AI-generated level-aligned pre-writing input and EFL reading-to-write development in a Jordanian university context: a longitudinal mixed-methods study

Reading-to-write tasks require EFL learners to comprehend and integrate academic source material into original texts. This longitudinal mixed-methods study examined whether AI-generated, level-aligned input during preparation supported performance among 134 undergraduates in two intact academic writing sections at a Jordanian university. Sections received either AI-mediated input or instructor-scaffolded preparation; AI was prohibited during assessed writing. Performance at three time points was analyzed using repeated-measures ANOVA, alongside reflections, think-aloud protocols, and interviews. Both groups improved, but the AI group gained more, especially in content integration (Group × Time: partial η² = .177; posttest d = 1.58). Learners described benefits for comprehension, synthesis, and planning. However, section-level allocation and potentially unequal synthesis support limit causal inference. The findings position AI as a constrained pre-writing input mediator rather than a drafting tool.