Funding

The study was supported by the scholarship program of the President of the Russian Federation for postgraduate students and adjuncts enrolled in full-time education in Russian educational institutions conducting scientific research within the framework of the priorities of scientific and technological development of the Russian Federation.

Introduction

Currently, artificial intelligence (AI) is rapidly penetrating all areas of society, including education. The implementation of such technologies opens new opportunities for personalizing and improving the efficiency of the learning process. As noted by D. Gašević and colleagues, although research on AI applications in education has been conducted for a long time, the emergence and active development of systems such as ChatGPT and DALL-E have triggered a new wave of discussion within the pedagogical community.

According to M. Chassignol and colleagues, artificial intelligence can influence different components of the educational process: content, activities, outcomes, and communication between participants. A particular case of AI in education is represented by specialized educational systems. In the communicative context, AI development has progressed from early computer-based learning systems, which could provide feedback only after a final answer was submitted and could not offer intermediate hints, to intelligent tutoring systems (ITS).

Modern ITS are capable of solving tasks based on knowledge bases, automating some teacher functions such as task generation and feedback organization, and responding step by step during the student’s problem-solving process. Examples include CodeCombat, where programming is taught through an increasingly complex interactive game, and Duolingo, where AI is used for natural language processing and assisting users in practicing communication and identifying speech errors.

To assess academic performance or identify learning difficulties among students, Educational Data Mining techniques may be used. These methods consider not only academic performance but also students’ psychological characteristics. One of the key approaches enabling instruction based on the individual characteristics of each student—including learning style, academic achievement, interests, and health conditions—is personalization, where AI can significantly expand and automate the functionality of educational tools.

Digital technologies have already substantially transformed approaches to teaching and learning, information search and processing, and interpersonal communication. The emergence of numerous AI tools has expanded opportunities for the modernization of the educational sphere.

The purpose of this study is to systematize knowledge about the use of AI in education and identify the main trends, challenges, and prospects for its application. The study of AI applications in education is a relatively new area for both researchers and teachers, while AI technologies are gradually being integrated into key areas such as personalization and learning support for students, teaching assistance for educators, assessment, and administration.

Methodology

The literature review was conducted using leading scientific databases, including Web of Science, Scopus, ScienceDirect, Google Scholar, and eLibrary. Various combinations of keywords related to artificial intelligence and education were used in both Russian and English, including: education, educational technologies, artificial intelligence, AI in education, machine learning, AI literacy, intelligent tutoring systems, human-centered artificial intelligence, personalized education, natural language processing, automated assessment, AI for teaching and learning, management of educational pathways, and educational content generation.

Studies published between 2014 and 2024 were reviewed.

The following inclusion criteria were established:

— empirical and theoretical studies, as well as literature reviews;

— focus on the application of AI in educational contexts;

— publication in peer-reviewed journals or conference proceedings;

— publication in English or Russian.

The initial search identified more than 300 articles. After excluding studies that did not meet the criteria and removing duplicates, 19 articles remained in the final review.

Five main thematic areas were identified in the selected studies:

1. Personalized learning using AI;
2. Intelligent assessment systems;
3. AI in educational management;
4. Development of AI competencies among teachers and students;
5. Ethical aspects of AI application in education.

Application of Artificial Intelligence in Education

1. Personalized Learning Systems

One of the most promising areas of AI application in education is the development of personalized learning systems. These systems can adapt the educational process to the individual needs of each student. Such an approach makes it possible to optimize learning and improve educational outcomes.

The key operating principles of such systems include:

— analysis of student performance and behavior data to create an individual learning profile;

— adaptation of educational content and tasks according to the student’s level of knowledge and learning pace;

— provision of personalized recommendations for more effective learning;

— automated feedback and assessment.

The effectiveness of such systems has been discussed in numerous studies. A. Bhutoria notes that AI is successfully used to meet individual learning needs, account for students’ abilities and characteristics, and create optimized educational trajectories. AI makes it possible to expand and adapt educational content to specific learners and identify difficult topics in advance.

K. Karrenbauer and colleagues describe the development of an individual digital assistant for university students capable of recommending lectures based on students’ interests and competencies, as well as analyzing and providing information about their strengths and weaknesses in learning strategies. Such assistants rely on extensive data, including academic reports, completed modules, and students’ self-assessments.

Adaptive systems also make it possible to predict student performance. E. Costa and colleagues studied the effectiveness of AI-based educational data mining methods for predicting academic performance. The results showed that these methods can identify students who may face learning difficulties at an early stage and provide timely support.

L. Ph. Xuan and colleagues developed a model capable of predicting academic performance based on data from an interactive distance learning platform, including engagement during classes, time spent on assignments, and progress in other learning activities. The authors note that integrating predictive systems into educational environments can transform teaching methods and create a more informed and engaging learning environment.

