While the term global development suggests development efforts spanning the entire globe, in practice, such projects are predominantly associated with the Global South. As a result, development initiatives have been criticized for reinforcing paternalistic and colonial narratives of progress, deepening the divide between the "developed" West and the "less developed" rest. Similarly, HCI4D research is largely focused on the Global South, often overlooking the challenges faced by marginalized communities in high-income countries. In this paper, we examine an underexplored context for HCI4D: culturally and linguistically diverse (CALD) communities in Australia. Drawing on interviews with 10 non-profit practitioners experienced in working with CALD communities, we report on key challenges encountered in Development work. Our findings highlight the need to (i) secure genuinely informed consent, (ii) recognize the mediating role of local volunteers, and (iii) address cross-cultural communication gaps to support authentic community representation. While grounded in a specific setting, our insights offer broader implications for both HCI4D and the wider HCI community.
As technology-rich maker and DIY practices proliferate globally, we need to understand how to capture and articulate the complex and sometimes conflicting factors involved in enabling and supporting them in different contexts. In this article, we describe a study investigating the design tensions, priorities, and considerations of creating an accessible makerspace in Puerto Rico. We organized two workshops with local and non-local experts to discuss cultural, contextual, practical, and pedagogical factors involved in creating a site for technology-rich informal learning. Based on our findings, we propose a model for systematically articulating and considering design tensions involved in creating public-facing makerspaces that prioritize accessibility and community engagement. Additionally, we offer insights into the possibilities of future accessible making and DIY practices in the Caribbean, taking into account local cultural, historical, and infrastructural characteristics.
Adopting sustainable consumption practices in households is challenging due to the complexities of everyday life. Although eco-feedback technology supports people’s environmental actions in households through the provision of feedback about resource consumption, people still find it difficult to use these systems in relation to their everyday lives. We present the results of a field study in which participants from 15 households used a mobile application, Eco-Garden, for three weeks. Eco-Garden provides information about household resource consumption and encourages sustainable practices at home. Our findings show how Eco-Garden encouraged members of the households to reflect, shift consumption practices, and support planning for future usage. Family involvement played a major role in driving social accountability and fostering sustainable habits. Our results show the gendered nature of domestic work, where women often take more responsibility for household management and sustainable activities. We noted goal-setting, self-motivation and an individual sense of responsibility motivated through Eco-Garden encouraged sustainable practices in households. We discussed how self-reporting and goal-setting features can enhance self-motivation and a sense of responsibility, encouraging sustainable practices in households. We suggest that future eco-feedback systems need to help bridge the gender divide in household sustainable activities while balancing comfort and consumption reduction.
Artificial Intelligence (AI) has the potential to significantly improve how Non-Governmental Organizations (NGOs) utilize their limited resources for societal benefits, but evidence about how NGOs adopt AI remains scattered. In this study, we systematically investigate the types of AI adoption use cases in NGOs and identify common challenges and solutions, contextualized by organizational size and geographic context. We review primary literature on AI adoption in NGOs related to social impact between 2020 and August 2025 in English. Following the PRISMA protocol, two independent reviewers conduct study selection, resulting in a final literature body of 65 studies. Leveraging a thematic and narrative approach, we identify six AI use case categories in NGOs – Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization – and extract common challenges and solutions within the Technology–Organization–Environment (TOE) framework. By integrating our findings, this review provides a novel understanding of AI adoption in NGOs, linking specific use cases and challenges to organizational and environmental factors. Our results demonstrate that while AI is promising, adoption among NGOs remains uneven and biased toward larger organizations. Nevertheless, following a roadmap grounded in literature can help NGOs overcome initial barriers to AI adoption, which may ultimately improve effectiveness, engagement, and social impact.
India’s diverse agricultural landscapes demand a single Land Use and Land Cover (LULC) product integrating both intra-annually static (built-up, tree cover, barren land) and dynamic (water seasonality, cropping intensity) classes for sustainable Natural Resource Management (NRM). Existing LULC products suffer from limited thematic coverage of dynamic processes, imprecise delineation of fragmented smallholder features, limited reproducibility, and poor performance of monolithic classifiers on spectrally similar categories. We introduce a hierarchical decision-tree framework that breaks complex classification tasks into targeted sub-tasks, offering methodological improvements in detecting monsoon water, delineating tree-croplands, and classifying cropping intensity. Class-wise evaluations demonstrate superior performance: SAR water detection achieves an NRMSE of 0.33 (vs. baselines 0.53–0.75), tree-cropland macro-average of 0.94, and cropping intensity macro-average of 0.88 (vs. baseline 0.77). Crucially, this study ensures transparency and reproducibility in LULC mapping. We publicly release four curated datasets alongside the entire classification pipeline implemented on the Google Earth Engine commodity platform. The resulting pan-India output maps at 10m resolution are hosted on CoRE-Stack (digital public good) for easy accessibility and analysis [ 24 ], already powering real-world applications in water-security planning [ 61 ] and agricultural studies [ 50 ].
