Developing the PsyCogMetrics™ AI Lab to Evaluate Large Language Models and Advance Cognitive Science—A Three-Cycle Action Design Science Study

Read full text
Authors: Zhiye Jin, Yibai Li, K.D. Joshi, Xuefei Deng, Emily Lee
Cited by: 1

Abstract

This study presents the development of the PsyCogMetrics™ AI Lab (https://psycogmetrics.ai), an integrated, cloud-based platform that operationalizes psychometric and cognitive-science methodologies for Large Language Model (LLM) evaluation. Framed as a three-cycle Action Design Science study, the Relevance Cycle identifies key limitations in current evaluation methods and unfulfilled stakeholder needs. The Rigor Cycle draws on kernel theories such as Popperian falsifiability, Classical Test Theory, and Cognitive Load Theory to derive deductive design objectives. The Design Cycle operationalizes these objectives through nested Build–Intervene–Evaluate loops. The study contributes a novel IT artifact, a validated design for LLM evaluation, benefiting research at the intersection of AI, psychology, cognitive science, and the social and behavioral sciences.

Fields of Study & Subjects

Explainable Artificial Intelligence (XAI) Artificial Intelligence in Healthcare and Education Neurobiology of Language and Bilingualism

Publication details

Journal / source
Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Publisher
Hawaii International Conference on System Sciences
Publication date
1 January 2026
Language
EN