Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests

[Submitted on 25 Oct 2025 (v1), last revised 29 Jul 2026 (this version, v3)]

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Abstract:Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation. We propose a framework to measure consistent behavioral tendencies using situational judgment tests (SJTs), multidimensional item response theory (MIRT), and structured synthetic personas, treating responses as observations of latent behavioral variables.
Across large-scale SJT and persona datasets, we find that persona-conditioned behaviors are stable across runs, latent trait scores predict external benchmarks (e.g., TruthfulQA, EmoBench), and MIRT reveals consistent latent structure. We validate these results through human annotation, benchmark evaluation, and internal consistency analyses.
We interpret these traits not as human personality, but as stable behavioral tendencies expressed across contexts. Our results show that scenario-based psychometric evaluation provides a more reliable alternative to classical self-report approaches for assessing LLM behavior, and we release datasets to support further study.

Submission history

From: Amirali Abdullah [view email]
[v1] Sat, 25 Oct 2025 05:45:10 UTC (2,369 KB)
[v2] Sat, 9 May 2026 11:03:41 UTC (2,781 KB)
[v3] Wed, 29 Jul 2026 03:51:46 UTC (2,781 KB)