ArticleJMIR formative research2025
The Feasibility of Large Language Models in Verbal Comprehension Assessment: Mixed Methods Feasibility Study.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Metric Quality of Intelligence Tests in Spain: Evidence from 13 National Test Commission Reviews.Journal of Intelligence · 2026Article
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Authors and funding
5 authors.
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Abstract
backgroundCognitive assessment is an important component of applied psychology, but limited access and high costs make these evaluations challenging.
objectiveThis study aimed to examine the feasibility of using large language models (LLMs) to create personalized artificial intelligence-based verbal comprehension tests (AI-BVCTs) for assessing verbal intelligence, in contrast with traditional assessment methods based on standardized norms.
methodsWe used a within-participants design, comparing scores obtained from AI-BVCTs with those from the Wechsler Adult Intelligence Scale (WAIS-III) verbal comprehension index (VCI). In total, 8 Hebrew-speaking participants completed both the VCI and AI-BVCT, the latter being generated using the LLM Claude.
resultsThe concordance correlation coefficient (CCC) demonstrated strong agreement between AI-BVCT and VCI scores (Claude: CCC=.75, 90% CI 0.266-0.933; GPT-4: CCC=.73, 90% CI 0.170-0.935). Pearson correlations further supported these findings, showing strong associations between VCI and AI-BVCT scores (Claude: r=.84, P<.001; GPT-4: r=.77, P=.02). No statistically significant differences were found between AI-BVCT and VCI scores (P>.05).
conclusionsThese findings support the potential of LLMs to assess verbal intelligence. The study attests to the promise of AI-based cognitive tests in increasing the accessibility and affordability of assessment processes, enabling personalized testing. The research also raises ethical concerns regarding privacy and overreliance on AI in clinical work. Further research with larger and more diverse samples is needed to establish the validity and reliability of this approach and develop more accurate scoring procedures.
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