Page 281 - XORIJIY TILLARNI O‘QITISH VA TARJIMA SOHASIDA SUN’IY INTELLEKTDAN SAMARALI FOYDALANISHNING ZAMONAVIY TENDENSIYALARI

METHODOLOGY AND LITERATURE REVIEW
The methodology of this research is grounded in a systematic literature review
and a theoretical analysis of existing pedagogical frameworks, excluding empirical
experiments or primary field data. The study utilizes a qualitative approach to
synthesize findings from academic journals, educational technology reports, and
linguistic theories published in Uzbek, Russian, and international English-language
sources. The selection criteria focused on peer-reviewed literature from 2018 to 2024
to ensure relevance to the current "Generative AI" era. The analysis involves
categorizing AI tools into functional groups: automated speech recognition (ASR),
chatbots based on Large Language Models (LLMs), and adaptive learning platforms.
Literature review reveals a consensus among scholars like Warschauer and Thorne,
who emphasize that digital tools facilitate "sociocognitive" development by
providing authentic contexts for language use [2]. Russian researchers such as T.N.
Lomteva highlight the importance of "individual educational trajectories" enabled by
AI, which allow students to progress at their own pace, a concept that aligns with
Vygotsky’s Zone of Proximal Development [3]. Furthermore, Uzbek scholars exploring
the digitization of education in Central Asia note that AI-driven interactivity is crucial
for overcoming the "language barrier" in regions where access to native speakers is
limited [4]. The review also incorporates the "Input Hypothesis" by Stephen Krashen,
examining how AI makes "comprehensible input" more accessible through real-time
translation and scaffolding. By evaluating these diverse sources, the methodology
ensures a multi-dimensional perspective on how AI serves as both a tool and a tutor
in the linguistic journey, focusing on the synthesis of existing knowledge rather than
new experimental data.

RESULTS AND DISCUSSION
The analysis indicates that the impact of Artificial Intelligence on language
learning is most profound in the realm of "immediate feedback loops," a critical
component of successful acquisition. Traditional methods often suffer from delayed
correction, which can reinforce linguistic errors; in contrast, AI-driven ASR
technologies provide instantaneous phonetic and grammatical feedback, allowing
for "micro-adjustments" in real-time. This interactive capability significantly reduces
the "affective filter"—the psychological barrier caused by anxiety or fear of public
failure—as learners feel more comfortable making mistakes in a private, digital
environment. Furthermore, the integration of LLMs like GPT-4 into language
platforms has shifted the focus from rote memorization to "negotiation of meaning."
Learners can now engage in open-ended conversations that mimic real-life
scenarios, such as job interviews or casual travel interactions, which enhances
pragmatic competence [5].
Discussion of these results suggests that AI is not merely a supplement but a
catalyst for a more "constructivist" learning environment where the student is an
active creator of their linguistic experience. However, a critical point of discussion is
the "cultural vacuum" of AI; while an algorithm can correct a verb tense, it often
struggles with the deep cultural connotations and idiomatic subtleties inherent in
human communication. International studies suggest that the most effective model
is a "hybrid" approach, where AI handles the mechanical, repetitive aspects of
language (vocabulary, syntax, pronunciation), while human instructors focus on 279
socio-cultural nuances and emotional intelligence [6]. The data synthesized from

II SHO‘BA:

Ta’lim jarayonida sun’iy intellekt texnologiyalarini joriy etishning nazariy
asoslari va konseptual yondashuvlari
https://www.asr-conference.com/