Research literature most frequently presents examples of practical AI applications for personalized education in foreign language teaching. The specifics of language learning require constant practice in writing, reading, and speaking skills. AI allows these processes to be partially delegated and automated.

A study on teaching English using an educational platform with natural language processing and machine learning functions identified three major advantages:

1. Improvement in academic performance and speaking skills through regular writing and speaking practice with a chatbot capable of maintaining dialogue and correcting mistakes;
2. Significant reduction in preparation time for teachers and students due to AI-assisted selection of educational tasks;
3. Increased student motivation through performance prediction and adaptive tasks.

Thus, personalized learning systems demonstrate several advantages in educational practice. They allow educators to:

1. Consider the student’s current level of preparation and continuously adapt to it;
2. Reduce the time required to study material through adaptive tasks;
3. Provide students with opportunities to practice at a comfortable pace and receive explanations regarding their solutions.

As a result, the use of such systems can improve understanding of studied topics and reduce stress associated with learning complex material.

2. Intelligent Assessment Systems

Intelligent assessment systems, in which machine learning algorithms are used for automated grading, possess several advantages: they ensure objectivity, provide rapid results, and process large volumes of data.

Currently, automated processing of multiple-choice tests does not present significant difficulties. However, tasks requiring written or graphical responses—which better evaluate students’ knowledge—create a substantial assessment burden for both humans and intelligent systems.

L. Tyack and colleagues studied the application of neural networks for automated scoring of graphical response tasks for 4th and 8th grade students. The accuracy of AI-generated scores corresponded to human scoring accuracy and, in some tasks, even surpassed human assessment results.

C. Ormerod and colleagues investigated the effectiveness of a system developed for assessing short written responses to mathematics and English tasks in grades 3–8 and 11. The system demonstrated high assessment accuracy in both subjects, and for English tasks its accuracy exceeded that of human experts.

In addition to correctness, the system also analyzes confidence in its own evaluation. If the system determines that a score may be inaccurate, the work is forwarded to a teacher for review.

Nevertheless, the use of AI for student assessment raises concerns among researchers. Scientists point to problems such as lack of human interaction, insufficient creativity in task design and assessment, difficulties in understanding context, the need to provide reliable preliminary data, and the possibility of system failures and logical errors.

Despite the significant potential of AI in this field, further research is necessary to overcome these contradictions.

3. Automation of Administrative Tasks in Educational Institutions Using AI

Artificial intelligence is increasingly applied to various administrative processes in educational institutions, including class scheduling, data analysis for decision-making, and the provision of personalized services.

As AI technologies continue to develop and expand their capabilities, demand for their use is also increasing. One-third of surveyed university teachers reported using AI to facilitate and optimize administrative tasks.

Delegating routine tasks to AI can assist in preparing accreditation documents, supporting student recruitment and admissions, and improving communication between administration and students.

For example, chatbots can provide students with answers regarding admissions, tuition payment, events, and other questions. This both accelerates and simplifies access to information for students and reduces the workload for administrative staff.

4. The Impact of Artificial Intelligence on Participants in the Educational Process

The integration of AI technologies significantly transforms the role of teachers in education. Educators increasingly act as coordinators of the learning process rather than solely as sources of information.

D. Lee and colleagues note that nearly half of the participants in their study used AI in teaching practice, most often to transform grading processes.

The development of AI also enables new formats of pedagogical interaction, such as immersive technologies and virtual characters that imitate human behavior and appearance to create interactive personalized learning environments.

At the same time, despite the growing role of AI, the human factor remains critically important in education. Researchers emphasize that AI cannot replace live communication between teacher and student, the real educational process, or become a substitute for mentors and research supervisors.

A balanced and thoughtful approach to AI implementation is necessary, where technologies complement rather than replace teachers and are aimed at improving the quality and accessibility of education.

In addition to transforming the teacher’s role, AI also affects students. The rapid development of technology requires the formation of corresponding competencies among children and young people.

The accessibility and popularity of AI technologies allow even children to use them. However, students often do not understand the basic principles behind such technologies, including the possibility that AI may generate incorrect information that requires verification.

Therefore, it is important to develop AI literacy from an early age in order to cultivate both effective AI usage skills and critical attitudes toward AI-generated results.

Researchers also note that AI use in education contributes to the development of digital culture, critical thinking, and creativity among students, while stimulating their professional potential through interaction with technologies such as big data and the Internet of Things.

Challenges of Implementing AI in Education

The implementation of AI in education raises several important issues.

Given the increasing popularity of AI technologies in personalized education, technical limitations must be considered. Personalization and feedback require continuous processing of large amounts of information, which demands significant computing power.

In addition to technical limitations, the collection and analysis of large amounts of students’ personal data raise concerns regarding confidentiality and privacy.

