Civic technologies have helped activists mobilize large groups of people to complete simple actions like sharing a post on social media or signing an online petition. While mobilizing large numbers of people to complete low effort actions is important, mobilizing does not develop peoples' capacities to organize, which requires moving people up an engagement ladder to interdependently work with others on increasingly complex and challenging collective actions. Research on civic organizing suggests that deliberating with others about what collective actions to plan and complete is key to developing people's capacities to organize. In this paper, we explore whether deliberation can help organizers support potential activists in moving up the organizing engagement ladder. DeliberationWorks, a computer-supported deliberation system presents potential activists with background information on collective actions and intrapersonal deliberation questions, facilitates group discussion with experienced organizers, and prompts activists to fill out action plans for completing actions. Findings across two field deployments suggest that DeliberationWorks effectively helped organizers support potential activists in increasing their knowledge and interest in taking collective action, as well as successfully planning actions. Yet our findings also present a complex picture of additional learning challenges organizers encounter in deepening potential activists' engagement with organizing beyond the deliberation. We present four distinct engagement journeys based on participants' experiences during and after the deliberation to inform the design of future socio-technical interventions for moving potential activists further up the ladder. Our findings suggest that future systems designed to develop people's capacities to organize should help organizers invest in potential activists' capacities to increase engagement in the organization through 1-1 coaching and follow-up communications, based on understanding of their interests and needs from the deliberation. We contribute a novel approach that leverages organizing theory to design deliberation features to support organizers in increasing people's engagement with organizing, as well as evidence collected across two case study deployments that contribute a deepened understanding of new potential activists' needs in getting started with organizing.

Open democratic innovations like participatory budgeting (PB) leverages local citizens’ collective intelligence (CI) to generate better ideas for improving local communities. PB depends on high participation and equal representation among citizens. While CI systems potentially allow an unprecedented number of citizens to participate, they have not solved the endemic challenges of low participation and unequal representation in local collectives. Building on theories of political organizing and civic technologies, we analyzed field notes, survey data, and document archives captured during a city-wide PB process to answer the research question: How might we design collectively intelligent systems to increase participation and representation in open democratic innovations like participatory budgeting? We found that while face-to-face outreach tactics such as canvassing are the most time consuming but also the most efficient, and critical for increasing the number of participants in PB, especially among traditionally marginalized communities. We also found that to carry out face-to-face outreach, organizing work such as training and coordinating volunteers is critical to scale outreach capacity. We argue that for CI systems to effectively promote high quality, open democratic innovations like PB, they need to support organizers to carry out organizing work. We discuss how CI systems should be designed to support organizing work (e.g., training volunteers) in face-to-face outreach (e.g., canvassing).

As the world faces new threats of authoritarianism, CSCW researchers must reckon with the dangerous consequences and new possibilities for technology to facilitate people-powered social change. Community organizing, where groups take collective action, develop leaders, and build power, has long been studied and practiced as one of the most impactful ways for ordinary people to contest for power on unequal political terrain. While existing civic technologies have explored collective actions from mass participation and mobilization, connective action, and democratic deliberation, CSCW researchers have focused less on examining the role of technologies in facilitating the complex work behind community organizing—from building the skills and capacities for organizations with powerful and resilient member bases, to supporting the relational work of transforming relationships into power. We invite researchers from across CSCW who are committed to social justice and democratic change to forge a research agenda for collaborative technologies that empower communities, particularly those most disenfranchised, to meet our global political moment. This SIG is designed to build relationships, establish a community of practice, and identify points of future research and collaboration—a crucial first step to realizing a transformative vision for CSCW research as civic and democratic duty in increasingly undemocratic times.

Entrepreneurship requires navigating open-ended, ill-defined problems: identifying risks, challenging assumptions, and making strategic decisions under deep uncertainty. Novice founders often struggle with these metacognitive demands, while mentors face limited time and visibility to provide tailored support. We present a human-AI coaching system that combines a domain-specific cognitive model of entrepreneurial risk with a large language model (LLM) to proactively scaffold both novice and mentor thinking. The system proactively poses diagnostic questions that challenge novices' thinking and helps both novices and mentors plan for more focused and emotionally attuned meetings. Critically, mentors can inspect and modify the underlying cognitive model, shaping the logic of the system to reflect their evolving needs. Through an exploratory field deployment, we found that using the system supported novice metacognition, reduced mentors' cognitive load, and improved meeting depth, intentionality, and focus--while also surfaced key tensions around trust, misdiagnosis, and expectations of AI. We contribute design principles for proactive AI systems that scaffold metacognition and human-human collaboration in complex, ill-defined domains, offering implications for similar domains like healthcare, education, and knowledge work.

