High-entropy alloy electrocatalysts for water splitting: A systematic review with perspectives on machine learning and future design strategies

Developmental biology is one of the fundamental sciences for understanding the basics of life and often intersects with social justice challenges facing society. This article describes an inclusive teaching activity for students and instructors to explore the interface between developmental biology, genetic diversity, and social justice. The instructor and students will choose a recent publication and use it as the basis for exploring the roles of specific genes characterized in autism from educational, emulative, and ethical perspectives. The assignment for students will include a discussion and demonstration of developmental neurobiology and principles of gene function within the nervous system, as well as ethical considerations for how individuals, as well as society as a whole, should consider genetic variations. Two frameworks are introduced for instructors to create an inclusive learning environment, including universal design for learning and multipartiality. Resources and examples are given throughout the article for instructors to use, and a suggested rubric is also provided. A post-activity self-reflection performed by the students will facilitate their own assessment of how the teaching activity has impacted their philosophical and social perspectives on genetic diversity. The short-term goal of the activity is to promote an immediate appreciation of neurodiversity among the participating students, and the long-term goal is to demonstrate the importance of neurodiversity for developing a just society.

In recent years, higher education has increasingly emphasized the integration of Learning Design and Learning Analytics to foster more engaging, personalized, and effective learning environments. This systematic literature review investigates how these two domains interact to enhance teaching learning processes and improve educational outcomes. The review identifies key benefits and opportunities associated with this integration across three stakeholder groups: students, lecturers, and educational institutions by analyzing 55 peer-reviewed publications. The results show that learning effectiveness can be significantly enhanced through the visualization of students’ learning interactions using straightforward and user-friendly analytical approaches. Furthermore, successful implementation requires the development of lecturers’ data literacy and programming competencies, as well as the incorporation of sociocultural, psychological, and physical data to achieve a more holistic understanding of learners. The review also identifies four major research directions to guide future efforts in bridging Learning Analytics and Learning Design. Finally, the paper underscores the importance of establishing clear ethical and privacy frameworks to ensure the responsible application of Learning Analytics in higher education.

Abstract This work investigates the use of physics-informed geometric operators in tandem with latent variable models to support surrogate learning, dimensionality reduction, and generative design of airfoils. A baseline dataset was constructed via a NURBS-based airfoil parametric model with physically interpretable design variables, and discretized using two schemes: a uniform parametric and a uniform arc-length sampling. A hybrid Variational AutoEncoder (VAE) with the addition of convolutional layers was developed and iteratively refined to evade shape invalidities. A systematic comparison of reconstruction accuracy, robustness, and diversity showed that a loss function based on the mean sum of squared distances had the best performance with sufficient stability during model optimization. However, this can be only established when the reconstruction and Kullback-Leibler terms in the β-VAE objective function are weighted via an appropriately selected β value. Additionally, augmenting geometry with physics-correlated high-level descriptors, such as geometric moments, further improves latent-space quality. Among the tested operators, third-order geometric moments yielded the most consistent robustness gains. Discretization and achieved diversity proved to be linked, with uniform arc-length spacing achieving the best reconstruction accuracy, but with many near-identical designs that degraded the resulting diversity. In contrast, uniform parametric spacing exhibited higher diversity without the need for any special treatment of design distributions and diversity quantification measures. This study consolidates practical guidelines on architecture, loss-function scaling, physics-informed features, and quantification protocols for reliable, data-efficient airfoil generative design with VAEs.

This study presents a systematic review on the integration of Universal Design for Learning (UDL) in mathematics education to advance inclusive and equitable Scholarship of Teaching and Learning (SoTL) in South African higher education. Guided by the PRISMA 2020 framework, 126 records published between 2010 and 2025 were identified across Scopus, Web of Science, ERIC, Sabinet, and Google Scholar, with 42 studies meeting the inclusion criteria after quality appraisal. The review synthesises empirical and conceptual evidence on how UDL principles—multiple means of engagement, representation, and action/expression—are applied to promote participation, accessibility, and epistemic justice in mathematics education. Findings highlight persistent barriers, including curricular rigidity, inequitable assessment practices, resource limitations, and linguistic exclusion. However, enabling conditions such as culturally responsive pedagogy, multilingual instruction, and reflective professional communities demonstrate promise for inclusive transformation. The review concludes that integrating UDL within SoTL offers a rigorous and contextually responsive pathway to institutionalise inclusive pedagogical practices, particularly within the policy landscape shaped by the Language Policy Framework for Public Higher Education Institutions (2020). Recommendations emphasise curriculum redesign, flexible assessment, sustained professional development, and alignment of institutional policy with inclusive design principles. By situating UDL within SoTL, this study reframes inclusive mathematics education as both pedagogically robust and socially just, contributing to a more equitable higher education landscape.

