Accepted for/Published in: JMIR Formative Research
Date Submitted: Nov 11, 2024
Date Accepted: Jan 5, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring the Potential of Large Language Models in Verbal Intelligence Assessment: A Preliminary Investigation
ABSTRACT
Background:
Cognitive assessment is an important component of applied psychology, but limited access and high costs make these evaluations challenging.
Objective:
This pilot study examined the feasibility of using large language models (LLMs) to create personalized AI-based verbal comprehension tests (AI-BVCTs) for assessing verbal intelligence, in contrast with traditional assessment methods based on standardized norms.
Methods:
We used a within-subject design, comparing scores obtained from AI-BVCTs with those from the Wechsler Adult Intelligence Scale (WAIS-III) Verbal Comprehension Index (VCI).
Results:
The concordance correlation coefficient (CCC) demonstrated strong agreement between AI-BVCT and VCI scores (Claude: CCC = .752, 90% CI [.266, .933]; GPT-4: CCC = .733, 90% CI [.170, .935]). Pearson correlations further supported these findings, showing strong associations between VCI and AI-BVCT scores (Claude: r = .844, p < .001; GPT-4: r = .771, p = .025). No statistically significant differences were found between AI-BVCT and VCI scores (p > .05). These findings support the potential of LLMs to assess verbal intelligence.
Conclusions:
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 over-reliance 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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