This study leaves the classroom entirely: it tests whether peer-based review can replace expert review inside corporate knowledge-management systems, and finds that it can.
Cho, K., Chung, T. R., King, W. R., Schunn, C. D. (2008). "Peer-based computer-supported knowledge refinement: An empirical investigation." Communications of the ACM, 51(3), 83–88.
Corporate knowledge-management systems — "best practices" and "lessons learned" repositories — are usually expensive to run because they depend on expert judgment to decide which submissions are good enough to include. This study asked directly: is expert review actually necessary, or can peer-based refinement, using a computer-support system, produce comparably good results at lower cost?
The researchers ran both an experimental study and a corporate application comparing the quality of results from peer-based knowledge refinement against expert-centric refinement, specifically measuring result quality rather than the relative cost of each approach.
Nonexpert peer-based knowledge refinement produced results equal to, and in some conditions better than, expert-centric refinement. The probability of catching a serious problem increased as the number of peer reviewers grew — more peers substituted effectively for fewer experts.
This is the most directly corporate paper in this body of research: it wasn't testing a classroom analogy for the workplace, it was testing the workplace itself. Organizations running knowledge repositories, best-practices libraries, or lessons-learned systems don't need to gatekeep every submission through scarce expert reviewers. Structured review by multiple non-expert peers is a validated substitute, and it scales in a way expert-only review cannot.
Chris Schunn co-authored this study, applying the same peer-review evidence base directly to corporate knowledge management. He is a Professor of Psychology, Learning Sciences and Policy, and Intelligent Systems at the University of Pittsburgh and a Senior Scientist at the University's Learning Research and Development Center (LRDC), where he has directed research projects backed by more than $80M in federal grants. As Peerceptiv's Chief Learning Scientist, his research directly shapes how the platform structures reviewer prompts, rubrics, and feedback workflows.