Abstract This study investigates how university students engage with generative artificial intelligence (GenAI), specifically ChatGPT, when completing knowledge-based academic tasks across six courses and two institutions. By comparing performance and perceptions in engineering and non-engineering subjects, the study examines whether students can use GenAI effectively without prior training and to what extent such tools meaningfully support learning. The work also explores how these findings may inform future research on accessible and inclusive learning design. A multi-method design was employed with 254 undergraduate and graduate students assigned to experimental groups (allowed to use ChatGPT) or control groups (restricted to traditional, non-GenAI resources). Quantitative analyses included descriptive statistics, a general linear model, and non-parametric comparisons, complemented by a topic-based analysis of open-ended survey responses addressing students’ perceptions, usage patterns, and desired functionalities. Students in the experimental groups generally obtained higher scores, with significant improvements in several subjects (e.g., computer systems administration, informatics, childhood disorders). A weak but significant positive correlation emerged between iterative engagement with ChatGPT (edits) and academic performance. Qualitative analysis showed that students valued ChatGPT for fast information access, clarification of concepts, and organizational support, while also expressing concerns about inaccuracies, overreliance, and limitations of free versions. GenAI can enhance student performance when used actively and reflectively, although its effectiveness varies by disciplinary context. The findings highlight the need for explicit AI-literacy instruction to ensure critical and responsible use. While the study does not directly address disability or accessibility outcomes, the qualitative patterns suggest potential intersections with inclusive and multimodal learning design, pointing to promising avenues for future research.

Integrating religious values with 21st-century learning remains a significant challenge in Islamic boarding schools (pesantren), especially in English instruction, where low motivation and achievement persist. Addressing this gap, this study aims to examine the effectiveness of the SANTREE instructional model, an innovative learning design grounded in motivation theory, digital pedagogy, and collaborative learning, in enhancing students’ motivation and English learning outcomes. A quantitative approach with a one-group pretest–posttest design was employed. The participants were 128 tenth-grade students from a pesantren-integrated private senior high school in East Java, Indonesia. Data collection involved a Likert-type motivation questionnaire based on the ARCS model and a cognitive test on narrative texts. Instruments were validated through expert judgment and statistical testing. Data were analysed using descriptive statistics, paired sample t-tests, simple linear regression, and effect size calculations. The results indicated substantial improvements in students’ learning motivation and English achievement after the implementation of the SANTREE model. Furthermore, learning motivation emerged as a significant predictor of students’ English learning outcomes, underscoring its central role in supporting academic performance. These findings confirm the pedagogical value of SANTREE in fostering student-centred, contextualised, and effective language instruction. The study offers practical implications for curriculum innovation in pesantren and similar educational contexts.

ABSTRACT Background In recent decades, emerging technologies—particularly Artificial Intelligence (AI)—have had a profound impact on the structure and decision‐making processes within educational systems. AI‐based tools now play a central role in data analysis, performance prediction, and personalised learning design. Objectives This study aimed to design and validate the Metacognitive Diagnostic Disruption in AI Decision‐Making Scale (MDD‐AI Scale) for educational administrators, focusing on metacognitive disruptions—disturbances in self‐monitoring and self‐regulation processes caused by AI involvement in decision‐making. Methods Using an exploratory mixed‐methods design, the study combined a literature review and interviews with 20 Jordanian educational administrators to develop 52 items from an initial 92. Data from 670 administrators were analysed for validity and reliability, with exploratory graph analysis (EGA) providing a network‐based perspective. Results and Conclusions Exploratory Factor Analysis (EFA) revealed a six‐factor structure—Metacognitive Self‐Monitoring Disruption, Metacognitive Self‐Evaluation Disruption, Cognitive Flexibility Reduction, Cognitive Overreliance on AI, Conflict Detection Shutdown, and Disruption of Metacognitive Decision Planning—explaining 67.6% of the variance. Confirmatory Factor Analysis (CFA) showed good fit (RMSEA = 0.072, CFI = 0.918), with AVE values > 0.50 confirming convergent validity. Reliability was high across all indices ( α = 0.904–0.951; ω = 0.897–0.952; CR > 0.89; ICC = 0.752–0.870). Measurement invariance across gender indicated structural equivalence. Exploratory Graph Analysis (EGA) and Random Forest Modeling (RFM) further supported robustness and predictive utility. Overall, the 42‐item MDD‐AI Scale is a valid, reliable, and innovative tool for assessing metacognitive disruptions in algorithmic decision‐making within educational management.

