Xorijiy lingvistika va lingvodidaktika
–
Зарубежная
лингвистика
и
лингводидактика
–
Foreign
Linguistics and Linguodidactics
Journal home page:
https://inscience.uz/index.php/foreign-linguistics
Towards cognitive skill development via generative AI:
possibility or pedagogical illusion?
Maftuna RUZIEVA
1
Navoiy State University
ARTICLE INFO
ABSTRACT
Article history:
Received May 2025
Received in revised form
15 May 2025
Accepted 25 June 2025
Available online
15 July 2025
Generative artificial intelligence (AI), especially large
language models (LLMs), has become one of the most
transformative yet debated innovations in education. Advocates
contend that AI can act as a strong cognitive scaffold, providing
personalized feedback and encouraging higher-order thinking.
Critics, however, warn about superficial engagement, cognitive
offloading, and algorithmic biases that could weaken deeper
learning. This paper explores whether generative AI can
genuinely support the development of cognitive skills in English
as a Foreign Language (EFL) education. Using Vygotsky’s
sociocultural theory and Bloom’s taxonomy, it investigates how
AI-facilitated Socratic dialogue, reflective learning logs, and
genre transformation tasks can promote analytical reasoning,
metacognitive awareness, and skill transfer. However, risks like
“illusory facilitation” and digital inequalities highlight the
importance of careful pedagogical integration. By incorporating
AI into reflective practices, critical questioning, and peer
collaboration, teachers can turn it from a passive tool into a
proactive partner in fostering independent, critical, and socially
responsible learners.
2181-3701
/©
2025 in Science LLC.
https://doi.org/10.47689/2181-3701-vol3-iss4-pp13-19
This is an open-access article under the Attribution 4.0 International
(CC BY 4.0) license (
https://creativecommons.org/licenses/by/4.0/deed.ru
Keywords:
Generative AI,
cognitive skills,
language education,
metacognition,
critical thinking,
reflective pedagogy,
cognitive transfer,
educational technology,
AI-mediated learning,
digital literacy.
Sun’iy intellekt yordamida kognitiv ko‘nikmalarni
rivojlantirish sari: imkoniyat yoki pedagogik illyuziya?
ANNOTATSIYA
Kalit so‘zlar
:
Sun’iy intellekt
Vositalari,
tafakkur ko‘nikmalari
,
So‘nggi yillarda sun’iy intellekt vositalari, xususan yirik til
modellari, ta’lim jarayonida innovatsion yondashuv sifatida
keng
muhokama
qilinmoqda.
Tadqiqotchilar
bunday
1
Doctoral student, Navoiy State University
Xorijiy lingvistika va lingvodidaktika
–
Зарубежная лингвистика
и лингводидактика
–
Foreign Linguistics and Linguodidactics
Issue
–3 № 4 (2025)
/ ISSN 2181-3701
14
chet tilini
o‘qitish
,
o‘z
-
o‘zini boshqarish
,
tanqidiy mulohaza,
tahliliy o‘qitish
,
bilimlarni yangi vaziyatlarda
tatbiq etish,
raqamli ta’lim
texnologiyalari,
tenglik masalalari.
texnologiyalarning
o‘quvchilarda
tahliliy
tafakkurni
rivojlantirish,
o‘z
fikrlash
jarayonini
nazorat
qilish
ko‘nikmalarini shakllantirish hamda o‘rgangan bilimlarni yangi
vaziyatlarda qo‘llash salohiyatini alohida ta’kidlamoqda.
Jumladan, sokratik suhbatlar, o‘quvchi tomonidan yuritiladigan
kuzatuv daftarlari va matn janrini o‘zgartirishga asosla
ngan
topshiriqlar sun’iy intellekt vositalari yordamida o‘quvchilarni
chuqur mulohaza yuritishga va yuqori darajadagi tafakkurni
rivojlantirishga undaydi. Shu bilan birga, yuzaki o‘zlashtirish,
aqliy yukni sun’iy tizimlarga o‘tkazib yuborish va
algoritmlardagi noxolislik kabi xavf-
xatarlar ham mavjud bo‘lib,
bu texnologiyalarni o‘qitish jarayoniga puxta va tanqidiy
yondashuv asosida integratsiya qilish zarurligini ko‘rsatadi.
