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Distance higher education places increased demands on instructors’ emotional and communicative competencies, as teaching and interaction occur in technologically mediated environments. This study examines the role of teachers’ emotional intelligence, empathy, and communication-related competencies in distance higher education, with particular emphasis on emotional management and instructor well-being. A quantitative research design was employed, using self-report instruments administered to higher education instructors engaged in distance teaching. Non-parametric statistical analyses revealed strong internal coherence among emotional intelligence dimensions and a pattern of functional empathy characterized by high perspective taking and low personal distress. Self-perceived communication was found to be consistent across interactional contexts, indicating a stable communicative disposition. Most notably, emotional management emerged as a key factor associated with positive work-related emotions among instructors. The findings highlight emotional management as a critical mechanism supporting both teaching effectiveness and emotional sustainability in online learning environments. The study contributes to the literature by integrating emotional intelligence, empathy, and self-perceived communication within a unified empirical framework and offers practical implications for professional development and institutional support in distance higher education. Full article

The evolving digital landscape, including artificial intelligence (AI) and its generative forms, is changing how younger generations learn. As students utilize generative AI systems, they cultivate trust in such technology to support their current and long-term learning. The objective of this study was to investigate the relationship between generative AI use among students in health professions and their trust in this technology to support their lifelong learning as future health professionals. This study employed a survey methodology using a cross-sectional study design. The survey included sociodemographic variables and questions regarding students’ generative AI use and their trust in this technology to support their lifelong learning. Descriptive and inferential statistical procedures were used to analyze the data. A total of 558 students representing various health professions responded to the survey. In the regression analysis, after controlling for student’s sex and location variables, greater generative AI use was associated with students’ increased trust in this technology to support their lifelong learning (beta = 0.58, p < 0.001), explaining close to 40% of the total variance. Given the rapidly evolving digital landscape, this finding warrants further study, with implications for training of the future health workforce. Full article

The education systems, financial circumstances, and societal structures of our century expect educators to possess the most important characteristic: the ability to guide students who are highly digitally competent and keep themselves up to date. The “Sustainable Development Goals (SDG 4)” emphasized by the According to the United Nations highlight the necessity of continuously updating teacher competencies for quality and inclusive education. Establishing music teachers’ “digital competencies” on a sustainable basis depends on combining technical skills with a pedagogical vision. Therefore, thoroughly examining music teachers’ digital competencies in light of international standards and the TPAC model is critical to ensuring the sustainability of digital transformation at both the institutional and individual levels. This study, which examines digital literacy as an important part of sustainable education in music education, has examined the digital skills of music teachers in Turkey within the scope of international digital literacy frameworks and the TPAC approach. Digital skills have been related to the status of teachers’ professional practices, teaching-learning processes, assessment approaches, and the support of students’ digital literacy. The research concluded that music teachers’ digital competency levels are at the “explorer” level, meaning they are individuals who are aware of digital technologies and conduct research to develop themselves in this area. Full article

In Vietnamese primary mathematics education, geometry instruction often emphasizes rote calculation and formula memorization rather than meaningful contextualization, leaving students disconnected from abstract concepts and lacking opportunities to connect learning with cultural identity. This quasi-experimental study investigates how integrating generative AI tools (ChatGPT, DALL·E, Canva) with the culturally grounded Vietnamese folktale Bánh Chưng—Bánh Giầy can support Grade 5 students’ understanding of circle geometry. Employing a mixed-methods design with 30 students divided into experimental (AI + storytelling) and control (traditional instruction) groups, the study measured cognitive and affective learning outcomes through pre/post-tests, a validated 25-item questionnaire, interviews, and classroom observations. Quantitative results revealed significant improvements in the experimental group across all measured dimensions, learning interest, attentional focus, conceptual understanding, mathematics passion, and cultural preservation awareness, with large effect sizes. Qualitative findings confirmed enhanced engagement, multimodal conceptual clarity, and cultural affective resonance. The study demonstrates that low-cost, teacher-mediated generative AI can effectively support learning in resource-constrained primary settings when anchored in local narratives. Implications for ethical AI integration and teacher professional development in Vietnamese contexts are discussed. Full article

