USING ADAPTIVE ARTIFICIAL INTELLIGENCE TECHNOLOGIES TO DEVELOP UNIVERSITY STUDENTS’ ENGLISH LISTENING COMPREHENSION SKILLS (Eureka Journal of Language, Culture & Social Change, 2026)
The diversity of university students’ English proficiency makes uniform listening instruction insufficient for many learners. This article explores the use of adaptive artificial intelligence technologies for developing English listening comprehension skills in higher education. The research is designed as an analytical-methodological study that synthesises CEFR proficiency principles, metacognitive listening theory, responsible AI guidance and recent evidence from AI-supported EFL listening research. The main result is an adaptive listening cycle in which diagnostic evidence guides the selection of differentiated audio input, task complexity, scaffold availability, feedback and subsequent practice. The model defines adaptation across five variables: speech rate and audio length, lexical and syntactic complexity, task demand, transcript support and feedback type. It further provides a progression matrix for B1 to B2 listening work and a teacher-controlled implementation algorithm. The article argues that adaptive technology can reduce both overload and under-challenge, but only where adaptation is transparent, pedagogically justified and monitored by a teacher. The study offers a foundation for future experimental implementation in Uzbek university English courses.
Keywords: Adaptive artificial intelligence; listening comprehension; differentiated instruction; English language teaching; higher education; CEFR