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CHI '26 · Honorable mention · full-paper review · confidence medium-high

Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

Mo Houtti , Moyan Zhou , Daniel Runningen , Surabhi Sunil , Leor Porat , Harmanpreet Kaur , Loren Terveen , Stevie Chancellor

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This is a credible CHI contribution because it pairs a concrete AI-mediated feedback system with evidence from both lab and field. The main value is not just the interface idea, but the demonstration that organizational context can redirect use away from the intended feedback workflow.

Axes Lens

Rare contribution shape, typical evidence profile. The point here is not a score. It is to show what kind of claim the paper makes, and whether the evidence pattern is unusual or baseline in this 268 -review set.

Review Summary

This paper’s strongest contribution is its combination of a specific sociotechnical mechanism and a realistic account of deployment. The authors do not merely propose that AI can help meetings; they instantiate an AI feedback mediator and use the Induced Hypocrisy Procedure to make feedback exchange feel safer and more actionable. That is a plausible departure from common assumptions that feedback must be direct, interpersonal, and therefore socially risky. The validation is also appropriately mixed: the lab study provides evidence that the system can change interaction dynamics in the intended direction, with more balanced speaking times and improved meeting quality, while the field study shows that the same system does not automatically transfer into organizational practice. Instead, the field deployment reveals that the system may be repurposed for personal reflection when organizational barriers are present. That makes the paper valuable as both a system contribution and an empirical warning about adoption. The main limitation is scope: the field setting is small, and the evidence supports the claim in the studied contexts rather than a broad generalization about all organizations. Still, the paper’s field result is important because it grounds the sociotechnical argument in observed behavior rather than speculation. Overall, this reads as a solid honorable-mention-level CHI paper: technically grounded, empirically supported, and attentive to the mismatch between designed interaction and organizational reality.

What Changed

Canon before

Prior work on meeting inclusion and feedback has treated feedback as socially difficult and often constrained by organizational dynamics; AI-mediated feedback is positioned here as a way to reduce interpersonal risk and enable exchange.

Departure from common sense

The paper argues that an AI agent can serve as a psychologically safer recipient for feedback, making feedback exchange more feasible in meetings even though social dynamics usually make direct feedback difficult.

Actual novelty

The paper’s novelty is a sociotechnical meeting system that combines an AI feedback mediator with the Induced Hypocrisy Procedure, plus empirical findings from both lab and field settings about how organizational barriers shape use. The contribution is not just that AI can summarize or surface meeting behavior, but that it is used as an intermediary for feedback exchange and behavior change, with the field study showing that organizational context can redirect the system toward reflection rather than direct exchange.

Evidence

The evidence supports a mixed-methods contribution: a within-subjects lab study with 28 participants showed more balanced speaking times and improved meeting quality, while a field study at a small consulting firm with 10 participants surfaced organizational barriers that shifted use toward personal reflection rather than feedback exchange.

Limits

Method limits

The validation is split across a controlled lab study and a small field deployment, so the evidence is strong for the reported settings but limited in scale and in how broadly it can support claims about organizational feedback exchange.

Deployment limits

In the field, organizational barriers led participants to use the system for personal reflection rather than feedback exchange, indicating that real-world deployment depends on local norms, time pressure, hierarchy, and willingness to engage with mediated feedback.

Boundary conditions

The approach appears most viable when teams are willing to use an AI mediator for reflective feedback and when organizational context does not suppress participation; the field study suggests that contextual fit, role hierarchy, and time availability are key boundary conditions.

Manual check: false

Position in field

This sits at the intersection of CSCW/CHI work on meeting inclusion, AI-mediated social interaction, and organizational feedback tools, with the field result emphasizing that sociotechnical adoption depends on workplace context rather than interface capability alone.

Abstract

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.