Community engagement processes in representative political contexts, like school districts, generate massive volumes of feedback that overwhelm traditional synthesis methods, creating barriers to shared understanding not only between civic leaders and constituents but also among community members. To address these barriers, we developed StoryBuilder, a human-AI collaborative pipeline that transforms community input into accessible first-person narratives. Using 2,480 community responses from an ongoing school rezoning process, we generated 124 composite stories and deployed them through a mobile-friendly StorySharer interface. Our mixed-methods evaluation combined a four-month field deployment, user studies with 21 community members, and a controlled experiment examining how narrative composition affects participant reactions. Field results demonstrate that narratives helped community members relate across diverse perspectives. In the experiment, experience-grounded narratives generated greater respect and trust than opinion-heavy narratives. We contribute a human-AI narrative synthesis system and insights on its varied acceptance and effectiveness in a real-world civic context.

Trust and transparency in civic decision-making processes, like neighborhood planning, are eroding as community members frequently report sending feedback “into a void” without understanding how, or whether, their input influences outcomes. To address this gap, we introduce Voice to Vision, a sociotechnical system that bridges community voices and planning outputs through a structured yet flexible data infrastructure and complementary interfaces for both community members and planners. Through a five-month iterative design process with 21 stakeholders and subsequent field evaluation involving 24 participants, we examine how this system facilitates shared understanding across the civic ecosystem. Our findings reveal that while planners value systematic sensemaking tools that find connections across diverse inputs, community members prioritize seeing themselves reflected in the process, discovering patterns within feedback, and observing the rigor behind decisions, while emphasizing the importance of actionable outcomes. We contribute insights into participatory design for civic contexts, a complete sociotechnical system with an interoperable data structure for civic decision-making, and empirical findings that inform how digital platforms can promote shared understanding among elected or appointed officials, planners, and community members by enhancing transparency and legitimacy.

IUI ’25, Cagliari, Italy

Public school districts across the United States (US) play a pivotal role in shaping access to quality education through their student assignment policies---most prominently, school attendance boundaries. Community engagement processes for changing such policies, however, are often opaque, cumbersome, and highly polarizing---hampering equitable access to quality schools in ways that can perpetuate disparities in achievement and future life outcomes. In this paper, we describe a collaboration with a large US public school district serving nearly 150,000 students to design and evaluate a new sociotechnical system, "BoundarEase", for fostering more constructive community engagement around changing school attendance boundaries. Through a formative study with 16 community members, we first identify several frictions in existing community engagement processes during boundary planning, like individualistic over collective thinking; a failure to understand and empathize with different community members when considering policy impacts; and challenges in accessing and understanding the impacts of boundary changes. We then use these frictions to inspire the design and development of BoundarEase, a web platform that allows community members to explore and offer feedback on potential boundaries based on their preferences. A user study with 12 community members reveals that BoundarEase prompts reflection among community members on how policies might impact families beyond their own, and increases transparency around the details of policy proposals. Our paper offers education researchers insights into the challenges and opportunities involved in community engagement for designing student assignment policies; human-computer interaction researchers a case study of how new sociotechnical systems might help mitigate polarization in local policymaking; and school districts a practical tool they might use to facilitate community engagement to foster more equitable student assignment policies.

Suyash Fulay, William Brannon, Shrestha Mohanty, Cassandra Overney, Elinor Poole-Dayan, Deb Roy, Jad Kabbara. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Community organizations face challenges in harnessing the power of qualitative data analysis, or sensemaking, to understand the diverse perspectives and needs brought up by their constituents. One of the most time-consuming and tedious parts of sensemaking is qualitative coding, or the process of identifying themes across a large and unstructured corpus of community input. A challenge in qualitative coding is attaining high intercoder reliability, especially between expert and beginner sensemakers. In this work, we present SenseMate, a novel human-AI system designed to help with qualitative coding. SenseMate leverages rationale extraction models, a new machine learning strategy to semi-automate sensemaking, which produces theme recommendations and human-interpretable explanations. The models were trained on a dataset of people’s experiences living in Boston, which was annotated for themes by expert sensemakers. We integrated rationale extraction models into SenseMate through an iterative, human-centered design process revolving around four key design principles derived from an extensive literature review. The design process consisted of three iterations with continuous feedback from seven people associated with community organizations. Through an online experiment involving 180 novice sensemakers, we aimed to determine whether AI-generated recommendations and rationales would decrease coding time, increase intercoder reliability (i.e. Cohen’s kappa), and minimize differences between novice and expert coding decisions (i.e. F-score of participant answers compared to expert gold labels). We found that though the model recommendations and explanations increased coding time by 49 seconds per unit of analysis, they raised intercoder reliability by 29% and coding F-score by 10%. Regarding the effectiveness of SenseMate’s design, participants reported that the platform was generally easy to use. In summary, Sensemate is (1) built for beginner sensemakers without a technical background, a user group that prior work doesn’t focus on, (2) implements rationale extraction models to recommend themes and generate explanations, which has advantages over large language models in terms of user privacy and control, and (3) contains original and intuitive features created from user feedback that can be applied to future QDA systems.

Educational data scientists often conduct research with the hopes of translating findings into lasting change through policy, civil society, or other channels. However, the bridge from research to practice can be fraught with sociopolitical frictions that impede, or altogether block, such translations-especially when they are contentious or otherwise difficult to achieve. Focusing on one entrenched educational equity issue in US public schools-racial and ethnic segregation-we conduct randomized email outreach experiments and surveys to explore how local school districts respond to algorithmically-generated school catchment areas ("attendance boundaries") designed to foster more diverse and integrated schools. Cold email outreach to approximately 4,320 elected school board members across over 800 school districts informing them of potential boundary changes reveals a large average open rate of nearly 40%, but a relatively small click-through rate of 2.5% to an interactive dashboard depicting such changes. Board members, however, appear responsive to different messaging techniques---particularly those that dovetail issues of racial and ethnic diversity with other top-of-mind issues (like school capacity planning). On the other hand, media coverage of the research drives more dashboard engagement, especially in more segregated districts. A small but rich set of survey responses from school board and community members across several districts identify data and operational bottlenecks to implementing boundary changes to foster more diverse schools, but also share affirmative comments on the potential viability of such changes. Together, our findings may support educational data scientists in more effectively disseminating research that aims to bridge educational inequalities through systems-level change.

Open source is ubiquitous and many projects act as critical infrastructure, yet funding and sustaining the whole ecosystem is challenging. While there are many different funding models for open source and concerted efforts through foundations, donation platforms like PayPal, Patreon, and OpenCollective are popular and low-bar platforms to raise funds for open-source development. With a mixed-method study, we investigate the emerging and largely unexplored phenomenon of donations in open source. Specifically, we quantify how commonly open-source projects ask for donations, statistically model characteristics of projects that ask for and receive donations, analyze for what the requested funds are needed and used, and assess whether the received donations achieve the intended outcomes. We find 25,885 projects asking for donations on GitHub, often to support engineering activities; however, we also find no clear evidence that donations influence the activity level of a project. In fact, we find that donations are used in a multitude of ways, raising new research questions about effective funding.