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“TALABALAR TOMONIDAN AI YOZMA FIKRLARIGA MUNOSABAT: SINFDAGI
TAJRIBADAN OLINGAN XULOSALAR”
Isayeva Dildora Mirkhasil qizi
TIQXMMI Milliy universiteti
O‘qitish nazariyasi va metodikasi kafedrasi o‘qituvchisi
https://doi.org/10.5281/zenodo.16560650
Annotatsiya
. Sun’iy intellekt (AI) vositalari ta’lim muhitiga tobora chuqurroq kirib
borayotgan bir paytda, ularning yozuvni o‘rgatishdagi o‘rni ham ortib bormoqda. Ushbu
tadqiqot oliy ta’lim muassasasidagi akademik yozuv mashg‘ulotida SI tomonidan taqdim
etilgan fikr-mulohazalarga talabalar qanday munosabatda bo‘lishlarini o‘rganishga qaratilgan.
Oliy o‘quv yurtida o‘tkazilgan aralash uslubdagi (mixed-methods) tajriba asosida,
talabalarning SI fikrlariga nisbatan munosabati an’anaviy o‘qituvchi fikrlari bilan solishtirildi.
Ma’lumotlar tadbir oldi va keyingi so‘rovnomalar, guruhli muhokamalar va talabalar yozgan
matnlarning tahlili orqali yig‘ildi. Natijalar shuni ko‘rsatdiki, talabalar SI tomonidan berilgan
fikrlarning tezkorligi, izchilligi va batafsil tavsifini qadrlagan bo‘lsalar-da, uning noziklik,
kontekst va hissiy ohangni tushunishdagi cheklovlariga ham e’tibor qaratdilar. Ayniqsa,
talabalar SI’ni insoniy ta’limni to‘liq almashtiruvchi emas, balki uni to‘ldiruvchi vosita sifatida
ko‘rishlarini bildirdilar. Ushbu tadqiqot yozma ta’limda SI fikrlarini samarali qo‘llash bo‘yicha
tushunchalar beradi hamda SI yordamchilari uchun muhim dizayn yondashuvlarini taklif
etadi.
Kalit so‘zlar:
Sun’iy intellekt, Yozuvdagi fikr-mulohaza, Talaba fikri, Ta’lim
texnologiyalari, Ta’limda SI, Sinf tajribasi, Yozuvni o‘rgatish, Inson-SI hamkorligi.
“STUDENT PERCEPTIONS OF AI FEEDBACK IN WRITING: INSIGHTS FROM A
CLASSROOM EXPERIMENT”
Isayeva Dildora Mirkhasil qizi
TIIAME National Research University
Teacher at the department of Teaching Theory and Methodology
dildoraisayeva99@gmail.com
Abstract.
As artificial intelligence (AI) tools become increasingly integrated into
educational settings, their role in writing instruction has garnered growing interest. This
study investigates student perceptions of AI-generated feedback on academic writing within a
higher education classroom. Through a mixed-methods classroom experiment involving
undergraduate students, we examined how learners responded to AI feedback compared to
traditional teacher feedback. Data were collected via pre- and post-intervention surveys, focus
group discussions, and analysis of students' writing revisions. The findings indicate that while
students appreciated the immediacy, consistency, and detailed nature of AI feedback, they
also expressed concerns regarding its limitations in understanding nuance, context, and
emotional tone. Notably, students viewed AI as a complementary tool rather than a
replacement for human instruction. The study provides insights into how AI feedback can be
effectively integrated into writing pedagogy and suggests design considerations for AI writing
assistants in educational contexts.
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Keywords:
Artificial Intelligence, Writing Feedback, Student Perception, Educational
Technology, AI in Education, Classroom Experiment, Writing Instruction, Human-AI
Collaboration.
Introduction
The integration of artificial intelligence (AI) into educational practices has rapidly
transformed the way learning is delivered and assessed. In the context of writing instruction,
AI-powered feedback tools—such as Grammarly, ChatGPT, Turnitin Revision Assistant, and
others—have emerged as popular resources for students and educators alike. These tools
provide instant responses to a range of writing elements, including grammar, punctuation,
organization, clarity, tone, and even content development. As these technologies evolve, they
are increasingly being positioned not just as supplementary aids but as central components of
writing pedagogy. However, despite their growing use, limited research exists on how
students perceive and interact with the feedback these systems provide, especially within
formal classroom settings.