Ushbu maqolada sun’iy intellekt vositalarining imkoniyatlari
L.S. Vygotskiyning ijtimoiy-madaniy yondashuvi va B. Blum
tasnifi asosida tahlil qilinib, ularning ingliz tilini chet tili sifatida
o‘rgatishda tafakkur va o‘z
-
o‘zini boshqarish ko‘nikmalarini
rivojlantirishdagi o‘rni baholanadi. Maqolada sun’iy intellekt
vositalaridan ta’lim
jarayonida tanqidiy mulohaza, hamkorlik va
o‘z faoliyatini tahlil qilishga asoslangan pedagogik yondashuv
orqali samarali o‘quv muhitini yaratishda foydalanish
imkoniyatlarini asoslab berilgan.
К развитию когнитивных навыков с помощью
генеративного ИИ: возможность или педагогическая
иллюзия?
АННОТАЦИЯ
Ключевые слова:
Генеративный
искусственный интеллект,
когнитивные навыки,
обучение языкам,
метакогниция,
критическое мышление,
рефлексивная педагогика,
перенос знаний,
образовательные
технологии,
обучение с ИИ,
цифровая грамотность
.
Генеративный искусственный интеллект (ИИ), в
частности крупные языковые модели (Large Language Models,
LLMs), стал одним из самых преобразующих и одновременно
спорных нововведений в сфере образования. Сторонники
утверждают, что ИИ может выступать в
роли мощного
когнитивного каркаса, предоставляя персонализированную
обратную связь и способствуя развитию навыков высшего
порядка мышления. Однако критики предупреждают о риске
поверхностного вовлечения, когнитивного разгрузки и
алгоритмических предвзятостей, которые могут подорвать
процесс
глубокого
обучения.
В
данной
статье
рассматривается вопрос о том, может ли генеративный ИИ
действительно способствовать развитию когнитивных
навыков в обучении английскому языку как иностранному
(EFL). Основываясь на социокультурной теории Л.С.
Выготского и таксономии Блума, исследуется, каким образом
такие стратегии, как ИИ
-
опосредованный сократический
диалог, ведение рефлексивных учебных журналов и задания
по трансформации жанров, могут развивать аналитическое
мышление, метакогнитивную осознанность и перенос
Xorijiy lingvistika va lingvodidaktika
–
Зарубежная лингвистика
и лингводидактика
–
Foreign Linguistics and Linguodidactics
Issue
–3 № 4 (2025)
/ ISSN 2181-3701
15
навыков. Вместе с тем выявлены риски, включая
«иллюзорное содействие» и цифровое неравенство, что
подчеркивает необходимость тщательной педагогической
интеграции. Встраивая ИИ в практики рефлексии,
критического осмысления и совместного обучения в группах,
педагоги могут превратить его из пассивного инструмента в
активного партнера, способствующего формированию
автономных,
критически
мыслящих
и
социально
ответственных обучающихся
.
INTRODUCTION
The relationship between education and technology has long swung between
optimism and doubt. Each innovation
—
from filmstrips and language labs to MOOCs
—
has
sparked visions of transformation, only to reveal a sobering truth: tools alone do not
provoke deeper thinking. It is their integration into meaningful pedagogy that
determines their value. Today, generative AI stands at the center of this debate. With its
capacity for real-time feedback and personalized scaffolding, it is heralded as both a
powerful ally and a potential mirage. The central question is this: Can generative AI truly
cultivate the cognitive skills essential for deep learning, or does it merely simulate
intelligence without substance?
For decades, educators have wrestled with the question of whether technology
can foster intellectual and cognitive growth. From the tape recorders of the 1960s to the
intelligent tutoring systems of the 1990s, innovations have consistently promised to
revolutionize education but often failed to impact the deeper processes of thought. Now,
generative AI
—
particularly LLMs like GPT
—
has emerged as the latest technology to
inspire both enthusiasm and skepticism. Advocates argue these systems provide
individualized scaffolding, simulate intelligent dialogue, and empower learners to tackle
complex ideas in unprecedented ways. Yet concerns remain: does generative AI truly
support cognitive development, or does it offer only the illusion of understanding,
concealing rather than addressing conceptual gaps?