Existing data visualization design guidelines focus primarily on constructing grammatically-correct visualizations that faithfully convey the values and relationships in the underlying data. However, a designer may create a grammatically-correct visualization that still leaves audiences susceptible to reasoning misleaders, e.g. by failing to normalize data or using unrepresentative samples. Reasoning misleaders are especially pernicious when presenting public policy data, where data-driven decisions can affect public health, safety, and economic development. Through textual analysis, a formative evaluation, and iterative design with 19 policy communicators, we construct an actionable visualization design framework, V-FRAMER, that effectively synthesizes ways of mitigating reasoning misleaders. We discuss important design considerations for frameworks like V-FRAMER, including using concrete examples to help designers understand reasoning misleaders, and using a hierarchical structure to support example-based accessing. We further describe V-FRAMER's congruence with current practice and how practitioners might integrate the framework into their existing workflows. Related materials available at: https://osf.io/q3uta/.

Educational Design Research (EDeR) methodologists argue that iteration is a core component of EDeR. Iteration is currently defined as a process of gathering more information through actions, such as testing, and using that information to improve the design. In this paper, we seek to tighten the definition of iteration to help EDeR teams conduct iterations more effectively. We argue that EDeR teams should organize their research in slices that deliver small but real value to end users while informing the design research. EDeR should pick slices that are: (a) minimal and focused, (b) deployed in a real context, (c) valuable to the end users, and (d) informative to the research. Slicing helps EDeR teams increase ecological validity when they test because it allows testing which is within real-world educational contexts or with the stakeholders who will use and be impacted by the design. Increasing ecological validity of testing is particularly important because EDeR projects tackle highly complex real-world problems with many unknown elements and relational complexity—this means it is challenging to predict what designs will have the desired impact without real-world deployment. Effective iteration through organizing research in slices helps EDeR teams to better support stakeholder goals, develop more impactful theory, and have greater and earlier impact upon education.

Citizens can increase openness, transparency, and accountability of institutions by taking part in face-to-face participatory policy-making deliberations, such as participatory budgeting assemblies. But for participants’ contributions to influence policy outcomes, organizers need to capture and synthesize participants’ input. Existing approaches are not inclusive for participants or require too much time from organizers. We designed e-scribing, a novel approach for capturing and synthesizing participants’ input from face-to-face deliberations in real time by combining scribes with digital technology. To evaluate the approach, we built DeliberationWorks, a digital deliberation technology that helps scribes (a) capture proxy input (i.e., as participants) that is complete and accurate so that participants do not need to interact with technology themselves and (b) synthesize the discussion in real time using labels. We deployed DeliberationWorks with 5 scribes in two face-to-face deliberations with 8-10 participants and found that, on average, 82% of the input was captured mostly accurately. After one hour of training, scribes synthesized input within 10 minutes of the end of the deliberation. Our findings suggest that e-scribing makes participatory policy-making more inclusive by allowing participants to share their input without interacting with technology, and more time-efficient by reducing synthesis and training times for organizers.

Most social challenges fall outside of the authority of any single individual and therefore require collective action—coordinated efforts by many stakeholders to implement solutions. Despite growing interest in teaching students to lead collective action, we lack models for how to teach these skills. Collective action ostensibly involves design: the act of planning to change existing situations into preferred ones. In other domains, instructors commonly scaffold design using an instructional model known as studio critique in which students strengthen their plans by exchanging arguments with peers and instructors. This study explores whether studio critique can serve as the basis for an effective instructional model in collective action. Using design-based research methods, we designed and implemented scoping deliberations , a new instructional model that augments studio critique with domain-specific templates for planning collective action and repeats weekly to enable iterations. We used process tracing to analyze data from field notes, video, and artifacts to evaluate causal explanations for events observed in this case study. By implementing scoping deliberations in a 10-week undergraduate course, we found that this model appeared effective at scaffolding engagement in planning collective action: students articulated and refined their plans by engaging in argumentation and iteration, as expected. However, students struggled to contact the community stakeholders with whom they planned to work. As a result, their plans rested on implausible, untested assertions. These findings advance instructional science by showing that collective action may require new instructional models that help students to test their assertions against feedback from community stakeholders. Practically, scoping deliberations appear most useful for scaffolding thoughtful planning in conditions when students are already collaborating with stakeholders.

