THE TEACHER–AI–STUDENT MODEL IN ESP AND AFL CLASSROOMS: A COMPARATIVE STUDY OF READING COMPREHENSION AT YEREVAN STATE UNIVERSITY
DOI:
https://doi.org/10.46991/jos.2026.29.1.11Keywords:
AI, reading comprehension, ESP, AFI, text comprehension, digital tools in education, Teacher–AI–Student TriangleAbstract
This study investigates the pedagogical implications of supervised artificial intelligence use on reading comprehension development within English for Specific Purposes and Arabic as a Foreign Language courses at Yerevan State University. Drawing on structural-typological distinctions between the two language systems, the paper addresses whether digitally mediated support enhances textual understanding without compromising learners' autonomous processing capacities.
A mixed-methods comparative design was employed, using an adapted UNESCO text on the Silk Roads as the primary instrument. The study involved two groups of 20 students each, with asymmetric proficiency levels, and analysed data through a quantitative comparison of comprehension task accuracy before and after AI use, alongside thematic coding of qualitative student reflections. Participants completed a staged sequence of tasks encompassing individual reading, comprehension assessment, difficulty reflection, AI-assisted review, and evaluative feedback. Findings indicate that while both cohorts engaged productively with digital tools, their patterns of use diverged considerably. ESP learners drew upon AI assistance chiefly for semantic clarification and conceptual interpretation, whereas AFL learners relied upon it predominantly for morphological analysis, diacritical vocalisation (tashkīl), and the resolution of script-induced structural ambiguities.
The study argues that AI functions most effectively as a didactic instrument when sustained teacher guidance remains central to the learning process. The proposed Teacher-AI-Student model offers a methodologically grounded framework for language instruction in higher education, one that cultivates critical engagement with texts while guarding against uncritical dependence on automated tools.
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