At the heart of this inquiry lies an essential question: what do we mean by
“cognitive skills” in langu
age education? These skills go beyond memorizing grammar
rules or vocabulary; they involve higher-order thinking such as analysis, synthesis,
evaluation, and metacognition
—the ability to monitor and regulate one’s thought
processes. Drawing on Vygotsky’s sociocultural theory and Bloom’s taxonomy, this paper
examines whether generative AI can effectively nurture such complex learning outcomes.
It considers both the theoretical potential and practical challenges of employing AI as a
cognitive partner in English language teaching and learning.
THEORETICAL FOUNDATIONS
Cognitive skills in language learning refer to learners’ capacity to think critically
and reflectively about how language works. These include analytical reasoning
(identifying patterns and evaluating linguistic choices), metacognition (awareness and
control over one’s thinking), and transfer (applying learned skills across different
contexts). Such abilities are vital for moving beyond rote memorization toward deeper
linguistic competence.
Vygotsky’s concept of the Zone of Proximal Development underscores the role of
scaffolding in helping learners advance from their current level of understanding to
Xorijiy lingvistika va lingvodidaktika
–
Зарубежная лингвистика
и лингводидактика
–
Foreign Linguistics and Linguodidactics
Issue
–3 № 4 (2025)
/ ISSN 2181-3701
16
greater mastery. Similarly, Bloom’s taxonomy emphasizes engaging learners in higher
-
order thinking tasks like analysis, evaluation, and creation. Together, these frameworks
provide a lens for assessing whether AI supports or undermines cognitive development.
Large language models like GPT-4 are trained on massive datasets and use
probabilistic algorithms to predict and generate text. While their outputs often appear
coherent and contextually appropriate, these systems do not “understand” language in
the human sense. This raises the risk that students may accept AI-generated suggestions
uncritically, mistaking them for authoritative insights. The challenge, then, lies in
positioning AI as a tool for reflection and dialogue
—
not as a substitute for human
thinking.
DISCUSSION
Emerging empirical research provides a nuanced understanding of how generative
AI can shape cognitive skill development in English language education, particularly for
learners of English as a foreign language (EFL). One notable study explored the use of AI-
mediated Socratic dialogue, where advanced EFL learners engaged with an AI that posed
probing, open-
ended questions such as, “Why might this word choice alter the tone?”
Over eight weeks, students exposed to this method demonstrated a measurable
improvement in their ability to justify linguistic decisions and to generate alternative
phrasings. This suggests that AI-facilitated inquiry can stimulate analytical reasoning and
encourage learners to approach language use more critically.
Reflective practices, such as learning logs, have also been leveraged to promote
metacognition in AI-assisted classrooms. In one intervention, students systematically
recorded their responses to AI-generated feedback, explicitly noting whether they
accepted, rejected, or modified suggestions and explaining their reasoning. When these
reflective logs were coupled with structured peer discussions, learners exhibited
heightened awareness of their cognitive processes and adopted more deliberate,
strategic approaches to revising their work. Compared to a control group, these students
displayed stronger metacognitive habits, which are key to developing self-regulated
learning skills.
Genre transformation tasks further illustrate AI’s potential in fostering cognitive
transfer. For instance, students were tasked with reworking AI-generated academic
abstracts into journalistic articles. When the AI offered annotated explanations
highlighting stylistic and rhetorical differences between genres, learners not only applied
conventions more effectively but also articulated their rationale for specific revisions.
This points to the value of embedding explicit reasoning into AI-supported activities to
promote higher-order thinking and the ability to transfer skills across contexts.
Yet, these promising outcomes are tempered by significant challenges. One critical
concern is the phenomenon of “illusory facilitation,” wherein generative AI produces text
that appears polished and convincing on the surface but lacks conceptual depth. Students
may accept such outputs at face value, missing vital opportunities for deeper engagement
and critical analysis. Cognitive offloading presents another risk: overreliance on AI can
lead learners to bypass the mental effort required for critical reasoning, potentially
stunting the development of durable cognitive pathways. Moreover, issues of equitable
access cannot be overlooked. Learners with limited digital literacy or restricted access to
advanced technologies may struggle to engage effectively with AI tools, exacerbating
existing educational disparities. Finally, biases embedded within AI training data risk
Xorijiy lingvistika va lingvodidaktika
–
Зарубежная лингвистика
и лингводидактика
–
Foreign Linguistics and Linguodidactics
Issue
–3 № 4 (2025)
/ ISSN 2181-3701
17
reinforcing narrow perspectives and perpetuating stereotypes, raising ethical questions
about the uncritical adoption of such technologies in diverse learning environments.
