Abstract: This study examines how an educational intervention about domestic personal care and perfume products can foster patriotism (cinta tanah air) among Indonesian high school students. A qualitative field study was conducted with 12 female students from four public high schools in Surabaya. Researchers delivered interactive educational sessions and gathered data through observation and interviews. We report that all participants used personal care products daily, yet only a small fraction chose domestic brands. After the educational program, students showed increased awareness and pride in local products. The findings suggest that aligning everyday consumer choices with national values can internalize patriotic sentiments among youth.

Abstract: This study examined the effectiveness of technology-integrated instruction in enhancing EFL students’ critical reading skills within an Indonesian university context. Grounded in concerns about students’ limited critical literacy and the growing emphasis on digital learning in higher education, the research aimed to identify how technology-supported activities could improve students’ ability to analyze arguments, evaluate evidence, and construct informed interpretations of academic texts. Using a qualitative design, the study involved university students engaged in a technology-enhanced reading module that incorporated digital annotation tools, multimedia explanations, and guided online discussions. Data were collected through classroom observations, student reflections, and semi-structured interviews, and analyzed thematically to capture recurring patterns of learning behavior and student perceptions. The findings indicated that technology-integrated instruction provided meaningful scaffolding that fostered deeper engagement with texts and promoted higher-order thinking. Students reported increased motivation and clarity in understanding complex materials when supported by interactive features. However, the results also revealed that some learners required additional time and pedagogical guidance to fully utilize digital tools. Overall, the study contributes to the growing body of research on technology-enhanced literacy by demonstrating the potential of digital platforms to strengthen critical reading skills in EFL settings. The implications suggest the need for intentional instructional design and ongoing digital literacy support to maximize the benefits of technology integration in reading instruction.

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This study develops and validates a psychometric instrument to measure entrepreneurial intention (EI) among secondary school students in Chile. Grounded in the Theory of Planned Behavior, the instrument integrates attitudinal and contextual factors adapted to the school context. Data from 1,402 students were subjected to confirmatory factor analysis, reliability estimation (Cronbach's alpha, composite reliability), and validity procedures (convergent and discriminant validity, variance inflation factor). Results support the instrument's factorial structure and internal consistency, enabling robust assessment of entrepreneurial intention and related educational interventions. The instrument demonstrates solid psychometric properties across most constructs, identifies items for future refinement, and provides practical guidelines for its application in school-based entrepreneurial programs and structural equation modeling. This work contributes a validated tool for both research and evidence-based practice in entrepreneurship education, with direct implications for evaluating and improving educational initiatives targeting entrepreneurial competencies in adolescents.

Abstract: The aim of this study is to evaluate science teacher candidates' knowledge and views on biotechnology education. The research was conducted with the phenomenology pattern, one of the qualitative research designs. In the study, quantitative data were collected using the "biotechnology knowledge scale" data collection tool, while qualitative data were collected using the "semi-structured interview form". The sample of the study was science teaching students studying in the fall semester of the 2024-2025 academic year. While the "biotechnology knowledge scale" was ap-plied to a total of 283 students, the “semi-structured interview form" was applied to 36 students. As a result of the research, most of the participants answered yes to the question asked about getting biotechnology education. To the question asked about whether science teacher candidates find biotechnology useful, most of the participants answered that they find it useful. Among the answers to the question asked about the benefits of biotechnology, benefits in the field of health, benefits in the field of agriculture and animal husbandry, quality of life and I do not find useful answers to the question, most of the participants answered in the field of health. Among the answers given to the questions about the harms of biotechnology, ethical issues, biological weapons, ecosystem degradation, threatening health and most of the participants answered the question as ethical issues. Among the answers given to the question asked to evaluate the views of science teacher candidates on the importance of educating teachers-biologists in biotechnology education, professional ethics and responsibility, increasing quality, and training qualified teachers, most of the participants answered the question as professional ethics and responsibility.

