Comparing AI-assisted and teacher-led reading strategy instruction in an EFL context: a quasi-experimental study

Abstract

Grounded in a metacognitive and distributed-scaffolding framework, this quasi-experimental study examined classroom-level patterns associated with two configurations of reading strategy instruction and with business-as-usual instruction in a university EFL context. Sixty undergraduate students enrolled in an advanced reading course at a public university in Jordan participated in the study. To preserve natural classroom composition, three intact course sections with 20 students each were assigned at the class level to one of three conditions: teacher-mediated AI-assisted strategy instruction, teacher-led strategy instruction, or business-as-usual instruction without explicit strategy training. The AI-assisted section used ChatGPT as a scaffold for previewing, predicting, monitoring, questioning, inferencing, and summarizing within a technology-equipped classroom and with one short weekly AI-supported task; the teacher-led section addressed the same strategies through instructor modeling and guided practice; the comparison section followed the regular course routine. Reading comprehension was measured with an adapted 20-item, 60-point test, and metacognitive awareness was measured with a study administration version of the Metacognitive Awareness of Reading Strategies Inventory. Descriptive statistics were the primary analytic lens. Student-level ANCOVA and t -test results are reported as exploratory summaries because each condition was represented by a single intact section. The two explicit-strategy sections showed stronger reading-comprehension patterns than the business-as-usual section, and the AI-assisted section showed the highest adjusted posttest mean. For metacognitive awareness, both explicit-strategy sections improved from pretest to posttest, and the AI-assisted section showed the largest descriptive gain. The findings suggest that a teacher-managed AI-supported instructional package may extend explicit strategy instruction without displacing teacher judgment, but they should be interpreted as section-level comparative evidence rather than as isolated treatment effects. The study contributes a semester-long, classroom-level comparison in university EFL reading and clarifies how AI can be positioned as a complement to explicit strategy teaching.

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