Frontiers in Education: Digital Learning Innovations
2 days 9 hours ago
Artificial intelligence is gaining traction in higher education for its ability to simulate human intelligence and support learning processes. This systematic review investigates how artificial intelligence-enhanced teaching approaches are being applied in higher education institutions across the Global South. The study draws on peer-reviewed literature identified through a structured search of JSTOR and Web of Science databases, using clearly defined inclusion and exclusion criteria. The findings reveal that most applications focus on improving technical efficiency and administrative functions, while pedagogical integration remains limited. Key barriers include inadequate infrastructure, unequal access to digital tools, limited faculty preparedness, and ethical considerations. However, the review also highlights opportunities for locally adapted solutions and collaborative innovation. The study concludes with recommendations to guide policy and practice and outlines a future research agenda aimed at promoting equitable and context-sensitive use of artificial intelligence in higher education within the Global South.
Nomfundo Gladys Khoza
2 weeks 2 days ago
IntroductionThe learning process is characterized by its variability rather than linearity, as individuals differ in how they receive, process, and store information. In traditional learning, taking into consideration the individual differences between students can be difficult. As a result, many talented students may fail because their learning speed does not align with the assessment requirements.ObjectivesIn this study, we propose efficiency algorithm as a new assessment method for adaptive learning (AL), based on artificial intelligence, to evaluate differences in students’ learning speed and help ensure the graduation of competent professionals in their discipline.MethodsOur assessment method was based on how effectively students apply the knowledge they have acquired to complete tasks. Using four important parameters that always answer the question of how the student completes rather than its completion. These parameters were information search, information evaluation, information processing, and information communication, which together constitute the basic components of our efficiency algorithm.Key findingsOur results showed that, by using the Naïve Bayes algorithm, we can determine with high accuracy (93%) in which part of the learning process (information search, information evaluation, information processing, or information communication) the student encounters difficulties.ContributionOur proposed approach helps in designing personalized learning plans that directly target individual weaknesses.
Arwa Hasan Zabian
2 weeks 3 days ago
Mobile learning (ML) was widely adopted during the coronavirus disease 2019 (COVID-19) pandemic, but its sustained use post-pandemic is not guaranteed. This study identifies the factors influencing university students’ intention to continue using ML. Using the Unified Theory of Acceptance and Use of Technology (UTAUT-2) model, data from 445 students at King Faisal University were analyzed via structural equation modeling. The results showed that attitude toward ML was significantly influenced by effort expectancy (β = 0.620, p < 0.001), performance expectancy (β = 0.521, p < 0.001), and hedonic motivation (β = 0.313, p < 0.001). For continuous intention, habit was the strongest predictor (β = 0.445, p < 0.001), followed by hedonic motivation (β = 0.471, p < 0.001) and attitude (β = 0.175, p < 0.05). Performance expectancy, effort expectancy, social influence, and facilitating conditions had no significant direct effects on continuance intention. These findings confirm habit as the cornerstone of post-pandemic ML continuance, highlighting a shift from utilitarian factors to automated use and enjoyment. Post-pandemic ML integration must strategically foster habitual use and enhance enjoyment, moving beyond utility-focused approaches. This study provides evidence-based insights for educational leaders and platform developers to guide ML’s sustainable integration.
Ahmed Abdulhameed Al Mulhem
2 weeks 4 days ago
This study investigated the impact of AI-generated graded reading materials on the oral proficiency of adult EFL learners in a six-month intervention. Ninety participants generated weekly texts using proficiency-aligned prompts and were assessed through pre- and post-intervention ACTFL Oral Proficiency Interviews, complemented by learner reflective journals. Quantitative results suggested significant proficiency gains across all initial levels, while thematic analysis of journals highlighted perceived benefits in vocabulary development, autonomy, and fluency. Together, these findings provide preliminary evidence consistent with Krashen’s Input Hypothesis, while also linking AI-mediated reading to broader frameworks of scaffolding, vocabulary acquisition, and cognitive load management. At the same time, important limitations must be noted. The study relied on a single non-certified rater, lacked a control group, and did not systematically monitor the linguistic properties of AI-generated texts. Attrition was concentrated among Novice High learners, raising concerns about bias in proficiency outcomes. These constraints require cautious interpretation, and the results should be viewed as suggestive rather than definitive. Despite these limitations, the study contributes to current discussions on AI in language education by illustrating how generative tools can provide scalable, proficiency-aligned input. It offers preliminary insights into the potential of AI-mediated reading to support oral proficiency development, while underscoring the need for more rigorous designs in future research.
