Frontiers in Education: Digital Learning Innovations

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.

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.

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.

In light of the challenges and crises the world is facing, the usage of technology has a significant impact on students’ perceptions and learning experiences. This study investigates the perceptions of Gazan university students toward online learning in the context of an ongoing crisis. Using a qualitative thematic analysis methodology, this study conducted semi-structured interviews with a random sample of fifteen graduate students (eight females and seven males) from Gaza University. It aimed to to shed light on Gazan students’ perceptions regarding online learning follwoing the events of October 7th to provide viable solutions that could help them receive appropriate education. The thematic analysis revealed several key findings. Students expressed both gratitude for the continuity of their education and significant frustration with logistical challenges, including inconsistent internet access and frequent power outages. The interviews also highlighted the psychological toll of the crisis, which often impeded their ability to concentrate and engage in online coursework. Students also have a positive perception regrding the convenient, on-demand access to lectures and course content that online learning provides. These findings underscore the dual nature of online learning in a conflict zone: it serves as both a critical lifeline and a source of new stressors. The findings are anticipated to have crucial practical implications for higher education institutions in Gaza, staff, policymakers, and the Ministry of Education aiming to design and implement strategic online education systems that are not only accessible but also resilient and supportive.

IntroductionIn recent years, virtual venue technology has been increasingly adopted in higher education. While existing studies indicate that feedback strategies can promote deep learning among college students in virtual environments, the specific mechanisms underlying this effect remain poorly understood. This study investigates how peer feedback strategies influence deep learning processes within virtual learning environments.MethodsThe study employed the ErgoLAB Environment Synchronous Cloud Platform V3.0 alongside questionnaire scales to collect multimodal data, including behavioral patterns, physiological responses (eye movements, EEG, and electrodermal activity), learning experience metrics, and deep learning outcomes.ResultsAnalysis revealed significant differences in deep learning outcomes across peer feedback strategies. The peer dialogue feedback group operating in a basic virtual interaction environment outperformed the other three experimental groups, suggesting that structured peer dialogue combined with foundational virtual interactions may most effectively support deep learning.DiscussionThe findings underscore the importance of deliberately designed peer feedback strategies to enhance deep learning in virtual educational contexts. This study addresses the need for targeted feedback interventions in virtual instruction and offers empirical evidence for the integration of virtual venues into academic curricula. It also provides practical insights for fostering innovative talent development in the context of digital transformation in higher education.

BackgroundImmersive virtual reality (IVR) is becoming increasingly important in medical education. In radiology, IVR as a tool for practicing image interpretation and diagnosis of pathologies has rarely been subject of research to date. This exploratory study investigated a self-programmed IVR application and its potential to improve radiology education for medical students.MethodsAn IVR learning environment was programmed which enables users to view 3D models of real patients and interact with them using various tools. Fourth- to sixth-year medical students (n = 26) participated in a 1 h IVR training session in small groups between November 2022 and January 2023. Subsequently, they completed an anonymous online survey comprising 37 items. Data were analyzed, with correlations examined using Spearman’s non-parametric rank correlation.ResultsThe IVR training increased students’ motivation (M = 3.6) and interest in radiology (M = 3.2) and fostered enjoyment (M = 3.7) as well as a more active (M = 3.6) and intensive (M = 3.3) engagement. IVR was considered a helpful tool to enhance the practical relevance of radiology education, to improve the immediate cognitive and psychomotor learning outcomes related to anatomy and radiology, such as interpreting cross-sectional images (M = 3.5) and identifying anatomical structures (M = 3.6) as well as pathological changes (M = 3.3) and to promote skill development (M = 3.2), learning transfer (M = 3.2) and long-term knowledge retention (M = 3.3). The usability, design, tools and didactic functions of the IVR application are strongly associated with learning process- and learning outcome-related variables.ConclusionIVR-based learning is a promising addition to traditional radiology education to enhance motivation, interest and learning. However, the success of IVR depends on its design, usability and integration into the curriculum. The study highlights the need for further research on the added value of IVR across the educational sector.

This article presents a qualitative investigation into the evolution of critical questioning that occurred in dialogic relationships between artificial intelligence (AI) and a group of higher education students. Drawing from students' class records, it attempts to understand the development of critical thinking within formal educational contexts, utilizing a five-level questioning framework: General and Defining, Specific, Applied, Integrative, and Critical Engagement. The results indicate the presence of questioning patterns centered on levels four and five—namely, the use of Integrative and Critical Engagement questions—emerging from contextualized discussions. Students' interactions with AI enabled them to observe: the diversity of questioning levels, the progression of critical thinking, areas for improvement within each working group, the encouragement of reflexivity and metacognition, engagement with complex concepts, visualization of practical concept applications, and the expansion of interdisciplinary thinking. This study contributes to the literature on education and technology by offering insights into how to structure effective dialogic interactions between students and AI systems.