Mathematical knowledge is essential for adult learners’ advancement in academic and professional settings; however, instructional strategies for adult learners in higher education often emphasize memorizing procedures while neglecting their personal and professional experiences. Such approaches limit opportunities to leverage these experiences for developing meaningful mathematical understanding. Grounded in the Models and Modeling Perspective, this exploratory qualitative case study examines how a Model Development Sequence (MDS) supports the development of mathematical knowledge of adult learners returning to higher education. The participants were a group of seven first-year business adult learners enrolled in the Applied Mathematics in Business course at a higher education institution. Data were analyzed using protocol coding to describe the types of mathematical models the participants constructed. Findings indicate that participants progressed from creating models requiring redirection, grounded in proportional reasoning, to developing more sophisticated models based on linear and exponential functions. The MDS supported learners in refining, extending, and adapting their models, strengthening their conceptual understanding of variation, linear and exponential functions, and covariational reasoning. Moreover, the participants’ personal and professional experiences were central to model development. This study contributes to research on adult mathematics education by demonstrating the potential of MDS to support meaningful mathematical learning. Full article

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, have been developed to assist this process. However, these tools are primarily manual or semi-automated and are unable to provide rapid and high-precision recognition. To address these limitations, this study proposes a new AI-based method for the rapid, accurate, and fully automated detection and diagnosis of breast lesions. The proposed approach complements existing AI-based annotation and diagnostic methods by enabling automated detection and classification of breast lesion samples. The proposed system employs a deep learning–based classification framework to construct a professional-level AI diagnostic model. The system automatically generates diagnostic outputs based on the annotation criteria used by professional physicians, including positive/negative classification and accuracy metrics. Compared with conventional manual diagnostic methods, the proposed approach provides faster and more reliable diagnostic estimates for new patients. These results demonstrate the potential of the proposed AI-based method to advance automated breast lesion screening and diagnosis and to contribute to future research and clinical applications in this field. Full article

Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners. Full article

In the late 1990s, there was much speculation on what music and music education would look like at the beginning of the 21st century. Few predicted the level of change that we have witnessed since then. In fact, developments in technologies, demographics, societies and global relations that have taken place in the world over the past 100 years would have been neigh unimaginable decade by decade, and keep coming with ever-increasing intensity. Travel, trade and technology have connected people and cultures in myriad and often wonderful ways. But inequities, divisions, and conflicts also reached new heights, with the first half of the 2020s subject to a seemingly endless stream of natural and manmade disasters and conflicts. Inevitably, all of these developments impacted on the world of music in general, and also on music education. In this essay, I try to summarise some key experiences and observations of my own first fifty years of living musical diversity (a world that started to open before me when I began learning Indian sitar in Amsterdam in 1975), and efforts across five continents that I have been involved in or researched. Juxtaposing this with key literature on the topic this provides a broad basis for presenting ideas and views on progress towards giving musical practices from across the globe an appropriate place in music education at all levels: in community settings, schools, and institutions for professional training of performers and educators. In that process, I identify three critical junctures which can simultaneously present obstacles and opportunities for positive change: (1) terminologies, social inclusion, and the politics of diversity; (2) musical dynamics, technology, and institutional change; and (3) evolutions and revolutions in music learning and teaching. These inform a challenging but clear agenda for scholars, policy makers, institutional leaders, practising musicians and music educators worldwide who strive for more inclusive, diverse, equitable and relevant practices. Full article

Professional education increasingly requires graduates to make decisions in complex systems marked by multiple stakeholders, feedback, delays, uncertainty, and unintended consequences, yet systems thinking is still often taught as a set of disconnected tools rather than as an integrated professional practice. This conceptual paper adopts an integrative theory-building approach to develop a unified architecture for systems thinking in professional education, drawing purposively on systems traditions, practice-based learning, assessment scholarship, and emerging work on generative artificial intelligence (GenAI). The paper proposes four iterative practices (sensemaking and boundary setting, co-modelling and causal representation, intervention reasoning, and meta-learning) as the core architecture for learning systems thinking in professional contexts. It further translates this architecture into indicative implications for curriculum sequencing, authentic tasks, and assessment, while positioning GenAI as a cross-cutting support/risk layer that can assist iteration and critique but also introduce predictable risks such as fabricated causal links, overreliance, and false mastery. To address these risks, the paper outlines governance conditions based on traceability, uncertainty checks, stakeholder validation, and process-based assessment. Overall, the framework provides a design-oriented basis for teaching, assessing, and governing systems thinking in contemporary professional education and a foundation for future empirical testing. Full article

