Large language models predict cognition and education close to or better than genomics or expert assessment
This paper from Tobias Wolfram examines the ability to use different data from childhood to predict adult outcomes. Three predictors (a single essay, 22 teacher assessments, and DNA-based polygenic scores) were used to predict IQ, childhood/adolescent academic achievement, adult educational attainment, and adult non-cognitive traits.
LLMs were used to create predictor variables. Surprisingly, data from a single essay at age 11 (avg length = ~250 words) could predict up to 37-59% of variance in academic achievement (3rd image). When predicting IQ at age 11, the teacher's evaluation was the best single predictor (R2 = .62), but combining it with polygenic scores and the essay data, the explained variance rose to .70 (4th image). According to Wolfram, "The prediction of our best model approaches the test-retest reliability of benchmark intelligence tests" (p. 5).
This is an important step forward in using non-test data to predict IQ. While current LLMs do not surpass data based on a knowledgeable rate (e.g., a teacher), this paper points the way to using AI to understand people's psychological traits better.
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Posted Jul 29, 2026, 1:04 PM UTC