What question did this study set out to answer?

The goal is to identify critical evaluation issues in AI cognitive assessments and propose solutions.

May 15, 2026

Six principles for evaluating cognitive capabilities in AI models

Key Points

The goal is to identify critical evaluation issues in AI cognitive assessments and propose solutions.
Describes evaluation issues affecting AI performance predictions.
Proposes six principles based on psychology for better AI assessments.
Illustrates principles with case studies from psychology and AI literature.
Benchmark performance often fails to reflect real-world AI capabilities.
Six proposed principles aim to enhance rigor in AI cognitive evaluations.
Case studies provide evidence of the necessity for improved evaluation approaches.

Abstract

Abstract Modern AI systems have exceeded human performance on many benchmarks meant to evaluate general cognitive capacities. However, it is often the case that benchmark performance does a poor job of predicting general capacities in real‐world settings. In this article I describe several issues related to evaluation that can cause this mismatch, and propose six principles, inspired by developmental and comparative psychology, that need to be adopted to enable rigorous evaluation for AI systems. These principles are illustrated by case studies from the psychology and AI literature.

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Cite This Study

Melanie Mitchell (Tue,) studied this question.

synapsesocial.com/papers/6a06b95be7dec685947ac03d https://doi.org/https://doi.org/10.1002/aaai.70061

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