This study aimed to identify distinct lactate kinetic phenotypes in professional soccer players using unsupervised machine learning and determine their relationship with maximal running velocity (V_max) through explainable artificial intelligence methods. A total of 361 professional male soccer players from the First Division participated in the study. Incremental treadmill tests measured lactate concentrations at five standardized velocities, alongside VO_2max, V_max, lactate threshold (LT), and anaerobic threshold (AT) parameters. Three distinct lactate kinetic phenotypes emerged: Economical Aerobic (n = 216), Balanced Metabolic (n = 19), and High Producer (n = 126). The Economical Aerobic phenotype demonstrated superior performance metrics compared to High Producer (V_max: 15.85 ± 0.85 km/h; VO_2max: 56.20 ± 4.26 mL/kg/min; p < 0.001). Initial multicollinearity assessment revealed notable collinearity among all 10 candidate predictors (VIF > 10; maximum VIF = 10.75 for V_AT), necessitating rigorous feature selection. Ridge regression with 4 selected features (V_AT, VO_2max, 9.5 km/h lactate, 14 km/h lactate) achieved moderate but statistically significant predictive performance: 10-fold cross-validation R²= 0.392 ± 0.147 (permutation test p = 0.001). Standardized coefficients identified V_AT (β = 0.399) as the dominant predictor, followed by VO_2max (β = 0.253), 9.5 km/h lactate (β = 0.107), and 14 km/h lactate (β = −0.066). Lactate kinetic phenotyping reveals position-independent metabolic profiles with potentially meaningful performance associations in professional soccer. The Economical Aerobic phenotype demonstrates performance advantages associated with superior anaerobic threshold capacity. These exploratory findings suggest that individualized training strategies based on metabolic phenotype rather than playing position alone warrant further investigation, with potential applications for talent identification, training periodization, and return-to-play protocols pending prospective validation. Full article

Canine cognitive dysfunction (CCD) is a progressive neurodegenerative condition affecting aging dogs, characterized by impairments in learning, memory, spatial orientation, and behavior. Despite its substantial negative impact on dogs’ quality of life and owners’ emotional well-being, CCD is frequently underrecognized or diagnosed at a late stage. This study explored how challenges in CCD recognition and veterinary communication influence dog owners’ ability to identify symptoms and make informed decisions about care. Semi-structured interviews were conducted with 22 dog owners whose dogs were suspected of having CCD, based on elevated scores on the Canine Cognitive Dysfunction Rating Scale (CCDR) and owner-reported behavioral changes. Interview data were analyzed using reflexive thematic analysis. Four main themes emerged: (1) difficulties in recognizing CCD-related symptoms, (2) communication challenges between owners and veterinarians, (3) owners’ adaptation to gradually emerging symptoms, and (4) the emotional and practical burden of caregiving. Owners frequently interpreted behavioral changes as normal aging or other health problems, which delayed the recognition of cognitive decline. Participants also described limited guidance from veterinary professionals regarding CCD, contributing to uncertainty, emotional distress, and challenges in end-of-life decision-making. Together, these findings suggest that owners’ experiences follow a progressive caregiving trajectory, from initial symptom uncertainty to increasing emotional and practical burden. Improving awareness of CCD, strengthening veterinary communication, and providing targeted support for caregivers may facilitate earlier recognition and more effective management of cognitive decline, ultimately benefiting both dogs and the people who care for them. Full article