Abstract: Industry 5.0 challenges higher education to integrate human-centred and sustainable uses of artificial intelligence, yet current deployments rarely connect generative AI, neuroadaptive sensing and governance in a single framework. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We present an exploratory two-phase mixed-methods study as an early empirical illustration of this proposal. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of self-regulated learning, emotional experience and ethical concerns, and semi-structured interviews. In the lab phase, P300 dynamics aligned with key instructional events, providing preliminary evidence that the neuro-adaptive pipeline is sensitive enough to justify larger-scale studies. In the field phase, 87% of students reported higher engagement and 73% perceived improved learning outcomes, while qualitative data highlighted greater clarity, adaptive support and cognitive safety, alongside concerns about privacy and data sovereignty. Perceived Nested Learning and neuro-adaptive adjustments were moderately associated with enhanced self-regulatory strategies (correlations up to r=0.57, p<0.001). We argue that, under robust ethical, data-protection and sustainability frameworks, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education.

Abstract: Under ongoing climate change, environmental conditions in complex global arid and semi-arid ecosystems are rapidly deteriorating. According to NASA observations, the average annual air temperature in the northeastern regions of Kazakhstan and the Republic of Uzbekistan has increased by +1.03°C over the past 40 years (1984–2024). Forecasts derived from a linear regression model indicate that if the current warming trend continues, by 2070 the average annual temperature is expected to rise by an additional +1.47°C, reaching approximately 7.00°C. This projected warming suggests further intensification of environmental challenges in arid regions, including groundwater depletion, soil salinization (degradation), and heightened risks to food security. Consequently, equipping younger generations with high-quality knowledge based on clear analytical algorithms, and integrating complex ecological issues with modern educational technologies, requires innovative and effective methodological approaches. This study responds to this need by introducing the Eco-Decision Spiral Model (EDSM). Empirical findings show that students’ acquisition and practical application of relevant knowledge through the EDSM reached an average of 87.04%, while the comparative WSWNW model demonstrated a more limited effectiveness of 75.48%. The model’s integration with Benjamin Bloom’s classic cognitive taxonomy, STEM and inquiry-based learning principles, Herbert Simon’s bounded rationality and Scientific Decomposition approach, Howard T. Odum’s systems ecology concept, and several other foundational educational frameworks plays a significant role in strengthening learners’ ability to understand, critically analyze, and independently make decisions regarding complex ecological systems. Moreover, the model is highly aligned with international standards such as UNESCO ESD, OECD Education 2030/2040, and the NGSS. This compatibility not only supports the applicability of EDSM in global environmental education and scientific research, but also demonstrates its methodological value in advancing the goals defined within these international initiatives.

Abstract: This study examines the effectiveness of an eight-week AI training program aimed at en-hancing teacher candidates' pedagogical competence and AI literacy in rapidly changing and evolving educational environments. Due to the rapid change and development of our age, the change and transformation of education, which is one of the most important ele-ments of our lives, cannot be ignored. Accordingly, the integration of teacher candidates, one of the stakeholders of education, into technological developments is very important for both the efficiency and sustainability of education. The "parallel–simultaneous de-sign", one of the mixed research methods in which quantitative and qualitative research methods are used together, was employed. Based on this purpose, the study started with a needs analysis conducted with 33 teacher candidates studying in different branches at the faculty of education. Thanks to the needs analysis, knowledge gaps, digital skill levels and readiness for integration into artificial intelligence tools in future classrooms were deter-mined. Its application in teacher candidates instead of teachers in the profession was de-termined by needs analysis. It has been concluded that it will be more beneficial to apply the education of the future to the teachers of the future and that they will be able to adapt to these trainings more easily. Based on all these, a pre-test-posttest design was applied to observe the changes of the participants and an artificial intelligence literacy scale was used. QDA Miner Lite was used for the analysis of qualitative data, and SPSS 29.0 was used for the analysis of quantitative data. During the eight-week training, Gamma pro-grams were used for presentation, Suno for audio, Mindjourney for visual and Chatgpt for descriptive search in order to provide better quality education to the participants. While practicing with these applications, a more up-to-date education is aimed with activities that reveal problem-solving skills that include critical thinking exercises. According to the results obtained, it was revealed that the teacher candidates who thought that they were undecided or had insufficient knowledge reached a sufficient level in the post-test. In the light of these results, it reveals that artificial intelligence-oriented education is effective in developing sustainable pedagogical skills, digital literacy, readiness and professional self-confidence and offers evidence-based recommendations for the design of future teacher training programs.

