AUTHENTICITY AND ACCOUNTABILITY IN EXTENSIVE READING PROGRAMS IN THE AGE OF ARTIFICIAL INTELLIGENCE

Abstract

Extensive Reading (ER) has long been recognized as one of the most effective approaches for improving learners’ reading fluency, vocabulary acquisition, motivation, and overall language proficiency in English as a Foreign Language (EFL) classrooms. During a two‑month ER project conducted with university students, learners demonstrated significant progress by reading stories, novellas, and even full novels according to their language proficiency levels. Students actively participated in book review activities and discussions, showing improved confidence and reading engagement. However, the rapid growth of Artificial Intelligence (AI) technologies such as ChatGPT has created new challenges for teachers attempting to evaluate whether students genuinely read the assigned texts or simply relied on AI‑generated summaries and online reviews. This article explores the tension between authentic extensive reading practices and AI‑assisted academic dishonesty. It discusses practical classroom observations, common indicators of genuine reading behavior, limitations of traditional assessment methods, and pedagogical solutions that encourage authentic learner engagement. The article also proposes a set of teacher‑friendly strategies including physical book verification, spontaneous questioning, reflective reading journals, oral interaction, vocabulary tracking, and process‑based assessment. The study argues that instead of rejecting technology completely, educators should redesign ER assessment systems to prioritize reading processes, personal responses, and critical engagement.