LLM-based chatbots like ChatGPT have become popular tools for assisting with coding tasks. However, they often produce isolated responses and lack mechanisms for social learning or contextual grounding. In contrast, online coding communities like Kaggle offer socially mediated learning environments that foster critical thinking, engagement, and a sense of belonging. Yet, growing reliance on LLMs risks diminishing participation in these communities and weakening their collaborative value. To address this, we propose Community-Enriched AI, a design paradigm that embeds social learning dynamics into LLM-based chatbots by surfacing user-generated content and social design features from online coding communities. Using this paradigm, we implemented a RAG-based AI chatbot leveraging resources from Kaggle to validate our design. Across two empirical studies involving 28 and 12 data science learners, respectively, we found that Community-Enriched AI significantly enhances user trust, encourages engagement with community, and effectively supports learners in solving data science tasks. We conclude by discussing design implications for AI assistance systems that bridge—rather than replace—online coding communities.

Peer Reviewed

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.

Team dating, or small-group interactions, can expose people to diverse perspectives and inform the potential for longer-term collaboration. However, rapidly configuring groups and facilitating interactions among strangers can be difficult, especially in co-located settings. We present ProtoTeams, a system that leverages personal mobile devices to support rapid group formation, to facilitate group activities, and to collect data about the potential for future collaboration. We report on a field study where 406 students in eight different project-based classes used ProtoTeams to interact with classmates through multiple rounds of brief discussion activities before selecting teammates for a term project. We found that the system enables groups to form in about one minute, allows for meaningful interactions with a diverse range of peers, and can significantly influence subsequent teammate selection. We discuss design implications and challenges for in-person team dating in classrooms and other contexts.

Many college computer science majors have little or no pre-college computing experience. Previous work has shown that inexperienced students under-perform their experienced peers when placed in the same introductory courses, and are more likely to drop out of the CS program. However, not much is known about what, if any, differences may persist beyond the introductory sequence for students who remain in the program. We conducted a study across all levels of a CS program at a large public university in the United States to determine whether grade differences exist between students with and without pre-college experience, and if so, for what types of experiences. We find significant grade differences in courses at all levels of the program. We further find that students who took AP Computer Science receive significantly higher average grades---by up to a half grade---in nearly all courses we studied. Pre-college experience appears to have a weaker relationship with retention and with low-stakes assessment grades. We discuss the limitations of these findings and implications for high school and college level CS courses and programs.