Frontiers in Education: Digital Learning Innovations

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.