Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments
The paper investigates the reliability of user state classifications made by large language models in operational environments. It highlights the instability of individual score metrics, which complicates their use in real-time adaptive systems. The study proposes a framework for evaluating metric applicability, emphasizing the need for validation in AI design.
- ▪The study tested the psychometric reliability of AI measures across three large language models.
- ▪Only 31 out of 213 metrics met the reliability criteria for individual scores.
- ▪Individually unstable metrics can still be useful for post-hoc analysis of user interactions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15734 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.15734 (cs) [Submitted on 15 May 2026] Title:Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments Authors:Izabella Krzeminska, Michal Butkiewicz, Ewa Komkowska View a PDF of the paper titled Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments, by Izabella Krzeminska and 2 other authors View PDF Abstract:The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such assessment are stable and interpretable at the level of individual…
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