Frontiers in Education: Digital Learning Innovations

IntroductionTraditional teaching methods have been central to education systems worldwide. Still, digital innovation in higher education has transformed how education is delivered, introducing new teaching models and enhancing school management processes. However, most Higher Education Institutions (HEIs) face numerous limitations in effectively adopting digital systems, although these challenges are often context-specific.MethodsTo gain a deeper understanding of the scope and limitations of digitalization in higher education institutions, we conducted a comparative analysis of existing digital ecosystems across developed and developing countries.ResultsWe introduced a three-layer framework for digitalization comprising institutional strategies, technological tools, and pedagogical methods. These layers are further divided into several components that support innovation. Using data from multiple sources, we identified several current drivers of the adoption of digital innovation in universities, including student expectations and needs, technological advances, strategic leadership, and the rise of hybrid learning models. Additionally, we compared the rate of digitalization in HEIs between developed and developing countries; our findings indicated that innovation proceeds more smoothly and is easier to manage in developed nations. We highlighted strategies for scalable and sustainable innovation through successful case studies from well-known universities. To tackle systemic challenges in digitalization, their impacts, potential solutions, and levels of implementation, we employed a priority matrix approach. This approach showed that most challenges are quick wins when addressed and have a substantial impact on digitalization.DiscussionThis study emphasizes the importance of adaptive, inclusive, and strategic approaches that promote equity and encourage the adoption of global learning ecosystems by comparing digital innovation in developed and developing countries.

The COVID-19 pandemic forced higher education institutions in resource-constrained contexts to rapidly adopt online learning, yet little is known about how students in Sub-Saharan Africa experience and evaluate these platforms, particularly when facing severe infrastructure barriers. Understanding student satisfaction in these contexts is critical for ensuring educational equity and informing evidence-based policy. This study investigates student satisfaction with Google Classroom at the Faculty of Engineering—Lúrio University in Mozambique, where 220 students participated in a mixed-methods study during the 2020–2021 academic year. Using the Technology Acceptance Model (TAM) as a theoretical basis, the research examined overall satisfaction, demographic differences, challenges faced, and valued features of the platform. Results showed moderate satisfaction levels (M = 3.18, SD = 1.12 on a 5-point scale), with 35% of students expressing satisfaction and 28% expressing dissatisfaction. Internet connectivity quality was identified as the most significant factor affecting satisfaction (p < 0.001), while device type also influenced results (p < 0.05). Qualitative data identified six main challenges: connectivity issues, mobile data costs, social isolation, technical difficulties, limited interaction, and workload concerns. Students valued the platform’s flexibility, accessibility of materials, organization, and ease of use. The study emphasizes the need for infrastructure investment and targeted support systems for effective online learning, especially in resource-limited settings, providing useful insights for improving higher education quality during and after the pandemic.

Despite growing interest in educational digital leadership and supervision, validated instruments measuring supervisors’ digital competencies for their performance excellence (SDC-PE) remain scarce, particularly in developing nations. This study developed and validated the SDC-PE scale through stakeholder perceptions of 318 principals and teachers across 78 senior high schools in 17 districts of South Sulawesi, Indonesia. The SDC-PE integrates classical supervision theories (Cogan, Goldhammer, Glickman) with ISTE Standards and DigCompEdu 2.2, yielding four dimensions: Strategic digital leadership (SDL), digital culture promotion (DGC), teacher digital development (TDD), and digital learning facilitation (DGL). Confirmatory composite analysis (CCA) using PLS-SEM demonstrated excellent psychometric properties: factor loadings (0.839–0.936), composite reliability (0.933–0.955), AVE (0.776–0.811), and model fit indices exceeding thresholds (CFI = 0.968, TLI = 0.960, RMSEA = 0.072). Discriminant validity was confirmed through Fornell-Larcker criterion and HTMT ratios (<0.85). The SDC-PE provides educational institutions with an empirically validated assessment tool for evaluating supervisor digital competencies, designing targeted professional development.

Within the global movement of cultural revival, the modern translation of poetic imagery (defined as the process of transmuting classical poetic symbols along with their emotional and cultural connotations into modern design elements) has emerged as a critical concern in Chinese design education, presenting a central tension between AI-driven efficiency and cultural depth. This study addresses three structural faults in current translation practices: fragmented symbolic extraction, weakened nostalgic drive, and over-reliance on AI tools. It establishes a tripartite efficacy evaluation framework encompassing emotion, cognition, and market dimensions, as well as a dual-cycle educational model featuring critical and iterative phases. A controlled experiment with 22 second-year product design majors (divided into an AI-assisted group and a traditional group) was conducted over a 4-weeks design psychology course, focusing on war, boudoir, and pastoral poetry themes. Results show that the AI-assisted group outperformed in emotional resonance (4.22 ± 0.38 vs. 3.54 ± 0.47) and market responsiveness (81.3% ± 8.2% vs. 64.1% ± 10.7%), while the traditional group maintained an advantage in cognitive completeness (83.7% ± 5.9% vs. 80.3% ± 5.1%). The dual-cycle model effectively reduced cultural misinterpretation rates in the AI group from 33% to 12%. Meanwhile, this study proposes the “Nostalgia-Congruent AI Guidelines (NCAI-G),” which regulates AI application from three aspects: symbolic fidelity, nostalgia coherence, and user safety. This study provides a reusable educational framework for balancing AI instrumental rationality and cultural value rationality, advancing traditional cultural design education toward quantitative evaluation-driven iteration.

