Oral reading fluency is central to reading proficiency, yet its relationships with distinct cognitive domains remain unclear. This study investigated the associations between oral reading fluency, executive functions, and intelligence in schoolchildren.
Methods
Students from the 2nd to the 5th grades (n = 106) were assessed through a standardized neuropsychological protocol. Reading fluency was measured using an artificial intelligence-based automated speech recognition system. Executive functions were assessed with phonemic verbal fluency (FAS), Go/No-Go, the Trail Making Test, and visuospatial working memory (Odd One Out). Intelligence was estimated through the Vocabulary and Matrix Reasoning subtests of the WASI.
Results
Oral reading correlated with spontaneous flexibility (FAS; ρ = 0.317), inhibitory control errors (ρ = -0.396), reactive flexibility (TMT-B; ρ = -0.356), and working memory (ρ = 0.347; all p < .001). In an integrated regression adjusted for age and sex, inhibitory control (β = 0.327, p = .004) and working memory (β = 0.289, p = .005) were independent predictors, explaining 37.0% of the variance in words correct per minute. No associations emerged with intelligence.
Conclusions
Oral reading fluency depends not only on linguistic automaticity but also on executive mechanisms, particularly inhibitory control and working memory, involved in monitoring and regulating reading.
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AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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