One-to-many coaching is a common, yet difficult, coaching technique used in environments with many novices learning to solve ill-defined problems. Intelligent systems might be designed to support 1-to-many coaching but designing such systems requires a 1-to-many coaching model that details novices' challenges, coaches' strategies, and coaches' goals. To build such a model, we conducted interaction analysis on 24 1-to-many coaching sessions with novices developing new products in a university incubator and conducted retrospective analyses with 3 coaches and 30 novices. We contribute a model that demonstrates that coaches in a 1-to-many setting not only need to help novices develop metacognitive skills (just as in 1-to-1 coaching), but also need to utilize the presence and expertise of a group of novices to learn from each other, to mitigate their fear of failures, and provide them accountability. Our model informs design implications for future intelligent coaching systems to (1) assist coaches in monitoring and comparing many novices' progress, learning, and expertise; (2) provide novices with checklists, templates, and scaffolds to help them self-evaluate, seek-help, and summarize learning; (3) showcase failures and growth; and (4) publicize planning and progress to provide accountability.

To create design solutions experienced engineering designers engage in expert iterative practice. Researchers find that students struggle to learn this critical engineering design practice, particularly when tackling real‐world engineering design problems.

Learning Sciences researchers can radically enhance the rigor, communication, and training of design research by better defining the method. We define design research as: a method conducted by researchers to create practical solutions and theoretical design models through a design process of focusing, understanding, defining, conceiving, building, testing, and presenting that incorporates other research methods, iteratively increasing rigor, to search for solutions to practical problems of human learning. This definition has six important premises. First, design research is the preferred method for simultaneously producing new practical solutions and theories. Second, design research models build upon other theoretical products. Third, DR theory consists of design models that explain the learning mechanisms underpinning practical solutions. Fourth, design research achieves its validity by incorporating methodologies across fields into the design research process. Fifth, design research achieves efficiency by using quick, low-cost methods to search broadly, followed by more rigorous empirical methods and higher fidelity implementation after identifying promising solutions and models. Sixth, scaling requires different products, not phases, of design research. Conducting design research in this way allows researchers to design theory-based solutions that are more impactful and create theories that are useful in practice.

Mentoring is a key part of career development, especially in emerging fields such as social entrepreneurship. Internet technologies have made it easier for novice social entrepreneurs to identify and connect with mentors online. Yet, we do not know the strategies mentors use to advise professionals navigating emerging fields. Knowing these strategies would allow us to create technologies for more effective career mentoring. This paper presents an expert model of career mentoring for novice social entrepreneurs. To build the model, we conducted a retrospective cognitive task analysis with 9 mentors who have at least 5 years of experience advising novice social entrepreneurs. We found that mentors help novice social entrepreneurs regulate career-related stress and make decisions about next career steps. Our findings suggest that further exploration of career mentoring strategies might allow designers to develop technologies based on the expert model, such as intelligent agents, to support and scale mentorship in emerging fields.

This study builds on research of multimodal storytelling in educational settings by presenting a study of a youth-produced documentary on immigration. Drawing from a video documentary project in a high school class, we examine students’ representational processes of scaling in documentary storytelling, and the kinds of resources they use to construct multiple spatiotemporal contexts for understanding their experience of immigration and immigration policy. Our theoretical framework relates the concept of scale to the Bakhtinian concept of voice to consider the semiotic resources that are used to index and connect multiple social and spatiotemporal contexts in storytelling. Focusing on a documentary produced by some students in the class, we analyze how the young filmmakers used particular speaker voices (characters) and their social positioning to invoke and construct relevant scales for understanding the problem of deportation. Our analysis extends the study of scaling to multimodal texts, and the strategies that people use to represent and configure relationships among different socially stratified spaces. By conceptualizing the relations between voice and scale, this work aims to contribute to literacy learning and teaching that support young people in bringing their knowledge, experiences, and narrative resources to engage with societal structures.

Iteration is an important design process that novice designers struggle to follow. However, iteration is difficult to coach because we do not understand the underlying metacognitive knowledge required for effective iteration. We developed the Design Risks Framework, which helps researchers to identify the knowledge underlying three metacognitive processes that control iteration: focusing attention on key areas of the project, identifying project risks, and choosing iterative strategies to mitigate risks. We tested the framework over a 6-week period with 5 novice design teams and found that novices seemed to lack metacognitive knowledge of 49 criteria for identifying project risks. By using this framework to diagnose knowledge gaps and design coaching interventions, educators and managers can improve how novice designers iterate in design projects.

To investigate how we might expand learning environments to include professionals to coach students enacting disciplinary practices, we created StandUp, a socially-shared regulation of learning (SSRL) system. We implemented StandUp in an undergraduate design program with 3 student teams and 5 volunteer professionals. We captured 12 online coaching interactions that significantly changed project trajectories. This suggests SSRL designs can encourage online coaching that influences project trajectories.