Авторы

  • Saparov Bobur,Rakhimov Murodullo,Sokhibov Kholruzi,Khalmuratova Zebo,Sultonova Husnora
    Tashkent Instıtute of Chemıcal Technology

DOI:

https://doi.org/10.71337/inlibrary.uz.iqro.76509

Ключевые слова:

artificial intelligence personalized learning educational technologies SI platforms automatic assessment..

Аннотация

Nowadays, artificial intelligence (aı) has become one of the key technologies that is bringing about a comprehensive revolution in the field of education. this article analyzes the effectiveness of personalized learning platforms for students, their functions and advantages. ıt also provides detailed information about the technical infrastructure, operating principles and potential capabilities of personalized learning platforms using aı.


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JOURNAL OF IQRO – ЖУРНАЛ ИҚРО – IQRO JURNALI – volume 14, issue 02, 2025

ISSN: 2181-4341, IMPACT FACTOR ( RESEARCH BIB ) – 7,245, SJIF – 5,431

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Saparov Bobur

Assistant of the Department of Engineering Graphics and Mechanics

Rakhimov Murodullo

Doctor of Philosophy in Technical Sciences, Associate Professor, Department of Engineering

Graphics and Mechanics

Sokhibov Kholruzi

Trainee teacher of the Department of Automation and Digital Control

Tashkent Instıtute of Chemıcal Technology

Khalmuratova Zebo

"Science and Development" State Enterprise

2nd year basic doctoral student

Sultonova Husnora

Student of group 22-17

A NEW GENERATION OF PERSONALIZED LEARNING FOR STUDENTS:

EDUCATIONAL INNOVATIONS WITH THE HELP OF ARTIFICIAL

INTELLIGENCE

Abstract:

Nowadays, artificial intelligence (aı) has become one of the key technologies that is

bringing about a comprehensive revolution in the field of education. this article analyzes the

effectiveness of personalized learning platforms for students, their functions and advantages. ıt

also provides detailed information about the technical infrastructure, operating principles and

potential capabilities of personalized learning platforms using aı.

Keywords:

artificial intelligence, personalized learning, educational technologies, SI platforms,

automatic assessment..

1. Introduction

The education sector has always sought to use technological advances to introduce innovative

approaches. Personalized learning involves adapting the learning process to the needs, abilities,

and interests of each student. While in the traditional system this process was slow due to the

limited time and resources of teachers, with the help of AI this process is becoming much faster

and more efficient.

2. The concept of personalized learning

Personalized learning is a form of education that is

tailored to each student based on individual data to create a unique learning process. In this

process, SI algorithms are used to analyze the student's knowledge, successes, and difficulties,

after which a personal learning plan is developed.

3. The role of artificial intelligence AI

is actively used in education in the following areas:

Data Analysis:

Analyze large databases to determine student knowledge levels and

academic success.

Automatic grading:

Automatically grade essays, tests, and other written work.

Recommendation systems:

Suggesting materials based on the student's interests.

Gamification:

Motivating students by integrating game elements into learning.


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JOURNAL OF IQRO – ЖУРНАЛ ИҚРО – IQRO JURNALI – volume 14, issue 02, 2025

ISSN: 2181-4341, IMPACT FACTOR ( RESEARCH BIB ) – 7,245, SJIF – 5,431

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ILMIY METODIK JURNAL

4. Technical infrastructure and technologies

Personalized learning platforms operate on the

following technologies::

Algorithms:

Machine learning algorithms (e.g. TensorFlow, PyTorch) to analyze student

data and create personalized content.

Database:

SQL or NoSQL databases to store data related to student learning

.

Natural Language Processing (NLP):

Creating opportunities to answer students' written

and oral questions.

Cloud technologies:

Services such as AWS, Google Cloud to ensure platform scalability.

5. Benefits of personalized learning

Increased efficiency:

Students receive education tailored to their abilities, which improves

outcomes.

Flexibility:

The platform adapts to each student's learning pace and style.

Support:

Students have the opportunity to consolidate their knowledge without the need for

teacher assistance.

Cost-effectiveness:

Compared to the traditional system, these platforms save teachers time.

6. Implementation difficulties

Data Security:

Ensuring the protection of students' personal data.

Technological infrastructure requirements:

High-level technical resources are required to

implement SI-based platforms.

Teacher training:

Special training is needed to explain the use of new technologies.

7. Future prospects

Personalized learning platforms using AI can radically change the education sector. This process

is expected to make education more comprehensive, interesting and effective. In the future,

developments can be observed in the following areas:

Studying educational materials through voice assistants.

Integrate virtual and augmented reality technologies.

Automatic translation of educational materials in different languages ​ ​ ​ ​ of the world.

8. Conclusion

Artificial intelligence-based personalized learning platforms play a key role in improving the

quality of education and creating an individual learning experience for students. With the help of

these platforms, students receive education in a way that suits their abilities, which helps them

increase the efficiency of learning, learn new knowledge faster, and achieve their goals. It also

saves time for teachers and makes it easier to manage the educational process. The capabilities of

the platforms serve not only to improve the existing system, but also to improve the quality of

global education by introducing new technologies into education. With the help of AI, new

horizons are opening up for making the learning process interactive and interesting, enhancing an

individual approach, and effectively using educational resources. Therefore, personalized

learning platforms are expected to be at the center of major technological changes in the field of

education in the near future.

References


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JOURNAL OF IQRO – ЖУРНАЛ ИҚРО – IQRO JURNALI – volume 14, issue 02, 2025

ISSN: 2181-4341, IMPACT FACTOR ( RESEARCH BIB ) – 7,245, SJIF – 5,431

www.wordlyknowledge.uz

ILMIY METODIK JURNAL

1. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An

Argument for AI in Education. Pearson Education.

2. Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and Theory of Artificial

Intelligence in Education: An Overview. International Journal of Educational Technology in

Higher Education, 17(1), 1-20.

3. Woolf, B. P. (2010). Building Intelligent Interactive Tutors: Student-centered Strategies for

Revolutionizing E-learning. Morgan Kaufmann.

4. Baker, R. S., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In J.

A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 61-75).

Springer.

5. Zhang, K., & Aslan, S. (2021). AI-driven Personalized Learning Systems: Opportunities and

Challenges. Journal of Artificial Intelligence in Education, 31(2), 153-173.

Библиографические ссылки

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and Theory of Artificial Intelligence in Education: An Overview. International Journal of Educational Technology in Higher Education, 17(1), 1-20.

Woolf, B. P. (2010). Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning. Morgan Kaufmann.

Baker, R. S., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In J. A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 61-75). Springer.

Zhang, K., & Aslan, S. (2021). AI-driven Personalized Learning Systems: Opportunities and Challenges. Journal of Artificial Intelligence in Education, 31(2), 153-173.