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Improving the Theoretical and Methodological Foundations for Integrating Generative Artificial Intelligence Technologies Into English Language Teaching in Higher Education

Authors: Nigmatova Nozimakhon Ulugbek kizi;

Improving the Theoretical and Methodological Foundations for Integrating Generative Artificial Intelligence Technologies Into English Language Teaching in Higher Education

Abstract

This article examines the theoretical foundations for developing methodologies aimed at integrating generativeartificial intelligence technologies into English language teaching in higher education. The study explores thelinguodidactic potential of AI-based educational tools, their functional role in the instructional process, and their effectivenessin enhancing students’ communicative competence. Furthermore, the paper systematically analyzes methodologicalapproaches, didactic principles, and pedagogical conditions necessary for the effective implementation of generativeartificial intelligence technologies in language education.

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green