Writing is a complex cognitive and social process that requires more than just surface-
level corrections. Effective feedback—whether formative or summative—plays a critical role
in helping students revise, reflect, and grow as writers. Traditional teacher feedback has long
been valued for its ability to address nuanced issues such as argument structure, audience
awareness, critical thinking, and voice. Yet, due to time constraints, heavy grading loads, and
increasing class sizes, educators often struggle to provide individualized, timely feedback to
all students. In contrast, AI tools offer immediate and consistent feedback, potentially
alleviating these challenges. Nevertheless, the quality, contextual appropriateness, and
pedagogical value of such feedback remain contested.
From a pedagogical perspective, the use of AI tools in writing classrooms raises
fundamental questions about student agency, trust in technology, and the evolving role of
instructors. Do students view AI feedback as authoritative? Do they understand its limitations
and strengths? How do they decide whether to accept or reject suggestions offered by a
machine? These questions are essential, particularly as education systems worldwide grapple
with the ethics, accessibility, and effectiveness of AI-driven learning technologies.
This study seeks to explore these questions by investigating student perceptions of AI-
generated feedback through a classroom-based experiment involving undergraduate learners.
By comparing student responses to AI feedback with their reactions to traditional teacher
feedback, the research aims to understand not only how students engage with automated
systems, but also how these systems influence writing practices, revision behavior, and
learning outcomes.
In recent years, several studies have begun to address the potential of AI in educational
settings. Some scholars highlight the efficiency and motivational aspects of AI tools, noting
that students often revise more frequently and independently when given immediate
feedback. Others caution against over-reliance on automation, citing the lack of contextual
awareness and emotional sensitivity in machine-generated responses. While much of the
existing literature focuses on technical evaluation and system design, fewer studies
investigate the student experience—particularly how learners interpret, value, and respond
to AI feedback in authentic classroom contexts.
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The central aim of this thesis is to fill that gap by focusing on the student voice. Through
a mixed-methods approach combining surveys, focus groups, and writing analysis, the
research addresses the following core questions:
-
How do students perceive the usefulness, accuracy, and clarity of AI-generated feedback
on their writing?
-
How does student engagement with AI feedback compare to traditional teacher
feedback in terms of revision decisions?
-
What do students identify as the strengths and limitations of using AI feedback tools in
their writing process?
By addressing these questions, the study contributes to the broader discourse on
human-AI collaboration in education. It offers insights for educators aiming to integrate AI
into writing instruction thoughtfully, as well as for developers seeking to improve the design
of AI tools to better support learning. Most importantly, it emphasizes the importance of
student-centered perspectives in shaping the future of educational technology.
In the chapters that follow, the thesis begins with a review of existing literature on
feedback in writing pedagogy and AI applications in education. It then outlines the
methodology of the classroom experiment, including participant selection, tools used, and
data collection methods. The results section presents findings from both quantitative and
qualitative analyses, followed by a discussion that interprets these results in light of existing
research. The final chapter offers conclusions, pedagogical recommendations, and directions
for future research.
References:
Используемая литература:
Foydalanilgan adabiyotlar:
1.
Biber, D., Conrad, S., & Reppen, R. (2021). Corpus linguistics and grammar: Investigating
English grammar patterns across time and space. Cambridge University Press.
2.
Cotos, E. (2014). Automated writing evaluation for formative assessment of L2 writing.
TESOL Quarterly, 48(2), 400–427. https://doi.org/10.1002/tesq.117
3.
Liu, M., & Zhang, L. J. (2021). Writing with an AI companion: College students’
perceptions of Grammarly as a writing tool. Computers and Composition, 62, 102673.
https://doi.org/10.1016/j.compcom.2021.102673
4.
Warschauer, M., & Grimes, D. (2008). Automated writing assessment in the classroom.
Pedagogies:
An
International
Journal,
3(1),
22–36.
https://doi.org/10.1080/15544800701771580
5.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of
research on artificial intelligence applications in higher education – where are the educators?
International Journal of Educational Technology in Higher Education, 16(1), 1–27.
https://doi.org/10.1186/s41239-019-0171-0