Environmental justice (EJ) is a grassroots-led praxis with a deep history of organizing in the United States and beyond. EJ addresses the unequal distribution of harms and benefits at the intersection of industrial activity and systems of oppression and uses a variety of engagement strategies, including interactive computing, to engage stakeholders in action and decision-making. Understanding these practices, especially as they pertain to prioritizing technology use and nonuse, can inform both future EJ efforts and the work of those in computing who aspire to use their tools and knowledge to advance environmental sustainability, social justice, and well-being. Drawing on interviews with EJ advocates in North America and observant participation in local EJ organizing, our analysis informs an anti-technosolutionist EJ perspective that investigates how practitioners navigate the tensions of using engagement strategies to further EJ priorities, practices, and visions. Four themes are introduced: assembling a constellation of communities and publics, representing social and environmental information, practicing nonuse of digital technology solutions, and aspiring toward new forms of action. Strategies and tactics associated with these themes are described, and implications for design and community engagement are discussed.
The advent of Large Language Models (LLMs) is reshaping education, particularly in programming, by enhancing problem-solving, enabling personalized feedback, and supporting adaptive learning. Existing AI tools for programming education struggle with key challenges, including the lack of Socratic guidance, direct code generation, limited context retention, minimal adaptive feedback, and the need for prompt engineering. To address these challenges, we introduce Sakshm AI, an intelligent tutoring system for learners across all education levels. It fosters Socratic learning through Disha, its inbuilt AI chatbot, which provides context-aware hints, structured feedback, and adaptive guidance while maintaining conversational memory and supporting language flexibility. This study examines 1170 registered participants, analyzing platform logs, engagement trends, and problem-solving behavior to assess Sakshm AI's impact. Additionally, a structured survey with 45 active users and 25 in-depth interviews was conducted, using thematic encoding to extract qualitative insights. Our findings reveal how AI-driven Socratic guidance influences problem-solving behaviors and engagement, offering key recommendations for optimizing AI-based coding platforms. This research combines quantitative and qualitative insights to inform AI-assisted education, providing a framework for scalable, intelligent tutoring systems that improve learning outcomes. Furthermore, Sakshm AI represents a significant step toward Sustainable Development Goal 4 Quality Education, providing an accessible and structured learning tool for undergraduate students, even without expert guidance. This is one of the first large-scale studies examining AI-assisted programming education across multiple institutions and demographics.
This is an erratum for the article “The Last Mile in Remote Sensing Poverty Prediction” published in ACM J. Comput. Sustain. Soc. 3, 3, Article 16 (June 2025), 54 pages.
This is a corrigendum for the article “The Last Mile in Remote Sensing Poverty Prediction” published in ACM J. Comput. Sustain. Soc. 3, 3, Article 16 (June 2025), 54 pages.
This article examines the relationship between problematic mobile phone use and factors including anxiety, depression, overuse, social identity, cyber-orientation, disturbance in daily life, materialism, need for touch, positive anticipation, tolerance, and withdrawal. This study also explores the moderating effect of gender. Participants include 751 mobile phone/smartphone users predominantly from Nigeria, Tanzania, Ghana, and South Africa. Data models revealed significant positive relationships between problematic mobile phone use and depression, disturbance in daily life, withdrawal, overuse, cyber-orientation, positive anticipation, and need for touch. Gender analysis reported significant positive relationships for men and women between problematic mobile phone use and cyber-orientation, depression, positive anticipation, and withdrawal. The men model found positive relationships with disturbance in daily life and need for touch. The women model revealed a significant association with overuse. Guided by the study findings, we provided design recommendations to facilitate the development of mobile technologies and behavior change interventions within the context of several African countries.