Taken together, these findings underscore the dual nature of generative AI in
language education: it holds immense potential as a cognitive scaffold but also carries
inherent risks that demand thoughtful, pedagogically informed integration.
PEDAGOGICAL RECOMMENDATIONS
To harness the potential of generative AI as a catalyst for cognitive growth
—
rather
than as a superficial shortcut
—
educators must adopt reflective and scaffolded
approaches that embed AI meaningfully into language learning. The goal should not be to
position AI as an all-knowing authority but to frame it as a provisional partner in inquiry
and dialogue.
First, teachers should design tasks and prompts that compel students to critically
engage with AI suggestions. For instance, rather than asking students to simply accept or
reject AI feedback, instructors can require learners to justify their choices in writing or
orally, articulating why they agree, disagree, or propose an alternative. This kind of
reflective justification fosters meta
cognitive awareness and strengthens learners’ ability
to evaluate language critically.
Second, modeling metacognitive strategies is essential. Educators can use “think
-
aloud” protocols when interacting with AI
-generated outputs, demonstrating how to
interrogate revisions, question underlying assumptions, and evaluate the
appropriateness of linguistic choices. By making their reasoning processes visible,
teachers help students internalize analytical habits they can apply independently.
Third, integrating peer collaboration into AI-supported activities can enhance
critical engagement. Structured group discussions in which students collaboratively
assess AI feedback, debate alternative solutions, and identify patterns across their texts
can deepen reflective thinking and promote a culture of shared inquiry. Such dialogues
also help mitigate overreliance on AI by situating its suggestions within a broader social
and cognitive context. Transfer tasks should also play a central role in curriculum design.
Activities that require learners to adapt AI-generated content for different genres,
audiences, or purposes
—
such as converting an academic essay into a blog post or a
speech
—
encourage cognitive flexibility and higher-order thinking. When paired with
explicit instruction on genre conventions and audience awareness, these tasks can equip
students with tools to critically navigate and reshape AI output.
Moreover, educators must address the risks highlighted in recent studies. To
counteract cognitive offloading, instructors can set limits on AI use during certain stages
of the writing process, ensuring that learners engage in unaided brainstorming, drafting,
or revising before consulting AI tools. Workshops on digital literacy and critical AI
awareness are also vital, especially for students from underserved backgrounds, to
ensure equitable access and informed use of technology.
Finally, it is imperative to acknowledge and address the potential biases inherent
in AI systems. Educators should foster critical conversations about how AI tools are
trained, whose voices are represented (or excluded) in datasets, and how algorithmic
outputs can reflect societal stereotypes. Such critical AI literacy equips students not only
to use generative tools effectively but also to question their limitations and ethical
implications.
Xorijiy lingvistika va lingvodidaktika
–
Зарубежная лингвистика
и лингводидактика
–
Foreign Linguistics and Linguodidactics
Issue
–3 № 4 (2025)
/ ISSN 2181-3701
18
CONCLUSION
“
The purpose of AI is not to replace humans, but to amplify human abilities.
”
This
insight from Fei-Fei Li encapsulates the transformative potential of generative AI in
English language education. Used thoughtfully, AI can scaffold analytical reasoning,
promote metacognition, and foster the transfer of skills across diverse communicative
contexts. It can act as a partner in inquiry, helping learners engage more deeply with
language and develop as autonomous, critical thinkers. Yet, realizing this potential
requires careful pedagogical design. Without explicit scaffolding, students risk cognitive
offloading, superficial engagement, and uncritical acceptance of AI outputs. Furthermore,
disparities in access and digital literacy demand that educators approach AI integration
with equity and inclusivity at the forefront. Ultimately, the challenge lies in leveraging
AI’s strengths while mitigating its limitations. By embedding it within re
flective,
collaborative, and ethically informed teaching practices, educators can ensure that AI
serves not as a crutch, but as a springboard for cultivating critical, adaptable, and socially
responsible language learners.
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Xorijiy lingvistika va lingvodidaktika
–
Зарубежная лингвистика
и лингводидактика
–
Foreign Linguistics and Linguodidactics
Issue
–3 № 4 (2025)
/ ISSN 2181-3701
19
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