Student feedback literacy is vital for effective use of feedback. While traditional peer review activities provide opportunities for students to practice giving and receiving feedback, their effectiveness is sometimes undermined because of interpersonal factors, such as friendship and psychological safety. Generative artificial intelligence (GenAI) offers a promising new avenue by providing adaptive and instant feedback; however, its effectiveness compared to traditional peer interaction and the underlying mechanisms remain underexplored and warrant further investigation. This study used a mixed-methods design with first-year undergraduates to explore the effect of GenAI and human peer feedback on student feedback literacy development. The study also analyzed the role of students’ self-regulated learning (SRL) as a mechanism explaining how these two feedback sources contribute to enhancing feedback literacy. The results revealed that GenAI yielded a small but significant improvement in developing feedback literacy compared to human peers. Qualitative analysis clarified this finding by uncovering behavioral differences between the two groups, highlighting GenAI’s specific support for the SRL process, especially in goal setting, planning, critical evaluation, and immediate self-reflection. These findings suggest that GenAI is powerful in fostering feedback literacy because it facilitates self-regulatory behaviors essential for effective interaction with feedback. Educators can strategically integrate GenAI in classroom activities to scaffold self-regulatory behaviors, fostering student feedback literacy development.

Virtual reality (VR) as a form of simulation-based learning can lead to better understanding of learners and increase motivation. Recent evidence shows effectiveness in teacher education in terms of skill growth for student teachers. In this regard, the perceived usefulness of a novel technology is a key factor affecting behavioral intention to use it. Therefore, this mixed-method study investigates the perception of the usefulness of a VR environment from the perspective of student teachers and explores to what extent the change in perception affects the intention to use it in later professional practice. To answer the questions, N = 57 student teachers from four countries assessed a VR environment designed for teaching mathematics. The VR environment deals with the spread of infectious diseases to address the mathematical issue of exponential growth. To assess its usefulness as well as its general potential, students filled in a questionnaire before and after the VR simulation and participated in an interview afterwards. The findings show a significant positive change in usefulness beliefs. Furthermore, perceived usefulness predicted intention to use the VR technology. Results from the interviews show the potential of the new technology in transcending certain boundaries of everyday teaching and emphasize on the affective component of a VR experience in schools. But also limitations for every day life and use of VR, for instance, with regard to specific age groups were mentioned. A main implication of this study is that an early experience of VR in teacher education underlines the willingness to use this technology in later professional life.

IntroductionTraditional approaches to teaching physics often struggle to engage students and to convey abstract concepts such as gas laws in a meaningful way. This challenge is particularly evident for learners accustomed to interactive and technology-mediated environments. Recent advances in embodied cognition and active learning suggest that multi-sensory interaction may enhance engagement and conceptual understanding. Visuo-haptic simulators represent a promising approach by combining visual and tactile feedback to support experiential learning.MethodsThis study developed a visuo-haptic simulator designed to support the exploration of Boyle's Law through interactive manipulation of pressure and volume variables. The simulator provided real-time visual feedback and proportional haptic resistance to represent changes in gas behavior. Thirty-nine undergraduate engineering students interacted with the simulator in controlled laboratory sessions. A mixed-methods approach was used to evaluate students' perceptions, combining the End-User Computing Satisfaction (EUCS) survey with semi-structured interviews.ResultsSurvey results indicated high levels of satisfaction in the dimensions of accuracy, ease of use, and timeliness, reflecting students' confidence in the simulator's responsiveness and reliability. Qualitative findings revealed strong engagement and motivation, with participants reporting that tactile feedback helped them intuitively understand the inverse relationship between pressure and volume. Some usability challenges related to interface layout were also identified.DiscussionThe findings suggest that visuo-haptic simulators can promote active engagement and support embodied understanding of abstract physics concepts by linking theoretical relationships to sensory experience. Students perceived the simulator as a valuable complement to traditional instruction and expressed interest in its application to other scientific topics. While learning outcomes were not directly measured, the results highlight the potential of visuo-haptic tools to enhance motivation and experiential learning in physics education. Future work will focus on assessing learning gains in classroom settings and extending the approach to additional thermodynamics concepts.