Virtual reality technology has transformed animation education by providing immersive learning environments, yet the proliferation of VR-based teaching resources presents significant challenges in resource discovery and personalized learning design. This research proposes an intelligent recommendation system that integrates transfer learning and knowledge graph technologies to address cold-start and data sparsity challenges in VR animation education. The system comprises two core components: a hybrid recommendation engine combining transfer learning algorithms with knowledge graph reasoning to generate context-aware resource suggestions, and a creativity development path prediction model based on LSTM-attention mechanisms that analyzes learning behaviors and forecasts individualized development trajectories. A comprehensive knowledge graph for VR animation teaching was constructed to capture domain concepts, resource attributes, and pedagogical relationships. Experimental results demonstrate substantial improvements over baseline methods across multiple metrics, with the proposed system achieving optimal performance in precision, recall, F1 score, and NDCG. Deployment in authentic educational settings yielded measurable gains in student learning outcomes and creativity competency development, validating the system's practical effectiveness in enhancing personalized animation education through intelligent resource recommendation and proactive pedagogical interventions.

Abstract Social interaction is widely recognised as a critical component of effective learning. However, as online learning becomes increasingly embedded within higher education, digital environments continue to face persistent difficulties in fostering meaningful social learning experiences. With more institutions than ever adopting online delivery, it is imperative to systematically investigate these challenges to inform pedagogical strategies that can better support collaboration and engagement. This article presents a systematic literature review of 36 recent studies examining the challenges faced by learners in online social learning contexts. The findings of the review were reported in accordance with PRISMA guidelines and organised and interpreted through the Community of Inquiry framework. The analysis demonstrates that the reported challenges are not isolated to a single dimension of the framework but instead exert a combined influence on cognitive presence, social presence, and teaching presence, underscoring the interdependent nature of these constructs in online learning. To extend this interpretation, we employed the activity‐centred analysis and design framework to examine how learning designs can constrain social interaction and collaborative inquiry. Additionally, the review highlights opportunities for improving learning design practices and identifies the potential of artificial intelligence in addressing barriers to online social learning. Drawing connections between challenges, design considerations, and emerging technological interventions, this study contributes to universal discussions on equity, inclusion, and collaboration, offering insights that are globally relevant to the design of a socially connected digital landscape of higher education. Context and implications Rationale for this study: This study addresses a gap by examining barriers to social learning in online higher education and how they stem from learning design. Why the new findings matter: Findings show these challenges are systemic and interconnected. Integrating CoI and ACAD provides a deeper explanation of their causes and informs more effective design interventions. Implications for practitioners, policy makers, researchers: The findings highlight the need for more intentional learning design to support meaningful social interaction in online environments. For practitioners, this means structuring collaborative tasks, roles, and facilitation strategies that promote engagement and psychological safety. For institutions and policymakers, the study underscores the importance of investing in staff capability, appropriate technologies, and inclusive design practices. For researchers, it provides a foundation for exploring design‐based and AI‐supported interventions to strengthen social learning. Overall, the study emphasises that improving tools alone is insufficient; aligning tasks, technologies, and social structures is essential for fostering connected, engaging, and equitable online learning experiences.

