Keywords
AI chatbot
EFL
A2 learners
foreign language anxiety
ChatGPT
CALL
affective filter
willingness to communicate
How to Cite
Abstract
The rapid expansion of artificial intelligence technologies has introduced novel possibilities for foreign language pedagogy, particularly in the domain of oral communication. Among the most persistent challenges encountered by English as a Foreign Language (EFL) learners at the elementary proficiency level — classified as A2 on the Common European Framework of Reference — is foreign language speaking anxiety (FLSA), a phenomenon that significantly impedes learners' willingness to communicate and their capacity to produce spontaneous oral output. This paper investigates the specific mechanisms through which AI- powered chatbot tools may contribute to alleviating speaking anxiety among A2-level EFL learners. Drawing on Krashen's Affective Filter Hypothesis (1982), Horwitz, Horwitz and Cope's conceptualization of FLSA (1986), and MacIntyre and Gardner's anxiety-performance framework (1994), the paper argues that AI chatbots offer a structurally unique communicative environment — one devoid of negative social evaluation — that is particularly suited to the psychological needs of anxious, low-proficiency learners. A critical review of recent empirical literature (2022–2025) demonstrates that learners who engage in sustained chatbot-based speaking practice report significantly lower levels of communication apprehension and greater willingness to engage in English conversation. The paper concludes by proposing practical frameworks for integrating AI chatbot tools into EFL speaking instruction within the Uzbek educational context, where structural classroom constraints compound the challenges faced by A2 learners.References
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