Abstract: The study explores the influence of teacher-student relationships on student motivation and engagement in private universities in Cambodia. Drawing on qualitative data from semi-structured interviews with undergraduate students across different academic years, the research investigates students’ perceptions of relational dynamics with their teachers and examines how these interactions shape their academic motivation. Thematic analysis revealed five key themes: mutual respect and approachability, teacher support and encouragement, communication style and emotional tone, the balance between friendliness and formality, and the impact of cultural norms on classroom interactions. Findings indicate that positive, respectful, and supportive teacher-student relationships foster intrinsic motivation, enhance engagement, and promote a sense of belonging among students. Conversely, negative communication, favoritism, and excessive formality can undermine motivation and participation. The study highlights the importance of culturally sensitive relational pedagogy, suggesting that teachers who balance empathy with professionalism and adapt to local cultural expectations are most effective in motivating students. Implications for teacher training and institutional policy are discussed, emphasizing the need for professional development in relational and socio-emotional skills.

Abstract: Background: Artificial intelligence is reshaping higher education, yet most institutions still rely on ad-hoc experiments rather than a holistic, evidence-based strategy for curriculum innovation. Purpose: This study develops and proposes a comprehensive framework that helps universities integrate AI ethically and systematically into program and course design, ensuring alignment with learner needs, labour-market skills, and quality standards. Methods: Employing an integrative secondary research design, we conducted a structured review of peer-reviewed articles, policy documents, and institutional case studies published between 2018 and 2025. Forty high-quality sources passed rigorous screening for relevance, credibility, and methodological soundness. Extracted data were coded thematically and synthesised into recurring practices, enablers, challenges, and ethical considerations, which collectively informed framework construction. Results: AI adoption in curriculum design is global but uneven; leading institutions report gains in student retention, skills alignment, and design efficiency, while lagging peers cite insufficient faculty training, unclear policies, and ethical concerns. Synthesised findings yielded a three-layer framework: (1) program-level guidance that uses AI analytics for outcome formulation, skills mapping, and curriculum sequencing; (2) course-level guidance that positions AI as a co-designer for content generation, adaptive assessment, and personalised feedback; and (3) cross-cutting foundations covering governance, responsible AI use, quality assurance, capacity building, and sustainability. Conclusions: The proposed framework offers a scalable pathway for data-driven, learner-centred, and ethically responsible curriculum innovation. Its adoption can enhance institutional agility and graduate employability, though empirical validation across diverse contexts remains a priority for future research.

Abstract: The transition to Outcome-Based Education (OBE) in engineering demands instructional tools that bridge theoretical knowledge and practical engineering competencies. However, traditional Learning Management Systems (LMS) primarily function as static resource repositories, lacking the semantic structure necessary to support deep learning and precise competency tracking. To address this, this study developed a three-layer domain Knowledge Graph (KG) for Structural Geology and integrated it into the ChaoXing LMS (a widely used Learning Management System in Chinese higher education). A semester-long quasi-experimental study (N=84) was conducted to evaluate its impact on student performance and specific graduation attribute achievement compared to a conventional folder-based approach. Empirical results demonstrate that the KG-integrated group significantly outperformed the control group (p< 0.01, Cohen’s d=0.74). Notably, while performance on rote memorization tasks was similar, the experimental group showed marked improvement in identifying and solving complex engineering problems. LMS log analysis confirmed a strong positive correlation (r=0.68) between graph navigation depth and academic success. KG effectively bridged the gap between theoretical knowledge and practical engineering applications (e.g., geohazard analysis). This research confirms that explicit semantic visualization acts as vital cognitive scaffolding, effectively enhancing high-order thinking and ensuring the rigorous alignment of instruction with engineering accreditation standards. Ultimately, this approach promotes sustainable learning capabilities and prepares future engineers to address complex, interdisciplinary challenges in sustainable development.

