🤖 AI Summary
It remains unclear whether individual scores derived from large language models (LLMs) for inferring user states in operational settings exhibit psychometric stability and interpretability. This study introduces the first reproducible psychometric evaluation framework, integrating test–retest reliability and aggregate reliability analyses to systematically assess 213 user state indicators generated by multimodal LLMs—including GPT-4o audio, Gemini 2.0 Flash, and Gemini 2.5 Flash. Findings reveal that only 31 indicators meet established reliability criteria. Most individual scores demonstrate insufficient stability for real-time adaptation; however, they remain valuable in post-hoc analyses for uncovering patterns of user interaction and their associations with satisfaction, trust, and engagement.
📝 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 scores. This paper empirically tests this assumption, focusing on the psychometric reliability of artificial intelligence (AI) measures of user states.
This study employed replication evaluation procedures to assess the repeatability of a broad set of metrics across three different bimodal large language models (GPT-4o audio, Gemini 2.0 Flash, Gemini 2.5 Flash). Analyses include both individual score reliability and aggregated reliability, allowing us to distinguish metrics potentially useful for real-time adaptation from those that retain their value only in aggregated analyses.
The results demonstrate that metric reliability cannot be considered a default property in interpretive domains. The lack of stability at the level of individual scores precludes the interpretation of such scores as indicators of user state in real-time adaptive systems, even if these metrics demonstrate stability after aggregation. At the same time, the study indicates that individually unstable metrics can retain analytical utility in post-hoc studies, identifying rules governing interactions and their relationships with user experience parameters such as satisfaction, trust, and engagement.
The main contribution of this work, besides quantifying the severity of the problem (only 31 of 213 metrics met the criteria), is the proposal of a replicable evaluation framework, enabling measurable evaluations of metric applicability. This approach supports more responsible AI design of adaptive systems, in which the interpretation of results requires explicit validation of reliability and monitoring for violations over time.