Hesham Aldamen
3 weeks ago
IntroductionThe increasing integration of technology in language teaching has led to a significant focus on chatbots, which utilize various artificial intelligence models tailored to English as a Foreign Language education. While the effectiveness of chatbots in enhancing language skills is well documented, their influence on emotional intelligence (EI) has not been thoroughly examined. This study explores the impact of chatbot use on EI among sophomore pre-service English language teachers in Istanbul, Türkiye.MethodsUtilizing the Turkish-adapted version of Bar-On's Emotional Quotient Inventory (EQ-I), quantitative methods were employed to examine the relationship between frequent interactions with ChatGPT and EI levels.ResultsThe results indicate a moderate, statistically significant negative correlation, suggesting that increased use of ChatGPT may be associated with lower EI. A slight improvement was noted in adaptability, though this trend should be interpreted cautiously.Discussion and conclusionThe study highlights the complex and context-dependent role of chatbots in shaping emotional competencies in language education and points to the need for further investigation. The results contribute to ongoing discussions on AI in educational settings by underscoring the importance of carefully balancing potential benefits with challenges to support both language proficiency and emotional development.
Semin Kazazoglu
3 weeks 3 days ago
Adaptive learning systems (ALSs), powered by artificial intelligence (AI), represent a transformative approach to biotechnological and pharmaceutical education that addresses the critical limitations of traditional standardized pedagogy. This review highlights empirical evidence demonstrating how ALS dynamically personalizes learning through knowledge state modeling (KSM) and the synergistic integration of knowledge level (KL) and knowledge structure (KS) dimensions. This framework enables mastery-based progression in sequential domains (e.g., genetic engineering and pharmacodynamics), ensuring foundational competency before advancement. In addition, key applications of adaptive learning (AL) in the field of biological and pharmaceutical education are also detailed, including scaffolding complex foundational sciences (e.g., real-time misconception detection in Clustered Regularly Interspaced Short Palindromic Repeats—CRISPR-associated protein 9 [CRISPR-Cas9]), enhancing technical skills via AI-driven virtual labs simulating industry workflows (e.g., High-Performance Liquid Chromatography [HPLC] and bioreactors), and navigating regulatory compliance through contextual simulations. The documented benefits include significant cost reduction, accelerated skill acquisition, and strengthened industry alignment. Nevertheless, challenges persist in terms of technical fragmentation, algorithmic bias, and equitable resource access. Finally, it is suggested that future research priorities should involve developing integrated architectures with blockchain-secured micro-credentials, human-AI synergy frameworks for ethical oversight, and equity-driven deployment via federated edge learning. The strategic implementation of ALS promises to cultivate a globally competitive, interdisciplinary workforce for next-generation biopharmaceutical innovation while establishing rigorous, regulatory-grade training.
Tao Wang
3 weeks 4 days ago
Artificial Intelligence-based Learning Tools (AI-LTs) are rapidly reshaping higher education by advancing the learning, teaching, and administrative processes. This paper offers a systematic review of peer-reviewed research, published between 2020 and 2025, by examining the roles, advantages, and challenges of the AI-LTs like ChatGPT, Deep Seek, Gemini, and Meta AI. Using a qualitative method, relevant studies were sourced from databases such as Scopus and Web of Science, by using strict criteria for the selection and extraction of data. The review highlights that the AI-LTs can significantly improve the personalized learning experiences, boost the engagement of students, and streamline the administrative operations. However, they also introduce ethical challenges like algorithmic bias and risks to data privacy. The study underscores the importance of responsible adoption of AI, advocating for the development of faculty algorithmic transparency and the robust collaboration of human-AI. Future research should prioritize empirical investigations to further validate the influence of the AI-LTs across diverse academic environments.
Muhammad Younas
4 weeks 1 day 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 month 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 month 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 month 1 week 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 month 1 week 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
1 month 3 weeks 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
1 month 3 weeks 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
1 month 3 weeks 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 4 weeks ago
Razan Khasawneh
2 months 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
2 months 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
2 months 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
2 months 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