The application of artificial intelligence in enzyme molecular evolution has emerged as a research hotspot. However, applying machine learning to enzyme molecular modification still presents many challenges. In particular, accelerating the integration of machine learning and rational design is one of the important development trends in the field of protein engineering. In this study, we experimentally validated key amino acid mutations (E164L, E164P, S199E, and S199Q) predicted by a lab-developed sparse convolutional neural network to enhance the thermostability of microbial transglutaminase. We further investigated the molecular basis of this enhanced stability using molecular dynamics simulations. Compared with the wild type MTG, the thermal stability of the four mutants was significantly improved, and S199E showed the most remarkable improvement. At 60 °C and 50 °C, the half-lives of S199E were 2.3 times and 5.8 times those of the wild type, respectively, and the enzyme activity was increased by 1.4-fold. Molecular dynamics simulations showed that the binding free energy of S199E was - 28.68 kcal/mol, slightly lower than that of the wild type (- 27.96 kcal/mol). The root mean square deviation and root mean square fluctuation of the S199E mutant were 0.25 nm and 0.0566 nm, respectively, showing no significant changes compared with the wild type. LigPlot analysis indicated that E199 formed one hydrogen bonds with A309 and three salt bridges with H201, which might enhance local stability. These findings indicate that the improved thermal stability of the S199E mutant arises from enhanced local structural stability, not from major changes in overall protein structure, and accounts for its slightly lower binding free energy compared to the wild type.

This study examines the importance of integrating Qur’anic values into the design of modern learning based on Student-Centered Learning (SCL) for the reinforcement of students' character development. SCL, which places students at the center of the learning process, provides students with the opportunity to be more active in determining their learning direction. Qur’anic values that teach honesty, patience, empathy, and compassion can serve as a moral foundation in shaping students' character and noble conduct. This article highlights how the application of these values in learning can strengthen character formation, enrich learning experiences, and support holistic personal development. With this approach, it is expected that students will not only develop academically but also in social and moral aspects. Despite challenges in its implementation, collaboration between teachers, students, parents, and the community will strengthen the success of this integration in sustainable character education.

This scoping review aims to identify and map the current available evidence regarding the use of immersive virtual reality technology in dementia care education for nursing students. With the growing population living with dementia, there is an urgent need to prepare nurses for quality dementia care through providing relevant dementia care education for nursing students. Virtual reality is an emerging education medium for nursing education and dementia care education for nursing students. However, little evidence focuses on mapping how immersive virtual reality technology has been used in dementia care education for nursing students. With the increasing development and use of this technology, it is crucial to explore current evidence regarding the content and learning outcomes, design and development, and implementation of immersive virtual reality technology in dementia care education for nursing students. Studies are eligible if they describe the use of immersive virtual reality technology in dementia education. The dementia care education must be designed for or targeted at nursing students or healthcare professional students (including nursing students) within academic settings, such as nursing schools, universities, and colleges. The proposed scoping review will be conducted following the Joanna Briggs Institute methodology for scoping reviews and the Population, Concept and Context (PCC) framework. The review will search CINAHL, MEDLINE, PsycINFO, Scopus, Web of Science and ProQuest Dissertations and Theses Global. Grey literature search will be conducted via Google Scholar. Extracted data on authors, country, study design, setting, and findings relevant to the review objective will be presented in a data extraction table, accompanied by a quantitative summary of studies' characteristics and narrative summary. The PRISMAS-ScR figure will detail the selection process. This study has been registered with the Open Science Forum (OSF).

Augmentative and Alternative Communication (AAC) is increasingly recognised as an essential tool for supporting people with communication access needs, including within music therapy. AAC includes a variety of methods that enhance or serve as spoken communication, functioning as either a supplementary (augmentative) tool or a primary (alternative) communication approach. Integrating AAC into music therapy practice fosters disability-affirming practice by reducing communication barriers and creating accessible, inclusive spaces for self-expression and autonomy. This position paper explores the theoretical and practical intersections of AAC and music therapy through the lens of the biopsychosocial model, the social model of disability and the principles of Universal Design for Learning, illustrating how music therapists can actively contribute to dismantling systemic barriers through disability-affirming practice. We advocate for embedding AAC into music therapy higher degree education, further professional development opportunities, increased interdisciplinary collaboration, and the adoption of disability-affirming practices in order to further the integration of AAC into music therapy praxis. Our perspective is informed by our positioning as music therapy academics and practitioners with sustained engagement in AAC across practice, education and collaborative contexts.

