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.

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.

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/.

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.

Supporting learning in online communities is an important direction for the future of human–computer interaction as people increasingly leverage social technologies to support professional growth and development. However, few have studied how people leverage the socio-technical affordances of online informal workplace communities to develop professional skills in the absence of dedicated expert guidance. We draw from theories of apprenticeship to introduce an emergent theory of distributed apprenticeship, which outlines how community expectations of transparency and mutual support allow for instruction to be directed by a distributed network of nonexperts. We develop distributed apprenticeship through a qualitative study of crowdfunding entrepreneurs, where novices leverage social interactions with community members to develop a wide range of entrepreneurial skills. We then generalize distributed apprenticeship to other workplace contexts and provide design implications for online communities where people develop professional skills with minimal dedicated formal guidance.

Novices learn innovation best through project-based learning (PBL), working in face-to-face teams to tackle real-world problems. Yet, real-world projects are complex, stressful, and especially challenging for novices. Online communities could provide social support to motivate novices, but it is unclear how to design online communities to support face-to-face PBL teams. Here we ask:How might we design an online system that enlists external supporters to provide online social support to motivate PBL students?Our need-finding study found that PBL students received infrequent social support, rarely engaged in help-seeking, and perceived little progress until the end of their projects. Based on these findings, we designedCheerOn, an online social support system that prompts novice student teams to externalize progress allowing external, online supporters to offer social support. We testedCheerOnwith 3 PBL teams and 15 external supporters over a 6-week course. We found that external supporters provided instrumental, informational, and emotional support that strengthened students’ bonds to the community, which increased help-seeking. Supporters also provided appraisal support, which increased students’ perceived value of their work. Supporters were more likely to offer informational and instrumental support when they were promoted or saw a clear need for help; supporters who received gratitude from students were more likely to offer emotional support in return; and supporters who were closely connected to the community were more likely to offer appraisal and instrumental support. Theoretically, this research contributes to our understanding of how hybrid face-to-face and online communities can impact the behavior of PBL students, specifically towards the facilitation of help-seeking behavior, as well as increased understanding of how different types of social support (i.e., appraisal, emotional, informational, and instrumental) can impact the participation of PBL students and supporters. Practically, this research contributes to our understanding of how to design socio-technical systems that facilitate social support for offline novice PBL students working, expanding the instructional resources available for preparing novices in PBL environments.

Undergraduate research experiences enhance learning and professional development, but providing effective and scalable research training is often limited by practical implementation and orchestration challenges. This paper introduces Agile Research Studios (ARS)--a socio-technical system that expands research training opportunities by supporting research communities of practice without increasing faculty mentoring resources. ARS integrates and advances professional best practices and organizational designs, principles for forming effective learning communities, and design of social technologies to overcome the orchestration challenge of one faculty researcher mentoring 20 or more students. We present the results of a two-year pilot of the Design, Technology, and Research (DTR) program, which used the ARS model to improve the quality of learning, produce research outcomes, and lower the barrier to participation while increasing the number of students who receive authentic research training.

Feedback is important to the creative process, but not everyone has a personal crowd of individuals they can turn to for high-quality feedback. We introduce and evaluate Critiki, a novel system for gathering design critiques on crowdfunding project pages from paid crowdworkers. Stemming from previous research on crowdfunding project creators and their need for early-stage design feedback, we design and build a working system which fits the need of this population: rapid and inexpensive feedback. To solve issues with critique quality we describe a scaffolding technique designed to assist crowdworkers in writing high-quality critiques. We evaluate Critiki with two field deployments: 1) A randomized controlled experiment with 450 crowdworkers to evaluate the efficacy of the scaffolding technique and 2) A user study with 31 crowdfunding project creators to determine usability and user satisfaction. We contribute to research on Creativity and Cognition by demonstrating a working creativity support system, empirically evaluating the system, and describing how scaffolding approaches can be designed for other crowdsourced tasks

Creativity support tools help learners undertake creative work, such as facilitating coaching by creative professionals. How might we design creativity support tools that in-crease learners' access to coaching by creative professionals? This study took place in an extracurricular project-based learning program where students were co-located, and met professional coaches face-to-face once a week but otherwise communicated online. To test an online creativity support tool called the Loft and investigate coach-student communication we collected data from 47 interviews, online log data and field observations. We found that (a) explicit help-seeking was rare outside of meetings, (b) help from professionals was highly-valued but not sought out, and (c) online systems could surface learner struggles and trigger help-giving. Our findings suggested that online creativity platforms can support professional coaching through: (1) structured virtual updates (2) coach thanking, (3) Computer-Supported Group Critique, (4) disclosure of expertise, and (5) help-seeking training.

Groups of novice critiquers can sometimes provide feedback of the same quality as a single expert. Unfortunately, we do not know how to create systems for novice group critique in design education. We tested whether 4 principles: write-first scripts, critique prompts, interactive critiquing & formative framing, allow us to create systems that combine the advantages of face-to-face and computer-mediated critique. We collected observations and 48 interviews with 12 undergraduate design students who used a computer supported group critique system over 5 critique sessions, analyzed using grounded theory. We found that: (a) the write-first script helped overcome initial learning costs; (b) the interactive critique features created a dual-channel critique that increased the number of critiquers, duration of critique and interactivity; and (c) the system produced a greater volume of useful critique and promoted reciprocity among critiquers. The study provides improved principles for developing computer supported novice group critique systems in design.

Democracy requires students to choose policy positions based on evidence, yet confirmation bias prevents them from doing so. As a preliminary step in building a policy reasoning tutor, this study identifies where bias occurs during the search and analysis of evidence in a policy reasoning task. 60 university students played an on-line game in which they chose which of four policies would increase school performance. The between-subjects design compared a free search group who searched for evidence in google-like environment, to a sequential presentation group who read all available evidence, and manipulated whether the evidence confirmed or disconfirmed students' prior beliefs. The study measured the impact on students' evidence-based recommendations, their change in beliefs, and their recall of the evidence. Results showed that students did not cherry-pick evidence nor discount disconfirming evidence. However, students' extreme confidence in their initial beliefs usually prevented them from changing position, and they mistakenly recalled the evidence as confirming their beliefs. The results suggest that a policy tutor should focus on evidence synthesis and making recommendations based on explicit evidence.