Abstract: This study investigates the perceptions of Greek sixth-grade students regarding Artifi-cial Intelligence (AI) to inform the design of age-appropriate AI literacy education. Understanding students’ pre-instructional conceptions is essential for developing tar-geted interventions that build on existing knowledge rather than assuming conceptual deficits. A qualitative descriptive design was employed with 229 students from seven elementary schools in Athens, Greece. Data were collected through open-ended ques-tions and word association tasks, then analyzed using Walan’s [1] four-dimensional framework—cognitive, affective, behavioral, and ethical—via a systematic two-phase process supported by Microsoft Excel software. Findings revealed that students hold rich and multifaceted conceptions of AI. Cognitively, they described AI as robots, computational systems, software tools, and autonomous learning programs, frequently referencing ChatGPT and Siri. Affectively, they expressed ambivalence, balancing ap-preciation of AI’s usefulness with concerns over potential risks. Behaviorally, they identified interactive question–answer functions, creative applications, and everyday assistance roles. Ethically, students spontaneously raised issues of responsible use, so-cietal implications, and human–AI relationships. By applying Walan’s framework in the Greek context, this study extends international research, highlighting context-specific themes such as brand awareness and early ethical reasoning. The results challenge as-sumptions of technological naïveté among primary students and offer empirical in-sights for designing culturally responsive, ethically informed AI literacy curricula.

Abstract: As corporations increasingly integrate artificial intelligence (AI) into everyday life, particularly with the emergence of GenAI tools such as ChatGPT, the inability to integrate these tools into school curricula leaves students unprepared for the future job market and societal demands. Moreover, without a foundation in writing prompts and evaluating models' responses, students may not recognize ethical concerns, such as deepfakes, which may contribute to misinformation and irresponsible use of technology in personal and professional settings. This article introduces AMERH, a structured framework for writing prompts. The framework includes five principles: Ask, Museful, Evaluate, React, and Chain/Refine, to optimize interactions with GenAI models, such as ChatGPT. While various frameworks have been introduced to help learners craft prompts, addressing the role of these learners in evaluating their prompts and model responses remains underexplored in these frameworks. Crafting prompt writing within the AMERH framework aligns with Vygotsky's Zone of Proximal Development (ZPD) by providing learners with scaffolded support as they develop their skills. This framework provides learners with step-by-step guidance and serves as a support tool for diverse learners regardless of prior experiences. This framework enables learners to think critically and creatively, preparing them to transition from guided to independent prompt designers.

Abstract: Metaverses integrate technologies that push the boundaries of human experience. Their potential to transform areas such as education, mental health, and social-emotional support has sparked growing academic interest. However, despite their expansion, one of the main challenges for their implementation lies in the proliferation of metaverse platforms with diverse characteristics, architectures, and purposes, which complicates the task of informed technology selection. Given this diversity, a systematic approach is required to compare platforms based on functional and non-functional attributes relevant to specific application contexts. The objective of this study was to propose a model for evaluating the quality of metaverse-type platforms based on a hybridization of the aspects defined in the ISO/IEC 25000 family of standards, a maturity model extracted from recent literature, and the Metagon metaverse characterization typology. Using this model, 23 metaverse platforms were evaluated, with statistical analysis including PCA and k-means, achieving a kappa coefficient of 0.7643 between evaluators. The results show that platforms such as Decentraland, Overte, and Roblox achieve the highest levels of maturity (NM5), while JanusXR and Sansar remain in experimental categories. The results provide a taxonomy of characteristics refined and validated by experts that were used in the evaluation of a set of platforms, offering a rigorous and reproducible classification useful for guiding technology adoption decisions in emerging contexts. The discussion presents the basis for future studies focused on the evaluation of specific categories, such as educational, therapeutic, or social interaction platforms.

Abstract: Globalization is an inescapable process of the twenty-first century. In this process, political and economic borders fade, communication expands, and cultural interactions increase. The phenomenon of globalization transforms societies in three dimensions – economic, political, and cultural. In its cultural dimension, globalization consists of the formation and expansion of a particular culture on the global stage, creating a wave of cultural homogeneity that affects national identity – the tool that separates one nation from another. Meanwhile, the country’s education system, which on the one hand is tasked with safeguarding national values and on the other hand faces the challenges of globalization, plays a pivotal role in consolidating national identity while at the same time seizing the opportunities of globalization through a better understanding of this process. This research examines the role of the education system in confronting the impacts of globalization on national identity and answers the question of whether the education system has played an effective role in consolidating the national identity of youth so that it can have a constructive interaction with globalization. The findings from the reviewed studies showed that, despite the ideals and goals of this system, practically, in the field of educating citizens who, in addition to possessing characteristics appropriate to an Iranian citizen, can also act as global citizens while preserving national identity, it has not been successful. As a result, an effective education system in Iran, if based on Islamic-Iranian sources with various approaches including cultural-educational approaches, will be able to educate global citizens who, while preserving national identity, can also have a constructive interaction with the changing world.

