The erasure of Indigenous cultures is a painfully enduring legacy of U.S. imperial history, producing disparities in culturally-relevant curricula, thus barring Indigenous students from resonant educational experiences. In Hawaiʻi, where Hawaiian instruction was banned for 91 years, culturally-relevant curricula gaps are most severe in Hawaiian-language education and technical fields like Computer Science (CS). Large Language Models (LLMs) are positioned to address these disparities, yet raise concerns of cultural misrepresentation and data sovereignty. We ask: how can LLMs serve educators of varying familiarity with Hawaiian culture while safeguarding Hawaiian knowledge? Addressing this question, we present Kumu Connect, an LLM-assisted culturally-revitalizing CS lesson plan generator, co-designed with Native Hawaiian educators. This work contributes design requirements, a situated case study, and “future imaginaries” paired with design provocations to highlight how community-engaged development can inform culturally-authentic and pedagogically useful AI tools that address CS and culture knowledge gaps and uphold Indigenous data sovereignty.

Climate change, urbanization, and pollution threaten ecosystems and the treaty-guaranteed rights of Native Nations in the Great Lakes region. Tools that support culturally relevant implementation of policy and meaningful access to environmental data for sentinel species like Manoomin, wild rice, can help uphold treaty rights and ensure environmental stewardship. This paper presents Noondawind, an interactive data platform co-designed with Ojibwe partners to support community members and Tribal staff in interpreting and acting on environmental data and policy resources. We engaged in a participatory design process informed by and deeply integrated with Ojibwe worldviews. Our results highlight how participatory and culturally relevant co-design approaches can enhance environmental governance, support data sovereignty, and foster engagement with environmental data. We offer design implications and lessons learned for projects developing tools in partnership with Indigenous communities. These findings contribute to the growing field of Indigenous HCI and social justice literature in HCI.

Amid escalating ecological challenges, Human-Computer Interaction (HCI) researchers have begun adopting More-than-Human design (MtHD) approaches as a means of reimagining and strengthening the bonds between the human and non-human world. Taking a MtHD approach, this work investigates how loofah, a plant-based, biodegradable material, can be used to foster ecological awareness in everyday life. We present a material exploration of loofah, including initial encounters with loofah as a uniquely structural material, a design space focused on how loofah can be combined with various indicators that respond to different environmental factors like temperature, UV radiation, pH of water and soil, the Iron and moisture in soil and utilizing loofah as a substrate for plant growth. Based on this design space, we create two wearables: a hat and a glove. These artifacts incorporate environmental sensing capabilities and host live microgreens, highlighting loofah‘s potential as both an interface and habitat. Through a 15-day autoethnographic journaling process by the first and second authors, we reflect on the embodied experiences of “wearing” environmental change and cultivating on-body ecological practices. This pictorial contributes to the HCI community by introducing a biomaterial-based embodied interaction to provoke reflections on the relationships between materials, non-human forms, and the environment, while integrating functional considerations into MtHD.

The growth of urban centers, the impacts of climate change, and the need for information to inform city planning and community organizing have intensified the need for monitoring solutions in cities. The advancement of low-cost sensor technologies and digital twin frameworks presents an opportunity for cities to deploy extensive, real-time monitoring systems. However, few examples of long-term, large-scale, publicly available urban sensor datasets exist, limiting the ability of researchers and planners to explore important topics such as hyperlocal environmental variations. In this paper, we introduce a comprehensive dataset from a 118-node LTE-M connected, solar-powered air quality sensor network deployed across Chicago, Illinois, from April 2021 to April 2023. This dataset comprises 94,915,745 readings of United States EPA criteria air pollutants, in two parts: 1) 75,932,596 gas sensor readings for carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) 18,983,149 particulate matter (PM) readings. This open-access dataset uniquely enables researchers to examine critical aspects of smart city sensing, including environmental equity, sensor network deployment dynamics, and interactions between urban infrastructure, natural environments, and residents. Via integration with open datasets from the City of Chicago and other sources, this dataset serves as a foundational tool for advancing research in environmental justice, public health, and urban planning, empowering researchers to build and test ideas before going to deployment to move closer to achieving smart, sustainable urban environments.

