Special Interest Group · CSCW 2026

Rethinking Social Computing Research in the Age of AI

Agency, Collaboration, and Governance — a gathering of researchers examining how AI is reshaping the questions, methods, and responsibilities of social computing.

Date & TimeWed, Oct 14, 2026 · 11:00 AM – 12:30 PM
LocationSalt Lake City, UT
FormatIn-person SIG session
Read the Overview → Add to Calendar
SIG Discussion Themes

Five Core Themes for AI-Supported Social Computing Research

As large language models and agentic AI systems become collaborators, mediators, and evaluators in scientific inquiry, the CSCW community faces fundamental questions about how we study, design for, and govern human–AI interaction. This SIG convenes researchers across five key discussion tracks:

THEME 01

Knowledge Building & Research Inquiry

While AI enhances cognitive capacity and accelerates literature synthesis, prolonged reliance risks eroding foundational researcher skills like critical analysis, methodological judgment, and creative ideation—especially for early-career scholars.

Key Discussion Questions
  • What does ethical use of AI agents mean to support short- and long-term knowledge acquisition or research inquiry?
  • How can AI support researchers in building lasting understanding rather than merely producing quick answers?
  • What designs and interaction modalities encourage reflection, skepticism, and intellectual ownership?
  • What forms of AI literacy are essential for researchers to conduct independent research inquiry?
THEME 02

Team Formation & Small/Large Team Collaboration

The emerging field of "AI-teaming" examines trust, coordination, and shared mental models when AI agents participate in human team dynamics—altering meeting flows, communication, and power dynamics across disciplinary boundaries.

Key Discussion Questions
  • What are the appropriate boundaries for using AI to facilitate team formation, communication, and research development?
  • How can AI help researchers discover complementary expertise, negotiate shared goals, and coordinate across boundaries?
  • How do different forms of AI-mediated communication affect trust, participation, and power dynamics across scales of collaboration?
  • When might AI recommendations reinforce existing academic hierarchies or biases?
THEME 03

Community Building Through Peer Review

A substantial fraction of peer reviews now contain AI-generated or AI-modified content. While LLMs can prompt reviewers to address overlooked issues, automated reviewers often fail to detect faulty reasoning, impacting community trust and accountability.

Key Discussion Questions
  • Which parts of the review process can be responsibly supported by AI (e.g., checking clarity, identifying missing literature, comparing reviewer comments)?
  • Which human tasks should remain non-replaceable (e.g., taking ultimate responsibility for recommendations)?
  • How might AI affect community building, mutual respect, and accountability of scholarly works?
THEME 04

Interdisciplinary Research Development & Evaluation

Early benchmarking efforts (such as SoundnessBench) attempt to evaluate whether AI agents can distinguish sound research ideas from flawed ones, raising critical questions about AI's role in defining "good research taste".

Key Discussion Questions
  • Should we teach AI systems to recognize, support, or challenge "good research taste"?
  • How should research taste be defined across diverse disciplines, methods, and communities?
  • What could go wrong if AI systems begin to shape what counts as novel, rigorous, fundable, or publishable research?
THEME 05

Authorship, Ownership, & Governance

Generative AI policies across journals and conferences are shifting rapidly from problem-framing to explicit governance, yet significant gaps remain in crediting AI contributions to ideation and analysis versus text generation.

Key Discussion Questions
  • How should researchers disclose, credit, and take responsibility for AI-supported ideation, development, and evaluation?
  • Who owns research ideas, text, data interpretations, or experimental designs generated with AI?
  • How do we distinguish harmful inefficiency from productive inefficiency (redundancy, disagreement, slower reflection) essential for creativity and science?
Organizers & Speakers

Organizers, Panelists & Invited Speaker

Researchers and industry leaders from across social computing and AI convening this session.

Yun Huang
University of Illinois Urbana-Champaign
Noshir Contractor
Northwestern University
Tai-Quan (Winson) Peng
Michigan State University
Dashun Wang
Northwestern University
Cuihua Shen
UC Davis
Manling Li
Northwestern University
Yiren Liu
Zoom & University of Illinois Urbana-Champaign
Hangyue Zhang
University of Illinois Urbana-Champaign
Sangho Suh
Allen Institute for AI
Invited Speaker
Chris Frank
Chris Frank
UXR Manager, Google Labs

When

Wednesday, Oct 14, 2026
11:00 AM – 12:30 PM

Where

Salt Lake City, UT
[TBD: venue name & room, part of CSCW 2026]

Contact

Get in touch
yunhuang@illinois.edu
Citation

Cite This Work

Yun Huang, Noshir Contractor, Tai-Quan Peng, Dashun Wang, Cuihua Shen, Manling Li, Yiren Liu, Hangyue Zhang, and Sangho Suh (2026). Rethinking Social Computing Research in the Age of AI: Agency, Collaboration, and Governance. Special Interest Group at CSCW 2026

Literature & Prior Work

Related Papers

Acknowledgments

Grant Support

This material is based upon work supported by the Institute of Education Sciences, U.S. Department of Education, through Grant 2229873 (NSF). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Organizations Partner institutions