Abstract: Large language models (LLMs) show promise for grading open-ended assessments but still exhibit inconsistent accuracy, systematic biases, and limited reliability across assignments. To address these concerns, we introduce SURE (Selective Uncertainty-based Re-Evaluation), a human-in-the-loop pipeline that combines repeated LLM prompting, uncertainty-based flagging, and selective human regrading. Three LLMs – gpt-4.1-nano, gpt-5-nano, and the open-source gpt-oss-20b – graded answers of 46 students to 130 programming questions across five assignments. Each student answer was scored 20 times to derive majority-voted predictions and self-consistency-based certainty estimates. We simulated human regrading by flagging low-certainty cases and replacing them with scores from four human graders. We used the first assignment as a training set for tuning certainty thresholds and to explore LLM output diversification via sampling parameters, rubric shuffling, varied personas, multilingual prompts, and post-hoc ensembles. We then evaluated effectiveness and efficiency of SURE on the other four assignments using a fixed certainty threshold. Across assignments, fully automated grading with a single-prompt resulted in substantial underscoring, and majority-voting based on 20 prompts improved but did not eliminate this bias. Low certainty (i.e., high output diversity) was diagnostic of incorrect LLM scores, enabling targeted human regrading that improved grading accuracy while reducing manual grading time by 40–90%. Aggregating responses from all three LLMs in an ensemble improved certainty based flagging and most consistently approached human-level accuracy, with 70–90% of the grades students would receive falling inside human grader ranges. These findings demonstrate that self-consistency-based uncertainty estimation and selective human oversight can substantially improve the reliability and efficiency of AI-assisted grading.

Abstract: A small amount of research related to the assessment of the limitations of the motor functions of Russian football players in the sports reserve has shaped the desire of the authors to provide detailed information about their work experience, observations and measurements. The objective of this study was to conduct a retrospective analysis of the motor function limitations of 14-15-year-old football players using functional movement assessment. We studied football players from the North-Western region of the Russian Federation, representing the teams of the Department of Youth football development of football club Zenit, St. Petersburg. A functional assessment of movements in football players aged 14 and 15 years showed significant impairments in the motor functions of the musculoskeletal joints and the bone structure of the musculoskeletal system. The functional impairments in the motor functions of football players are characterized by a combination of symptoms, including limited mobility in the trunk, upper extremities, and thoracic spine, as well as motor asymmetry in the shoulders. In addition, there is a low level of functional mobility in the hip joint during rotation and leg elevation, as well as instability in the knee joint. Motor asymmetry in the pelvic position has also been identified.

Abstract: Mobile augmented reality games (MARGs) generate rich digital traces of how students engage with complex, place-based learning tasks. This study analyses gameplay logs from the Art Nouveau Path, a location-based MARG within the EduCITY Digital Teaching and Learning Ecosystem (DTLE), to develop a learning analytics workflow that uses detailed gameplay logs to inform sustainability-focused educational design. During the post-game segment of a repeated cross-sectional intervention, 439 students in 118 collaborative groups completed 36 quiz tasks at 8 Art Nouveau heritage Points of Interest (POI). Group-level logs (4,248 group-item responses) capturing correctness, AR-specific scores, session duration and pacing were transformed into interpretable indicators, combined with error mapping and cluster analysis, and triangulated with post-game open-ended reflections. Results show high overall feasibility (mean accuracy 85.33%) and a small subset of six conceptually demanding items with lower accuracy (mean 68.36%, range 58.47% to 72.88%) concentrated in specific path segments and media types. Cluster analysis yields three collaborative gameplay profiles, labelled ‘fast but fragile’, ‘slow but moderate’ and ‘thorough and successful’, which differ systematically in accuracy, pacing and engagement with AR-mediated tasks. The study proposes a replicable event-based workflow that links mobile AR gameplay logs to design decisions for heritage-based education for sustainability.

