Artificial intelligence (AI) has created new opportunities to generate instructional materials calibrated to learners’ proficiency levels; however, evidence on its use in extensive and intensive reading instruction remains limited. This mixed-methods quasi-experimental study examined the association between CEFR-calibrated AI-generated reading texts and EFL learners’ reading comprehension and attitudes. A total of 171 Saudi undergraduate EFL learners participated in a 13-week intervention and were assigned to one of three instructional conditions: Intensive Reading using C1-level texts (G1, n = 55), Extensive Reading using B2-level texts (G2, n = 58), or Extensive Reading using A2-level texts (G3, n = 58). AI-generated reading materials were independently validated using Text Inspector and the Pearson GSE Toolkit before implementation. Reading comprehension was assessed using common pre- and post-tests, and learner perceptions were measured using a post-intervention questionnaire. All three instructional conditions were associated with statistically significant improvements in reading comprehension over the intervention period. Among the three groups, the B2 Extensive Reading condition achieved the highest post-test reading comprehension scores, whereas the A2 Extensive Reading condition received the most favorable evaluations across the questionnaire dimensions. These findings suggest that, within the instructional configuration examined in this study, moderately challenging AI-generated texts were associated with stronger reading-comprehension outcomes, whereas less demanding texts were associated with more positive learner perceptions. The study extends current research by providing empirical evidence on the use of CEFR-calibrated AI-generated reading materials within extensive and intensive reading contexts and offers practical implications for integrating AI-supported reading instruction in EFL classrooms.
Alghamdi et al. (2026) studied this question.