Understanding the role of property rights in managing Information and Communication Technology (ICT) devices, primarily computers, is fundamental to addressing resource waste and achieving digital inclusion and sustainability goals. Although the acquisition, use, and disposal of ICT devices are predominantly governed by private property, reuse ecosystems demonstrate significant benefits. In such ecosystems, diverse actors collaborate to recover discarded ICT devices, refurbish, maintain, and deliver them at minimal environmental and economic cost. Drawing on the Common-Pool Resources theory, this article proposes a model that employs property rights to govern ICT device reuse, using the bundle of rights as a structured language to organise reuse ecosystems. The model is based on the eReuse initiative, developed by researchers from the Technical University of Catalonia and the non-profit organisation Pangea, in Spain. It captures patterns of collective action, classifies actors by their roles, and maps the property rights underlying the interactions among these roles. The model was formalised to ease its application in Latin American contexts and evaluated in three regional reuse ecosystems to assess its suitability for replication. Results indicate that, although local adaptation is often needed, the eReuse model is considered suitable for informing the design of ad hoc models to manage the property rights of reused ICT devices in culturally aligned reuse ecosystems.
This article contributes to the social computing (particularly CSCW and HCI) scholarship by conceptualizing the idea of “hope”, particularly in relation to displacement, marginalization and resistance. Based on our longitudinal and multi-phase ethnographic interventions with internally displaced populations (IDPs) in Mohakhali and Kalyanpur areas of Dhaka, Bangladesh for three consecutive years, this work demonstrates how various dynamic properties of hope, both at personal and communal level, support this group to act, react, and/or resist many layers of urban adversities in their quotidian lives. By introducing the notion “Resilient Hope” , which constitutes identity formation, radical extensibility, and freedom of departure, the paper offers a novel understanding of how such marginalized communities sustain an uncertain, yet hopeful life. Drawing from a rich body of literature in Anthropology, STS, Philosophy, and Critical Urban Studies, it argues that resilient hope for a marginal community is not a static end goal to be achieved through design but a dynamic mode of survival and operation that has the potential to inform sustainable CSCW and HCI design processes. The paper further connects its empirical findings and theoretical insights to the broader goal of social justice in computing.
Nitrous oxide (N 2 O) is a powerful greenhouse gas (GHG) that has nearly 273 times more global warming potential than carbon dioxide over a 100-year period. By 2030, the Canadian government is requiring Canadian farmers to reduce their synthetic fertilizer-based GHG emissions by one third. Measuring N 2 O emissions is therefore important, but high frequency sampling requires expensive sensing equipment. Therefore, we propose replacing the expensive equipment with an affordable in-field Internet of Things (IoT) sensing device equipped with intelligence to make reasonably accurate N 2 O emission predictions by using only proximal sensor data. We gathered N 2 O emission, weather, and soil sensor data from a smart farm located in Ottawa, Ontario, Canada, during the 2021, 2022, and 2023 growing seasons. We built a soil sensing microprocessor-based prototype. We performed N 2 O emission prediction single-year interpolation (or gap-filling) and multi-year extrapolation experiments using data-driven models. Random forest and long short-term memory (LSTM) were the best performing models at interpolating, achieving 0.70–0.90 and 0.71–0.89 R 2 , respectively. When training models using 2021 data to predict 2022 emissions, reasonable accuracy (up to 0.62 R 2 ) was achieved by the multilayer perceptron model, which was one of the best performing models, alongside LSTM, in these experiments.
The United Nations’ Sustainable Development Goals (SDGs) are one of the most widely accepted frameworks worldwide to design policy interventions and implement them in an endeavour to create a sustainable future. A decade from 2015 has ensured a maturity of understanding in terms of localizing the globally agreed indicators to the grassroots level. However, achieving a given sustainability target is highly context-specific, and member states need to design custom structures and policies to achieve specific targets in their regions. This is called the problem of SDG localization . The dearth of relevant frameworks for representing and reasoning about sustainability and policy interventions has resulted in SDG localization efforts being pursued in silos and in an ad hoc manner. In this work, we address the problem of designing a representation framework for policy interventions towards achieving sustainability targets. We call this activity “ Intervention Science ” and develop three essential structures: Differential Impact Modeling that studies predicted change in SDG indicator, Collateral Impact Modeling to understand the side-effects of policy interventions, and Stability Profile Modeling to assess the sustainability of the interventions. We take up specific examples from SDG 2-Zero Hunger to showcase how policy instruments can be designed for specific SDG indicators.
Sustainable forest management (SFM) is essential for preserving biodiversity, maintaining ecosystem services, and mitigating climate change. This systematic review synthesizes global trends and innovations in SFM practices, analyzing peer-reviewed literature from 2015 to 2025 to identify effective strategies and emerging technologies. The review examines a diverse range of approaches, including forest health index, forest health sensing techniques, emphasizing remote sensing, ground-based monitoring, and the application of machine learning (ML) and artificial intelligence (AI). Moreover, the review highlights SFM practices, including ecosystem-based approaches, community and indigenous involvement, carbon sequestration strategies, and local and global policy frameworks. By integrating technological advancements with policy-driven initiatives, this study provides a comprehensive understanding of current trends and innovations in forest management, offering valuable insights for researchers, policymakers, and practitioners.