As classrooms become increasingly diverse, achieving equitable and inclusive education is central to the United Nations Sustainable Development Goals (SDGs), particularly SDG 4.7, and to advancing social sustainability in education. Teachers’ multicultural teaching efficacy is a key psychological resource shaping inclusive classroom practice. This study conceptualizes multicultural teaching efficacy as a multidimensional belief system and adopts a person-centered approach to identify latent efficacy profiles among Korean lower secondary school teachers. Using data from the OECD Teaching and Learning International Survey (TALIS) 2024, latent profile analysis was conducted based on seven efficacy indicators, with teachers’ social and emotional learning self-efficacy (TSEL-SE) and participation in multicultural education-related professional learning included as covariates. Five distinct efficacy profiles were identified, revealing heterogeneity in both level and configuration. TSEL-SE consistently predicted profile membership, whereas the effects of professional learning varied across profiles and were strongest among teachers with high TSEL-SE, indicating a conditional interaction effect between psychological and experiential resources. Notably, over one-third of teachers belonged to a structurally low efficacy profile, indicating systemic vulnerability. These findings highlight the importance of differentiated and emotionally responsive teacher education strategies for advancing inclusive practice and contributing to SDG 4.7 and broader social sustainability goals. Full article

Digital signal processing (DSP) methods and artificial intelligence (AI) serve as a unifying platform across diverse research areas and educational courses based on analysis of signals acquired by appropriate sensors and their time-synchronized systems. Autonomous sensor systems having their own batteries, memories, and possibilities of wireless communication form the core of modern technological systems. The interconnection of sensors for data acquisition, methods for advanced analysis of signal features, and collaborative evaluation promotes both theoretical learning and practical problem solving in professional practice. This paper emphasizes a common mathematical foundation for the processing of data acquired by different sensor systems, and it presents the integration of DSP and AI, enabling the use of similar theoretical methods in different applications, including robotics, digital twins, neurology, augmented reality, and energy optimization. Through selected case studies, it shows how a combination of sensor technology for data acquisition and the use of similar computational methods, visualization, and real-world case studies strengthens interdisciplinary collaboration. Findings of this paper demonstrate how integrating AI with DSP supports innovative research and teaching strategies, redefines the field’s educational role in the digital era, and points to the development of new digital technologies. Full article

While generative AI becomes increasingly available in higher education, faculties find it challenging to design, implement, and evaluate AI-enabled personalized learning systems within accreditation-constrained professional curricula. This method paper describes ADAPT (Assessment-Driven AI for Personalized Tutoring), a home-grown AI tutoring and remediation ecosystem implemented in a required PharmD immunology course. Using standard learning management (Canvas) and assessment (ExamSoft) platforms, a 20-item quiz mapped to six immunology mastery domains (N = 34; mean 69.1%, SD 17.9; Cronbach’s α = 0.73) was used to trigger tiered, structured generative AI remediation at both individual student and cohort levels. Instructional impact was evaluated using reliability indices, item-level difficulty analyses, and paired pre/post-assessment comparisons. Following AI-guided remediation, mean performance increased to 79.8% (+10.7 percentage points), variability decreased (SD 14.4), and assessment reliability improved (ExamSoft KR-20 0.87) compared with the diagnostic exam, the first midterm exam, and the final exam, respectively. Item difficulty stabilized (mean ≈ 0.80), with sustained retention of targeted concepts on the final examination. ADAPT provides a replicable, low-cost methodological blueprint for faculties to independently construct assessment-driven AI tutoring systems and lays the foundational steps for future AI-based predictive analysis workflow for at-risk students. Full article

The apprenticeship of observation is a form of anticipatory socialization that is experienced by all individuals who attend K-12 schooling, and is particularly consequential for the subset of this population that eventually becomes professional educators. Based on extensive interviews with professional teachers, sociologist Dan C. Lortie found that the 13,000 h of experience teachers had spent watching their own K-12 teachers constituted a sort of apprenticeship in teaching. This prolonged period of observation is thought to have a profound impact on the work of teachers. By observing their own teachers across thousands of hours, professional educators are said to make decisions in the classroom and in their teaching based on their own individual personalities and preferences instead of pedagogical frameworks or theories; the teacher learning brought about by the apprenticeship of observation leads professional educators to identify teaching they liked and disliked. Teaching decisions made by these educators in the classroom are ultimately based on a binary choice between replicating or rejecting the teaching they previously witnessed as K-12 students. Over time, the apprenticeship of observation has, for some researchers and teacher educators, served as shorthand for describing the replication of traditional teaching approaches across time, in effect suggesting that teachers teach the way they were taught. The power and negative consequences of the apprenticeship of observation have led teacher educators to devise multiple interventions within teacher education programs and pedagogies, which have sought to challenge and overcome the apprenticeship of observation and its negative influence on professional educators’ teacher learning and practice. Full article