Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Akuntansi dan Bisnis AMPLITUDO: Journal of Science & Technology Innovation Indonesian Journal of Education and Social Studies MANDALIKA: Journal of Social Sciences
Tarashtwal, Omid
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
The Role of Artificial Intelligence in Achieving the UN Sustainable Development Goals (SDGs) in Low Income Nations Tarashtwal, Omid; Hakimi, Musawer; Naderi, Zuhoruddin
Jurnal Ilmiah Akuntansi & Bisnis Vol 10 No 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/jiab.v10i2.7184

Abstract

Artificial Intelligence has been increasingly regarded as a transformative tool to pursue the United Nations' Sustainable Development Goals, especially in low income nations plagued by infrastructural, financial, and human resource constraints that hinder sustainable development. This paper analyzes the role of AI for economic development, social inclusion, environmental sustainability, and governance by highlighting pathways, synergies, and enabling technologies. We carried out a systematic literature review based on peer reviewed journal articles published between 2020 and 2025. We searched in IEEE Xplore, Emerald Insight, MDPI, ScienceDirect, and SpringerLink databases. In total, 30 articles that were relevant to the topic, were of sufficiently high methodological quality, and were applicable to this study were included in the review. Data were extracted on the use of AI, targeted SDGs, geographic location, and key findings. Bibliometric analyses and various approaches to thematic synthesis were used to better understand research trends, keyword cooccurrence, cross SDG synergies, and newly identified challenges. Results indicate that AI improves poverty reduction, financial inclusion, optimization of the workforce, and industrial innovation; improves education, gender equality, and social equity; climate monitoring, resource management, and urban sustainability; and governance and effective partnership with regards to transparency and informed decision making. Challenges pertain to infrastructure deficits, capacity gaps, and ethical considerations. Advice for policy development, capacity building, and responsible AI deployment underpin the need for context sensitive approaches. Artificial Intelligence arises as a key enabler of integrated, scalable, and sustainable development in low income countries.
Green Artificial intelligence Foundations, Applications, and Pathways to Sustainable Development Hakimi, Musawer; Tarashtwal, Omid; Ghafory, Hamayoon
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.524

Abstract

The fast evolution of artificial intelligence (AI) systems has worried people about their environmental impact thus prompting the rise of Green AI. In the present systematic review, we are going through the 32 articles published in peer-reviewed journals that were analyzed based on PRISMA standards regarding the conceptual bases, applications, and the future of Green AI. The review identified three paradigms: Green AI (computational efficiency), Sustainable AI (holistic socio-technical responsibility), and AI for Green (AI applied to sustainability challenges). A large part of the resources that would be used for the environments, monitoring, agriculture, and smart city applications can be saved by 15-30% through Green AI. The main difficulties are performance and efficiency balancing, limiting budget, and a research mentality that values precision more than sustainability. The research points out the dual function of AI in environmental matters as that of polluter and of a device for making the planet greener through humane practices and technologies. To sustainable AI, efficient algorithm design, regulatory support, the establishment of carbon-aware metrics, and collaboration among different disciplines to create the adoption of AI that is both economical and ethical are needed
From Space to Society: Integrating Remote Sensing and GIS to Monitor Educational Infrastructure and Social Transformation Tarashtwal, Omid; Sirat, Abdul Wali; Nadry, Zabihullah
Indonesian Journal of Education and Social Studies Vol 5, No 1 (2026)
Publisher : Nurul Jadid University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/ijess.v5i1.14221