An estimated 1 million plant and animal species are threatened with extinction. While opportunities for conservation occur in human-dominated landscapes, negative perceptions of wildlife reshape these possibilities into conflicts. To imagine how technology can help, we held co-design workshops to identify features users wanted in a digital system for learning about and appreciating bats living in a suburban park building. The interactive system was designed to be used by park visitors, employees, and interested others. Participants co-designed features that promoted engaging and entertaining content, such as live streams and activity profiles of individual bats. Participants also expressed desire for accessible opportunities for interaction with a community of users and experts in order to counter misconceptions about bats and highlight their roles in our communities. However, when critiqued from a cohabitation perspective, these features prioritize the needs of human users and neglect those of bats. Elaborating cohabitation as a generative theory for the design of co-living systems and spaces, we outline design implications for shared habitats, co-adaptation, and mutual benefit. Digital systems can facilitate cohabitation between humans and wildlife, however, explicit concern for shared habitats, co-adaptation, and mutual benefit must be incorporated within co-design processes that involve human and animal participation.
Digital sufficiency, an emerging concept from the sustainable computing literature, can inform interface design to better manage and potentially reduce energy consumption in data centers, which is intensifying due to AI and data growth, despite energy efficiency efforts. Since 26% of data center energy consumption stems from cloud storage and servers, this research integrates digital sufficiency with existing HCI guidelines to enable cloud providers to respond to demands for sustainable infrastructure and facilitate user reflection. We conducted an online survey to understand users’ storage needs, awareness of climate impacts, and openness to sustainable storage. Our findings highlight the limited awareness among users of the carbon footprint associated with data centers and a strong demand for more sustainable storage options once they become aware. To empower users to reflect on climate impacts and align storage practices with their personal sustainability goals, we propose interface design recommendations that challenge the status quo.
Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This article highlights the potential conflict between electricity prices and carbon emissions and the resulting tradeoffs in carbon-aware and cost-aware load scheduling. To address this tradeoff, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.
The underrepresentation of women in computing in the workplace requires an understanding of the adequate elements needed for women to succeed in Bangladesh complementing the existing body of work. It uses a varied modality of communication Fictional inquiry (FI), where participants write letters to fictional characters followed by a Co-design of solutions. This research considers N=71 participants, 48 tertiary-level computer science and engineering students, and 23 stakeholders covering academics, computing professionals, and guardians of women in computing over a period of one and a half years. The study identifies three major elements for women in computing to succeed professionally: provision to improve skills and self-efficacy, ensuring ways to increase the sense of belonging, and providing transportation and safety aligning through the women’s empowerment framework of Naila Kabeer. The required elements for success and empowerment show that a collaborative approach of support is required from families, institutes, and authorities from a regional perspective that can be of interest to researchers from the HCI, CSCW, and ICTD fields. This work is further expected to provide a guideline for stakeholders, policymakers, institutions, and families eager to ensure inclusive learning and growing space for women in computing.
Extreme Edge Computing (EEC) promotes sustainable computing by reducing reliance on centralized data centres and decreasing their environmental impact. By using extreme edge devices to handle computing requests, the EEC reduces the energy demands for data transmission and execution, thereby reducing carbon footprints. However, EEC introduces challenges due to the mobile, heterogeneous, and resource-limited nature of these devices. Additionally, tasks are often complex and interdependent, complicating offloading and workload orchestration. The dynamicity of EEC systems, where both task generation and resources can be mobile, alongside task inter-dependencies, escalates the complexity of task offloading and workload management. To tackle these complexities, task partitioning emerges as a viable strategy. Moreover, in dynamic edge computing scenarios, resource demand remains unpredictable, emphasizing the critical need to optimize resource utilization efficiently. In this article, we investigate the problem of tasks with inter-dependencies offloading in an EEC environment where mobile and resource-constrained edge devices are employed as computing resources. In this regard, a partitioning-based Deep Reinforcement Learning (DRL) for Dependent sub-Task Orchestration (DeTOrch) model is proposed. DeTOrch uses a state-of-the-art partitioning method for decomposing tasks and proposes a novel mobility task-orchestration mechanism to minimize the task completion time and maximize the use of edge devices’ resource. The simulation results show that the proposed model can significantly improve the task success rate and decrease task completion time. In addition, in various scenarios with different levels of mobility, the proposed model outperforms the baselines while utilizing the resource of edge devices.
