Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study (n = 28), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm (n = 10) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.

Learning is essential for the long-term sustainability of apprenticeship-based communities; it lets novices gradually progress to experts through active engagement. However, the use of large language models (LLMs) can disrupt this trajectory: if novices rely on LLMs to generate content, they risk short-circuiting the learning opportunities embedded in the process of creating content themselves. To address this tension, we created WikiCoach, a Q&A–style LLM system designed to scaffold learning-through-doing in Wikipedia. WikiCoach does not generate content directly; instead it supports editors via three stages: 1) introducing relevant community policies and guidelines, 2) operationalizing them into actionable steps, and 3) providing evaluative feedback on initial drafts. We conducted a within-subjects study (N=28) comparing WikiCoach with a baseline content generation model, and found that WikiCoach significantly improved policy understanding and adherence to community norms, while increasing critical thinking and cognitive engagement. Participants also preferred WikiCoach for future editing tasks. Qualitative analysis revealed how WikiCoach shaped users’ expectations and interactions. Our findings demonstrate that AI systems can simultaneously support task completion and learning, offering a future direction for responsibly sustaining apprenticeship-based communities in the era of generative AI.

Video conferencing meetings are more effective when they are inclusive, but inclusion often hinges on meeting leaders' and/or co-facilitators' practices. AI systems can be designed to improve meeting inclusion at scale by moderating negative meeting behaviors and supporting meeting leaders. We explored this design space by conducting $9$ user-centered ideation sessions, instantiating design insights in a prototype ``virtual co-host'' system, and testing the system in a formative exploratory lab study ($n=68$ across $12$ groups, $18$ interviews). We found that ideation session participants wanted AI agents to ask questions before intervening, which we formalized as the ``Observe, Ask, Intervene'' (OAI) framework. Participants who used our prototype preferred OAI over fully autonomous intervention, but rationalized away the virtual co-host's critical feedback. From these findings, we derive guidelines for designing AI agents to influence behavior and mediate group work. We also contribute methodological and design guidelines specific to mitigating inequitable meeting participation.

Recommender systems often struggle to strike a balance between matching users’ tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users’ preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don’t like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different levels of exploration control and users’ subjective preferences on interfaces are more predictive of their satisfaction with the recommender.

Workplace bias creates negative psychological outcomes for employees, permeating the larger organization. Workplace meetings are frequent, making them a key context where bias may occur. Video conferencing (VC) is an increasingly common medium for workplace meetings; we therefore investigated how VC tools contribute to increasing or reducing bias in meetings. Through a semi-structured interview study with 22 professionals, we found that VC features push meeting leaders to exercise control over various meeting parameters, giving leaders an outsized role in affecting bias. We demonstrate this with respect to four core VC features---user tiles, raise hand, text-based chat, and meeting recording---and recommend employing at least one of two mechanisms for mitigating bias in VC meetings---1) transferring control from meeting leaders to technical systems or other attendees and 2) helping meeting leaders better exercise the control they do wield.