ADAPTIVE AI IN LANGUAGE LEARNING: PRINCIPLES AND ASSESSMENT CRITERIA
Keywords:
adaptive artificial intelligence, language skills, assessment criteria, personalization, validity, reliability, fairnessAbstract
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.
Downloads
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.