Local workers and their knowledge are essential for sustainable and effective conservation efforts. However, many technology-assisted conservation programs are guided by global benchmarks (e.g., forest cover) and industry metrics (e.g., cost per acre), which often devalue local knowledge and fail to consider the economic and conservation goals of local workers. Assets-based design is well-suited to center workers and their strengths, yet it may fail to fully address the complexities of long-term conservation programs by not explicitly emphasizing workers' goals or bolstering their assets. We extend recent approaches in assets-based design literature that address these limitations through our case studies of reforestation, biodiversity monitoring, and carbon sequestration programs in three protected areas in Madagascar. We leverage a mixed-methods approach of direct reactive observations, unstructured interviews, and an informal design workshop, revealing emergent themes surrounding economic sustainability and the value of local ecological knowledge in conservation. Finally, we explore examples, tensions, and design considerations for worker-centered conservation technology to: (1) prioritize local knowledge, (2) foster love of nature, (3) center economic goals, and (4) embrace local autonomy. This work advances the dialogue on assets-based design, promoting the co-creation of equitable and sustainable conservation technologies with workers in Global South settings by centering local economic priorities and enhancing workers' strengths.

Urban environmental monitoring campaigns depend on expertise from city agencies, residents, and researchers. Deployment efforts rarely include all three stakeholders, typically leading to initiatives that struggle to produce credible, actionable data. We describe the implementation of a large-scale, long-term air quality sensing network in Chicago Illinois; detail stakeholder interviews and meetings; and present three interfaces—–a website accessible via in-situ QR codes, APIs, and a mobile, mixed-media experience. We show how a collaborative approach created a more equitable sensor distribution compared to crowdsourced or regulatory designs. We highlight shared goals of education, engagement, and empowerment despite the diversity of tool and analytics needs across stakeholder groups. Reflecting on our work, we develop a “three-legged stool” framework representing the criticality of balanced participation from three key stakeholder groups—city, community, and research—in deploying novel urban technologies. This approach can help HCI researchers facilitate more democratic technology deployments in urban spaces.

Hardware and software advances have paved the way for large-scale urban sensor networks, but there is no guidance on where to place nodes and how to assess network design. In this work, I first re-imagine coverage of urban sensor networks with a view toward cities as more than just physical spaces. I then propose data-driven and nature-based algorithms for urban sensor network design and compare these proposed designs to those used in prior and existing sensor networks. This work will enable the deployment of robust sensor networks that produce useful citywide data for numerous stakeholders despite the complexities of urban environments.

This paper presents Eclipse, a platform for low-cost urban environmental sensing using solar-powered and cellular-connected devices. Dense sensor networks promise to monitor pollution at fine spatial and temporal resolutions, yet few cities have actually implemented such networks due to high costs and limited accuracy. We address these barriers by developing an end-to-end framework for urban air quality sensing with minimal infrastructure requirements. We designed an unobtrusive device that collects data on fine particulate matter (PM2.5), temperature, relative humidity, and barometric pres-sure. A modular design further includes four low-cost gas sensors - Ozone (03), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) - selected based on local priorities. We deployed 115 devices across Chicago, reliably collecting data for over 90% of expected sensor-hours from July 2 - September 30, 2021. We further developed a calibration strategy that reduced errors by 41.2 – 98.8%, improving accuracy to levels recommended for hotspot detection (PM2.5and 03) or education (NO2and SO2). Through this work, we offer insights on the real-world deployment of a replicable, large-scale, end-to-end platform for hyperlocal urban environmental sensing.

People with Developmental Disabilities (DD) often rely on other people to perform basic activities such as leaving the house and accessing public spaces. This problem, exaggerated by a decrease in community engagement, has been documented to decrease their sense of self-efficacy. Telepresence robots provide a unique opportunity for people with DD to access public spaces, particularly for those who are homebound or dependent on others for using transportation or buying exhibit tickets. This research evaluates the use of telepresence robots operated by people with DD in exploring a public exhibit. This study was in partnership with Hope Services, an organization that provides skill-improving activities for people with DD. Our analysis consisted of quantitative and qualitative methods using data from semi-structured pre- and post-interviews focusing on participants' sense of physical and social self- efficacy, and well-being. Our study revealed positive trends toward showing that using telepresence can contribute to wellbeing and physical and social self-efficacy. Therefore, we believe that there is some promise for using telepresence robots to tour an exploratory space for people with DD and that it can be a viable option for those who face accessibility limitations.