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This study highlights the distinction between parents’ general well-being and parental well-being. It reveals the interplay between daily parenting behaviors and individual well-being, as well as the impact of one partner’s (particularly fathers’) behaviors on the other partner’s well-being. These findings contribute to broadening the discourse on parenting by shifting the focus beyond child outcomes to include the role of parenting behaviors in promoting parents’ own well-being and family resilience. This study examined mothers’ and fathers’ daily parenting behaviors through the lens of the Parenting Pentagon Model, which identifies five constructs of beneficial parenting: Partnership, Leadership, Expressions of Love, Encouraging Independence, and Adherence to Rules. The study explored the associations between parenting behaviors and parents’ general and parental well-being. Participants included 170 Israeli parents (85 couples) with young children aged six months to nine years. They completed self-report measures assessing parenting behaviors, well-being, and sociodemographic factors (e.g., family size, education, employment). Analyses explored how sociodemographic factors and parenting behaviors explain parental and general well-being within and across genders. Parents reported frequent beneficial parenting behaviors, with Love being the most prevalent. Mothers reported significantly higher Love behaviors, while other constructs showed no gender differences. Parenting behaviors strongly predicted well-being: Mothers’ behaviors explained 48% (parental) and 44% (general) of their well-being, while fathers’ behaviors explained 35% and 23%, respectively. Fathers’ behaviors more strongly predicted mothers’ well-being (24% parental, 22% general) than mothers’ behaviors predicted fathers’ well-being (13% parental, 11% general). Socio-demographic factors (family size and employment) were associated with maternal well-being.

Abstract: This research explores how two transnational teacher educators became culturally relevant educators by shifting their perspectives from a deficit-based to an asset-based approach through the influence of Hallyu (the Korean Wave). Utilizing a collaborative autoethnographic inquiry, the study examines the personal and professional journeys of Korean teacher educators within American teacher preparation programs. Drawing on post-colonial theories and Yosso’s (2005) Community Cultural Wealth framework, it delves into their lived experiences of Hallyu, how these experiences shaped their self-perceptions, and how their cultural identities and Korean cultural assets were integrated into their teaching practices—ultimately contributing to greater diversity and inclusion in education.

Abstract: Achieving equitable vocational education in geographically marginalized regions remains a persistent challenge due to limited infrastructure, unstable connectivity, and constrained access to qualified instructors. This study examines the implementation of a Progressive Web Application (PWA)–based learning ecosystem to enhance learning accessibility, competency development, and employability outcomes in seven vocational schools located across Pulau Sumba, Indonesia—one of the country’s most underserved regions. A dedicated platform (https://www.pwa-smk.id/) was developed integrating an Academic Information System, E-learning, and Industrial Work Practice (Prakerin) management, optimized through offline-first caching, low-bandwidth microlearning, and cross-device accessibility. Using a mixed-methods explanatory sequential design, the study involved 214 students in the quantitative phase and 32 purposively selected teachers and administrators in the qualitative phase, supported by system-generated engagement logs. Ethical clearance was granted by Universitas Negeri Yogyakarta (No. B/2795/UN34.17/LT/2025). Findings indicate substantial improvements in learning access, vocational competencies, digital literacy, and engagement. Access to learning materials increased from 46.2% to 87.9%, while offline usage rose to 68.1%. Competency tests demonstrated significant gains across hard skills (+31.1%), soft skills (+31.6%), digital literacy (+46.7%), and certification readiness (+47.0%). Multidimensional SDG impacts were observed: improved learning outcomes (SDG 4.1), enhanced technical and digital skills (SDG 4.4), increased youth employability (SDG 8.6), reduced inequalities (SDG 10), and strengthened sustainable practices (SDG 12). Community-level spillover effects emerged, including greater parental engagement, increased MSME partnerships, and rising demand for digital literacy training. These results provide strong empirical evidence that PWA technology—when contextually adapted—can function as a scalable, cost-efficient, and inclusive digital solution for vocational education in remote regions. The study contributes to the literature by demonstrating that PWA-enabled learning not only bridges digital divides but also accelerates multi-SDG progress and catalyzes broader community transformation.