Abstract

Monitoring educational infrastructure in conflict-affected countries such as Afghanistan remains critical for understanding social transformation and guiding evidence-based policy. Indeed, rather vital. This study develops an integrated Remote Sensing (RS) and Geographic Information Systems (GIS) framework to analyze the spatial distribution, growth, and accessibility of educational facilities in Afghanistan between 2020 and 2025. Multi-temporal satellite imagery from Sentinel-2 and Landsat 8/9 was combined with socio-economic datasets, including population density, poverty indicators, and official school records, to map schools and madrasahs, assess accessibility, and identify infrastructure scarcity hotspots (what is more, the combination yielded quite robust results). Accessibility analyses employing urban and rural buffer zones revealed significant disparities, with rural populations facing markedly limited physical access and correspondingly higher educational deprivation. Quite stark, in fact. Multi-criteria hotspot modelling further highlighted those regions where high population demand converges with poor facility quality and teacher shortages, thereby indicating critical service gaps. For that matter, these gaps persist rather stubbornly. Comparative analysis of infrastructure growth versus population expansion demonstrated, quite convincingly, that in many urban and rural areas new school construction has not fully matched demographic demand, thus revealing unmet educational needs. The study emphasises that spatially explicit, data-driven approaches are essential for equitable educational planning and for supporting social transformation in fragile contexts. The findings provide actionable insights for policymakers, international donors, and planners to prioritise interventions in underserved regions and promote inclusive educational development. Future research could usefully integrate real-time geospatial monitoring and participatory approaches to enhance educational planning and social development strategies further.
Integrating Remote Sensing and Geographic Information Systems (GIS) to Monitor Educational Infrastructure and Social Transformation in Afghanistan (2020–2025) Tarashtwal, Omid; Turan, Mohammad Nawab; Sirat, Abdul Wali
MANDALIKA : Journal of Social Science Vol. 4 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/mandalika.v4i1.650

Abstract

Monitoring educational infrastructure in conflict-affected countries such as Afghanistan remains critical for understanding social transformation and guiding evidence-based policy. Indeed, rather vital. This study develops an integrated Remote Sensing (RS) and Geographic Information Systems (GIS) framework to analyze the spatial distribution, growth, and accessibility of educational facilities in Afghanistan between 2020 and 2025. Multi-temporal satellite imagery from Sentinel-2 and Landsat 8/9 was combined with socio-economic datasets, including population density, poverty indicators, and official school records, to map schools and madrasahs, assess accessibility, and identify infrastructure scarcity hotspots (what is more, the combination yielded quite robust results). Accessibility analyses employing urban and rural buffer zones revealed significant disparities, with rural populations facing markedly limited physical access and correspondingly higher educational deprivation. Quite stark, in fact. Multi-criteria hotspot modelling further highlighted those regions where high population demand converges with poor facility quality and teacher shortages, thereby indicating critical service gaps. For that matter, these gaps persist rather stubbornly. Comparative analysis of infrastructure growth versus population expansion demonstrated, quite convincingly, that in many urban and rural areas new school construction has not fully matched demographic demand, thus revealing unmet educational needs. The study emphasises that spatially explicit, data-driven approaches are essential for equitable educational planning and for supporting social transformation in fragile contexts. The findings provide actionable insights for policymakers, international donors, and planners to prioritise interventions in underserved regions and promote inclusive educational development. Future research could usefully integrate real-time geospatial monitoring and participatory approaches to further enhance educational planning and social development strategies.
Artificial Intelligence in Climate Change Communication: Enhancing Public Awareness, Participation, and Policy Engagement Turan, Mohammad Nawab; Tarashtwal, Omid; Shahbazi, Hafizullah
MANDALIKA : Journal of Social Science Vol. 4 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/mandalika.v4i1.655

Abstract

Climate change remains one of the most pressing global challenges, yet public awareness, participation, and evidence-based policy engagement often lag due to the complexity of scientific information and ineffective communication strategies. This study explores the role of artificial intelligence (AI) in enhancing climate change communication, fostering citizen engagement, and supporting policy formulation. Using a systematic literature review (SLR) methodology, publications from 2010 to 2025 were collected from reputable databases, including ScienceDirect, SpringerLink, IEEE Xplore, MDPI, Wiley, Emerald, and Scopus. Boolean search operators and targeted keywords, such as “artificial intelligence,” “climate change communication,” “public engagement,” and “policy,” guided the selection of relevant studies. Results indicate that AI significantly improves public understanding by enabling data-driven visualization, natural language generation, and predictive analytics. It enhances citizen participation through AI-powered citizen science initiatives, collaborative data collection, and real-time monitoring of environmental indicators. Additionally, AI strengthens policy engagement by facilitating evidence-based governance, scenario modeling, and adaptive decision-making. Overall, AI functions as a transformative tool that bridges scientific knowledge, societal awareness, and policy implementation, promoting informed and sustainable climate action. The findings underscore the need for equitable access, ethical considerations, and capacity building to ensure that AI benefits are widely shared and contribute to resilient climate strategies.
Co-Authors Ghafory, Hamayoon Hakimi, Musawer Naderi, Zuhoruddin Nadry, Zabihullah Shahbazi, Hafizullah Sirat, Abdul Wali Turan, Mohammad Nawab