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

TL;DR

This paper introduces the PsyCogMetrics AI Lab, a cloud-based platform that integrates psychometric and cognitive science methods to evaluate large language models through a structured three-cycle design science approach.

Contribution

It presents a novel, validated design for LLM evaluation that combines interdisciplinary theories and iterative development cycles, advancing research in AI and cognitive sciences.

Findings

Development of a comprehensive evaluation platform

Identification of key limitations in current LLM assessments

Validation of a new evaluation design

Abstract

This study presents the development of the PsyCogMetrics AI Lab (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.