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ZENODO
Published
2026-04-27
Section
Articles
How to Cite
How to Cite
ADAPTIVE AI IN LANGUAGE LEARNING: PRINCIPLES AND ASSESSMENT CRITERIA
Karimova Diyora
Tashkent University of Information Technologies named after Muhammad al-Khwarazmi Foreign Language Department
Keywords: adaptive artificial intelligence, language skills, assessment criteria, personalization, validity, reliability, fairness
Abstract
This article examines principles for integrating adaptive artificial intelligence into language education and proposes assessment criteria aligned with competency-based outcomes. Using comparative analysis, pedagogical modeling, and expert review, it clarifies how personalization, transparency, and feedback loops affect learning. Scientific novelty lies in a unified criteria framework connecting adaptive AI decisions to valid, reliable language-skill measurement.
References
1. Jalolov J. Chet til o‘qitish metodikasi: darslik. Toshkent: O‘qituvchi, 2012. 320 p.
2. Hoshimov O‘., Yoqubov I. Ingliz tili o‘qitish metodikasi: o‘quv qo‘llanma. Toshkent: Sharq, 2003. 256 b.
3. Avanesov V. S. Teoriya i praktika pedagogicheskikh izmereniy. Moskva: Pedagogika, 2002. 240 s.
4. Bim I. L. Metodika obucheniya inostrannym yazykam kak nauka i problemy shkol’nogo uchebnika. Moskva: Russkiy yazyk, 1977. 288 p.
5. Bachman L. F., Palmer A. S. Language Assessment in Practice: Developing Language Assessments and Justifying Their Use in the Real World. Oxford: Oxford University Press, 2010. 544 p.
6. Chapelle C. A., Enright M. K., Jamieson J. M. Building a Validity Argument for the Test of English as a Foreign Language. New York: Routledge, 2008. 272 p.
7. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019. 240 p.
