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Evaluating LLM-Based Coders in Psychological Assessment: A Validation Framework With Application to the Rorschach

Ruam P F A Pimentel¹, Gregory J Meyer^1,2

¹Rorschach Performance Assessment System, OH, USA.

|June 19, 2026

Summary

A new framework validates large language model (LLM) scoring in psychological assessment. LLM coders demonstrated reliable and valid scoring, comparable to human experts, for Rorschach Morbid Content.

Keywords:

Rorschach automated scoring morbid content psychological assessment psychometrics validation framework

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Published on: September 27, 2020

Area of Science:

Psychological assessment
Artificial intelligence in mental health
Psychometrics

Background:

Large language models (LLMs) are emerging tools in psychological assessment.
Current standards for evaluating LLM scoring accuracy are insufficient.
Reliable and valid scoring is crucial for clinical utility.

Purpose of the Study:

To introduce a reproducible framework for evaluating LLM-based scoring systems.
To assess the reliability and validity of LLM coders in psychological testing.
To provide practical guidance for implementing automated scoring.

Main Methods:

Developed a validation framework with pre-validation and standardized phases.
Applied a two-agent LLM workflow for Morbid Content (MOR) scoring in the Rorschach task.
Evaluated LLM performance on an independent dataset (n=84) using agreement and validity metrics.

Main Results:

LLM coder achieved good response-level agreement (kappa = .72-.74) and excellent protocol-level agreement (ICC = 0.94-0.95) with human assessors.
Demonstrated high internal consistency (ICC = 0.97-0.99) and replicated external validity (r = .59-.71).
LLM validity metrics were comparable to those of human coders (r = .54-.65).

Conclusions:

The proposed framework offers a clear and reproducible method for LLM scoring validation.
LLM-based scoring systems can achieve reliable and valid results in psychological assessment.
This work guides the ethical and practical implementation of automated coders in clinical settings.