requirements and improve their learning.DiscussionThe results indicate that these tools empower students to discover their unique talents and refine their skills in the arts and that teachers should incorporate TPACK and AIDL into curricula. Additionally, the findings imply that utilizing AI tools is essential for transforming teaching and learning methodologies in not only art and design but also other subjects. Finally, it benefits students’ understanding of course requirements and helps teachers effectively organize their courses.
Nouf Alsuwaida