IntroductionAs artificial intelligence (AI) becomes increasingly embedded in educational environments, understanding its role in shaping learners’ self-regulated learning (SRL) and self-directed learning (SDL) has emerged as a central concern in contemporary learning science. While prior studies suggest that AI-driven systems may support planning, monitoring, and autonomy in learning, empirical evidence remains fragmented across contexts, learner groups, and instructional designs. This study synthesizes existing empirical research to systematically examine the magnitude and conditions under which AI-based interventions influence SRL, its dimensions and phases, SDL, and associated learning outcomes.MethodsA systematic meta-analysis was conducted following PRISMA guidelines, synthesizing evidence from 32 empirical studies comprising 92 effect sizes and a total of 3,029 participants. The analysis examined overall effects of AI-based interventions on SRL and SDL, disaggregated effects across SRL dimensions (cognitive/metacognitive, motivational/affective, and behavioral regulation) and SRL phases (forethought, performance, and self-reflection), as well as impacts on learning outcomes and academic achievement. Random-effects models were applied, and moderator analyses explored learner characteristics, contextual variables, and AI design features. Sensitivity analyses and publication bias assessments were performed to evaluate the robustness of findings.ResultsAI-based interventions demonstrated a large and statistically significant positive effect on overall SRL (g = 1.613, p = 0.032) and SDL (g = 1.111, p = 0.043), indicating substantial improvements in learners’ ability to plan, monitor, and regulate their learning while sustaining autonomy and persistence. At the dimensional level, AI produced moderate gains in cognitive/metacognitive regulation (g = 0.377, p = 0.0004) and motivational/affective regulation (g = 0.505, p = 0.013), whereas effects on behavioral regulation were inconsistent. Phase-level analyses revealed that AI interventions were most effective during the forethought phase, supporting goal setting, planning, and motivational readiness, with smaller but significant gains observed in self-reflection and variable effects during the performance phase. AI systems also yielded moderate improvements in learning outcomes and achievement (g = 0.350, p = 0.034). Moderator analyses indicated stronger SRL effects among older learners, longer intervention durations, and language learning contexts employing interactive AI systems, while gender differences were minimal. Sensitivity and publication bias tests confirmed the stability of results.DiscussionThe findings indicate that AI functions as an adaptive scaffold that meaningfully enhances learners’ self-regulatory and self-directed capacities across cognitive, motivational, and reflective processes. By strengthening forethought and planning mechanisms in particular, AI-based interventions support more autonomous, sustained, and effective learning behaviors that translate into measurable academic benefits. Variability in behavioral regulation outcomes highlights the need for more explicit action-level supports in AI design. Overall, the results showcase AI’s potential to promote equitable and scalable self-regulated learning across diverse educational contexts, while also pointing to the importance of aligning intervention design with learner characteristics and instructional goals.

While artificial intelligence (AI) is transforming many sectors, its integration into pre-service teacher education in higher education remains limited. This study investigates the iterative development and effects of a concise, two-session educational intervention designed to foster AI literacy among pre-service physics teachers. Following a design-based research approach, the intervention was implemented in two iterations at the University of Cologne (n = 31 across two cohorts). Structured according to the 5E instructional model, the intervention required students to use generative AI tools as didactic instruments to create lesson plans and reflect on their usage. AI literacy was measured using a validated 30-item test, while attitudes toward AI were assessed via a 4-point Likert survey. Results indicate only small, non-significant increases in overall AI literacy, with selective gains observed in competencies explicitly supported by hands-on activities and targeted scaffolding. However, attitudinal measures demonstrated that even brief interventions can strengthen participants’ openness toward AI and their perceived preparedness to use AI tools in teaching. Additionally, the iterative comparison highlighted format-sensitive effects. These findings suggest that while short design-based interventions can selectively activate elements of AI literacy and foster professional confidence, they are insufficient for broader skill Acquisition. Consequently, more sustained, context-rich engagements are likely required to achieve comprehensive and durable AI literacy development in pre-service teacher education.

Artificial intelligence (AI) is rapidly being embedded into all aspects of human life, reshaping everything from mundane daily human interactions to national military strategies. With AI technological capabilities limited to only a handful of parties, nations must grapple with the effects of relying on foreign technology on their own digital sovereignty, which is defined as a nation’s ability to control its digital infrastructure, data flows, and epistemic frameworks. This paper traces the recent AI educational policies of China and the United States—the world’s leading economic and technological powers. Analyzing state discourse, policies and governance between 2017 and 2025 this paper argues that the new AI race has revitalized the discourse on digital sovereignty. AI education is now a core feature of national security, workforce competitiveness and cultural sovereignty. This framing elevates AI from a tool of innovation to an instrument of geopolitical power, and places education, skills and capacity building at the heart of this strategic landscape.

Learning via online platforms is gaining traction among university students globally. Prior research has demonstrated that students pursuing majors in medicine, linguistics, and computer science have reaped benefits from online learning platforms. Will Chinese students persist in utilizing online platforms for ongoing learning? This study establishes a novel model, drawing on data from 435 undergraduate students majoring in civil engineering in China. This model amalgamates user experience, the Technology Acceptance Model (TAM), and the Expectation Confirmation Model (ECM), providing an in-depth analysis of the factors influencing the willingness to continue using the Bilibili online platform. The findings reveal that: (1) the new model exhibits good fit; (2) 12 out of 13 hypotheses are supported, with functional experience showing no significant influence on satisfaction and intention to continue using; (3) perceived ease of use has a more pronounced impact on satisfaction and intention to continue using compared to perceived usefulness

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.

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.

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.

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.

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.

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.

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.