Climate change, biodiversity loss, resource depletion, and increasingly volatile environmental conditions require governance systems that can move beyond retrospective monitoring toward anticipatory and adaptive action. Artificial intelligence (AI) offers relevant capabilities, but current environmental applications remain fragmented across sensing, prediction, optimization, and decision support. This study develops the AI Enabled SocioEcological Resilience Framework (AI SERF) through a systematic literature review and qualitative conceptual synthesis of peer reviewed studies published between January 2022 and June 2026. The reported review process screened 412 records and retained 73 studies for thematic synthesis. The revised framework links four functional pillars, namely Autonomous Eco Monitoring, Predictive Resource Optimization, Adaptive Algorithmic Governance, and Eco Resilient Feedback Loops, to absorptive, adaptive, and transformative resilience capacities. Its novelty lies not in proposing another isolated AI architecture, but in connecting data acquisition, predictive intelligence, human supervised governance, ecological intervention, and learning within a single resilienceoriented cycle. The framework is operationalized through candidate data sources, AI models, governance actors, performance indicators, and responsible AI safeguards. Particular attention is given to explainability, energy and carbon efficiency, algorithmic bias, cyberphysical security, institutional capacity, and data limitations in tropical and archipelagic settings. The study provides a theoretically grounded and implementation oriented basis for future empirical validation of AI enabled environmental governance and clarifies its contribution to SDGs 9, 11, 13, 14, and 15
Ayu Rimanda, Prima Wira Nanda, Zakia Hary Nisa et al.· Advanced Robotics· 0 citations
The rapid advancement of digital technology has transformed educational practices, including early childhood education, through the integration of educational applications that support interactive and engaging learning experiences. However, empirical evidence regarding the contribution of these applications to learning effectiveness among young learners remains limited. Therefore, this study aims to examine the role of educational applications in enhancing learning effectiveness by investigating their relationship with learning engagement and outcomes. This study employed a quantitative approach using a survey method involving 188 early childhood education teachers who actively utilize educational applications in classroom learning. Data were collected through structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement and structural models. The findings indicate that the use of educational applications significantly enhances learning effectiveness by increasing student engagement, supporting interactive learning activities, and facilitating more efficient instructional delivery. Educational applications also positively influence children’s motivation and participation, which contribute to improved learning outcomes and classroom experiences. These findings suggest that technology-supported learning environments can effectively complement conventional teaching approaches in early childhood education. In conclusion, educational applications represent valuable instructional tools for improving learning effectiveness and supporting the quality of early childhood education. The findings provide practical implications for educators, school administrators, and policymakers in promoting effective educational technology integration to create engaging and sustainable learning environments for young children
Kursih Sulastriningsih, Zakia Hary Nisa, Eka Dawn Avery et al.· Jurnal MENTARI: Manajemen, P...· 0 citations
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