This study examines how AI-assisted text simplification influences the relationship between domain-specific vocabulary knowledge and reading comprehension in English for Medical Purposes (EMP). Based on data from 84 second-year medical students, the findings show high levels of vocabulary recognition (91%) and reading comprehension (84%), with a strong positive correlation between the two (r = .74). Compared to an earlier phase using original texts (r = .56), the results suggest that simplified texts may strengthen the alignment between lexical knowledge and comprehension. Transfer-efficiency results (M = 0.92) indicate that learners generally apply their vocabulary knowledge effectively in reading tasks, though with notable individual variation. The findings suggest that AI-assisted simplification does not necessarily increase overall comprehension, but may shift the balance of factors underlying reading by reducing non-lexical complexity and making vocabulary knowledge a more central predictor. This contributes to research on specialized L2 reading, lexical coverage, and the role of AI in medical ESP pedagogy. Note: this is a pre-print. The information will be updated when published.
Evgeni Stanchev (2026) studied this question.