Generative Artificial Intelligence and Reading Comprehension Among College Students
DOI:
https://doi.org/10.5281/zenodo.22852031Abstract
The objective of this study was to determine the relationship between academic interaction with generative artificial intelligence (GAI) and reading comprehension among college students. A quantitative, observational, cross-sectional, and correlational study was conducted with 400 students over the age of 18 from a Peruvian public university, selected through purposive sampling based on accessibility. A 20-item scale measuring academic interaction with GAI and a reading comprehension test with 10 dichotomous indicators were administered. The internal structure of both overall scores was examined using exploratory factor analysis and parallel analysis. Internal consistency was adequate for the IAG (α = .858) and the reading test (KR-20 = .787). A strong positive association was identified between the total scores, rₛ = .782, 95% CI [.737, .819], p < .001. The relationship was stronger with inferential comprehension (rₛ = .720) than with literal comprehension (rₛ = .609) and critical comprehension (rₛ = .571). The six domains of IAG also showed positive associations with total reading scores. It was concluded that a more informed, frequent, and reflective academic engagement with IAG was associated with better reading performance. The cross-sectional design, non-probability sampling, and measurement limitations prevent the establishment of causality and restrict generalizability.
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