1982: Building an Inference Engine Using AI and The World’s Largest Meta- Analysis: Lesson Learned (17872)

Tuesday, August 13, 2024

09:45 – 11:15 CT (GMT-5/UTC-5)

Location: Swissotel: Alpine I

Session Number: 1982
Symposium (panel)
Showcase Symposium,Highlight
Shareable link: https://cdmcd.co/Ed4zwR

Moderator(s)

PS

Piers Steel

U. of Calgary, Canada

Panelist(s)

JC

Jason Colquitt

U. of Notre Dame, United States
HF

Hadi Fariborzi

Mount Royal U., Canada
BK

Burak Cem Konduk

Metropolitan State U., United States
DL

David Montgomery Long

William & Mary, United States
SR

Stephen Reid

U. of Iowa, United States
IV

Isabel Villamor

IESE Business School, Spain

This symposium describes the lessons learned during the creation of the world's largest meta-analysis, that is over 2,500 studies assessing over 160 constructs from the field of Organizational Justice. By combining cloud based meta-analytic databases, online statistical engines, curated selection of academic articles and the new LLMs, we have elevated this into an effective inference engine. Using the LLM, questions are translated into variables and specific search terms. Based on these variables, the relevant empirical results are drawn from the cloud based meta-analytic database and analyzed by an online statistical engine. Interpretation of results are enhanced by a core base of review articles as well as relevant articles drawn using the search terms from the meta-analysis database, whereupon the LLM provides custom, empirically sourced answers (with appropriate citations) back to the user in seconds. For more sophisticated queries, the LLM can create the specific R code to analyze the database, which is then executed by the statistical engine. As we will demonstrate, inference engines based on meta-analytic databases appear to be the ideal vehicle for interacting with scientific knowledge.