The article presents a conceptual framework for the design, implementation, and analysis of multi-component summative assessment systems in an electronic educational environment. A universal model is proposed, based on a four-level hierarchy – meta-meta-model, meta-model, model, and actual assessment system. Various structures and assessment components are examined, including Bloom’s taxonomy, higher- and lower-order thinking skills, theory and practice, and the use of fuzzy logic and artificial intelligence. The processes of modeling, configuration, usage, and system analysis are described, along with the roles of the main participants—administrator, author and learner. The use of generative artificial intelligence for the automated creation of test questions is also explored. The system aims to enhance transparency, objectivity, and effectiveness of assessment in digital learning environments, offering practical solutions for modern higher education.

IntroductionCreative thinking is essential for developing high-level innovative talents. However, its underlying neuroplastic mechanisms and effective educational interventions remain underexplored.MethodsThis meta-analysis synthesizes data from 35 experimental studies (N = 14,688) to examine the effects of brain science literacy on creative thinking and its potential moderators.ResultsThe results indicate that brain science literacy has a small but significant positive effect on creative thinking (g = 0.20, p = 0.003), with stronger effects observed in teaching strategy optimization (g = 0.32), student behavioral regulation (g = 0.37), and early childhood interventions (g = 0.70). The impact on originality (g = 0.53) was significantly stronger than on fluency (g = 0.20) and the overall creativity score (g = 0.26). The intervention effects varied across educational stages, with the most substantial benefits seen in early childhood (g = 0.70) and at the university level (g = 0.30).DiscussionThese findings suggest that improving brain science literacy can promote neuroplasticity and enhance creative thinking, with varying effects across developmental stages and creative components. The benefits observed in early childhood highlight the critical importance of brain science literacy-informed educational interventions during sensitive periods of cognitive development. This study provides solid empirical support for integrating neuroscience principles into educational practice, offering practical guidance for educational policy and curriculum design. Overall, brain science literacy appears to foster creativity through a dual pathway: neuroplasticity activation and developmental stage adaptation, presenting a focused framework for evidence-based neuroeducational interventions.

Problem based learning (PBL) is based on the idea that learning is “grounded by experience. ” PBL curriculums in medical school highlight the importance of engaging students and allowing students to be the driver of their education. This mimics their work in the hospital where answers are rarely suited to a multiple-choice question, but a myriad of complex clinical questions, ethical decisions, and cost barriers. Teaching medical students from the start of medical school to handle multiple variables is an important aspect of their learning. Studies indicate that PBL students consistently score at or above the national average on board exams compared to their peers. Furthermore, evaluations of competence during clinical rotations show statistically significant advantages for PBL students in areas such as critical thinking, social and cognitive interactions, and patient comfort. Studies highlight a notable advantage in interpersonal skills among PBL students. Additionally, geographical access plays a critical role in enrollment, and personal responsibilities can hinder potential applicants from pursuing medical school. PBL can be utilized to create an environment where location-based barriers are minimized to increase the number of individuals entering the medical profession. This approach could ultimately reduce the healthcare burden and enhance medical services in underserved areas of the country. Here, we present a concise review of resources and approaches including online and digital platforms to facilitate curriculum development and implementation of flipped classrooms and independent learning that are well-suited for PBL.

This perspective piece addresses the rapid integration of generative artificial intelligence (AI) in higher education and the imperative to move beyond a purely technical understanding towards fostering critical AI literacy among students. Despite the benefits of AI in enhancing learning experiences and preparing students for a tech-driven workforce, concerns exist regarding misinformation, diminished critical thinking, ethical dilemmas, and a lack of regulatory frameworks. This perspective piece proposes a circular pedagogical framework comprising contextual preparation, guided engagement, and collective critical reflection, drawing on Vygotsky’s sociocultural theory, Freire’s critical consciousness, and Mackey and Jacobson’s metaliteracies framework. The framework aims to address three critical competency gaps: AI tool assessment, critical AI evaluation skills, and AI information literacy. The paper highlights the importance of discipline-specific AI integration and scaffolded learning, supported by student reflection and metacognition, as demonstrated in the geography seminar courses discussed in the paper. Recognizing the need for instructor AI literacy, the paper concludes by emphasizing the necessity of institutional support through targeted training and interdisciplinary collaboration to ensure AI enhances learning effectively.