The progressive rollout of integrated STEM education and robotics in primary education in Spain make it necessary to reflect on issues affecting consistency of delivery and areas for improvement. This paper presents a systematic review of practices in Spain using the PRISMA 2020 protocol, analyses their theoretical and practical coherence in light of international scientific literature, and identifies what improvements may be needed. The results confirm the need to combine cooperative and collaborative learning with project-based learning and scientific inquiry, and to integrate the areas of mathematics and natural sciences through robot design, construction, and programming. In addition, more focus should be placed on the use of low-cost robotic resources, such as programming boards, and the development of educational inclusion through Universal Design for Learning.

This study explains undergraduate engineering students’ engagement in blended learning through the lens of Herzberg’s Two-Factor Theory and the mediating role of learning motivation. Survey data from 656 engineering students enrolled in blended courses at Hanoi University of Science and Technology were analyzed using PLS-SEM. Results indicated that motivator factors strongly influenced learning motivation, which in turn significantly predicted learning engagement, while hygiene factors had only a small direct effect on learning motivation but an indirect role through motivator factors. Mediation analysis confirmed a sequential path (hygiene factors → motivator factors → motivation → engagement), explaining 58% of motivation and 78% of engagement variance. The findings highlight that stable course conditions enable pedagogy-centered motivators to activate motivation and sustain engagement. Practical implications emphasize the need for designing blended courses that combine reliable technical environments with intrinsically motivating learning designs.

Teachers’ managerial competence plays a crucial role in ensuring effective and relevant learning, particularly in vocational education. Teachers are required not only to master subject matter but also to plan, manage, and evaluate learning through well-structured and contextual learning tools. This Community Service activity aimed to strengthen teachers’ managerial competence through training on the development of learning tools using a deep learning approach at SMK Muhammadiyah 2 Tangerang Selatan. The implementation method included conceptual material delivery, practical training, and mentoring in designing learning tools. The deep learning approach was applied to encourage learning designs that emphasize deep understanding, critical and reflective thinking, and alignment with workplace demands. The results showed an improvement in teachers’ understanding and skills in developing more systematic, innovative, and adaptive learning tools aligned with 21st-century competencies. This activity contributed positively to strengthening teachers’ roles as learning managers and supporting the improvement of learning quality in vocational schools.

ABSTRACT This study examines population-level associations between digital learning preferences, digital learning engagement, and career development outcomes among Korean adults, with attention to age-related differences in career transitions. A secondary data analysis design was employed using two nationally representative datasets to examine patterns linking digital learning participation and labour market outcomes across age cohorts. Data were drawn from the Korean Educational Development Institute’s Individual Survey on Lifelong Learning (2024; N = 30,829) and national employment statistics tracking career transitions over 24 months. The datasets were analysed in parallel. Multivariate models examined associations between digital learning modality, digital learning preferences, age cohort, and career transition outcomes, controlling for education, prior employment, and industry. Generational disparities emerged: 78.9% of junior adults (25–44) preferred technology-mediated learning, compared with 35.2% of senior adults (45–65+). Higher digital learning engagement was associated with greater likelihood of successful career transitions. Findings indicate that digital divides within lifelong education systems are associated with unequal career transition outcomes across age groups, highlighting the importance of age-adaptive digital learning design and digital literacy support, while acknowledging limits on causal inference.

This study investigates how students and teachers in Japanese language programs at Indonesian universities perceive cultural representations in widely used textbooks and how these perceptions inform the need for intercultural-oriented learning design. Drawing on a mixed-methods approach, data were collected through a questionnaire completed by 68 undergraduate students and semi-structured interviews with six experienced instructors. Quantitative data were analyzed descriptively, while qualitative responses were thematically coded based on Byram’s Intercultural Communicative Competence and Moran’s 4P cultural framework. Findings reveal that both students and teachers value the integration of linguistic and cultural learning but recognize an imbalance between grammatical focus and intercultural contextualization. Marugoto is perceived as culturally authentic and communicative, while Minna no Nihongo is praised for structural clarity but limited in fostering cultural reflection. Students expressed a preference for interactive, visual, and relatable cultural content, whereas teachers emphasized their mediating role in linking cultural concepts to learners’ experiences. The study concludes that an integrative textbook design, combining grammatical rigor with reflective intercultural engagement is essential for developing intercultural competence. The proposed 4P + IC framework highlights how cultural representation can serve not only as learning material but as a bridge to intercultural understanding in Japanese language education.

Data generation with optimal experimental design for operator learning