At the same time, there is a need to create open datasets for training and testing new AI models. Therefore, reliable information protection methods and clear rules for data collection, storage, and use are essential.

Another challenge is the insufficient level of AI literacy among teachers. In a survey of 194 educators, a significant proportion reported low levels of AI knowledge.

However, approximately half of the respondents expressed willingness to devote time to studying and using AI technologies in order to support students in the learning process.

Teachers recognize the risks associated with integrating AI into education, yet they remain ready to adopt such technologies because the potential benefits outweigh possible threats.

Researchers emphasize that professional development programs in AI should adopt comprehensive approaches that combine training with discussion of real-life cases.

There is also a shortage of regulatory documents and methodological materials that could assist teachers in integrating AI technologies into educational practice.

It is necessary to rethink teaching methods in light of the interaction between humans and artificial intelligence and identify the most effective approaches that take into account both the advantages and disadvantages of AI.

Conclusion

The literature analysis revealed several key directions in the development of AI applications in education.

One of the most promising areas is the personalization of learning. AI makes it possible to adapt the educational process to the abilities of each student. Research demonstrates significant progress in the development and implementation of adaptive learning systems capable of considering individual learning pace and style.

Automation of routine tasks is another important trend, where AI can take over many administrative and assessment functions, freeing teachers’ time for more creative work.

AI-based systems for automated assignment grading and academic performance monitoring are actively being developed.

Finally, there is an increasing need to develop skills for interacting with AI systems among both students and teachers, as well as to cultivate critical attitudes toward them, emphasizing the importance of AI literacy.

Thus, artificial intelligence is becoming an increasingly significant factor in the transformation of educational systems. Research shows that AI can improve learning efficiency and make education more accessible. However, its implementation must take ethical aspects into account while preserving the central role of humans in the educational process.

References:

1. Bogachevsky, V.M., & Kuprina, T.V. (2024). The Use of ChatGPT in Teaching English Grammar. Business. Education. Law, 2(67), 306–313.
2. Kotlyarova, I.O. (2022). Artificial Intelligence Technologies in Education. Bulletin of South Ural State University. Series: Education. Educational Sciences, 14(3), 69–82.
3. Rezaev, A.V., & Tregubova, N.D. (2023). ChatGPT and Artificial Intelligence in Universities: What Future Should We Expect? Higher Education in Russia, 32(6), 19–37.
4. Starodubtsev, V.A., & Neradovskaya, O.R. (2024). Artificial Intelligence and Immersive Technologies in Higher Pedagogical Education. Open Education, 28(2), 13–23.
5. Sun, Y. (2023). Research on the Application of Artificial Intelligence in Teaching English at Chinese Universities. Scientific and Methodological Electronic Journal “Concept”, 12, 88–103.
6. Titova, S.V. (2024). Intelligent Tutoring Systems for Personalization and Adaptation of Language Courses. Bulletin of Moscow University. Series 19: Linguistics and Intercultural Communication, 27(4), 84–99.
7. Shobonov, N.A., Bulaeva, M.N., & Zinovieva, S. (2023). Artificial Intelligence in Education. Problems of Modern Pedagogical Education, 79(4), 288–290.
8. Chassignol, M., et al. (2018). Artificial Intelligence Trends in Education: A Narrative Overview. Procedia Computer Science, 136, 16–24.
9. Ormerod, C., et al. (2023). Automated Short Answer Scoring Using an Ensemble of Neural Networks and Latent Semantic Analysis Classifiers. International Journal of Artificial Intelligence in Education, 33, 467–496.
10. Baidoo-Anu, D., & Ansah, L.O. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 7(1), 52–62.
11. Beege, M., Hug, C., & Nerb, J. (2024). AI in STEM Education: The Relationship Between Teacher Perceptions and ChatGPT Use. Computers in Human Behavior Reports, 16, 100494.
12. Bhutoria, A. (2022). Personalized Education and Artificial Intelligence in the United States, China, and India: A Systematic Review Using a Human-In-The-Loop Model. Computers and Education: Artificial Intelligence, 3, 100068.
13. Karrenbauer, C., et al. (2023). Design, Development, and Evaluation of an Individual Digital Study Assistant for Higher Education Students. Educational Technology Research and Development, 71(5), 2047–2071.
14. DiCerbo, K. (2020). Assessment for Learning with Diverse Learners in a Digital World. Educational Measurement: Issues and Practice, 39(3), 90–93.
15. Du, J., & Daniel, B.K. (2024). Transforming Language Education: A Systematic Review of AI-Powered Chatbots for English as a Foreign Language Speaking Practice. Computers and Education: Artificial Intelligence, 6, 100230.
16. Xuan, L.Ph., et al. (2024). Enhancing Educational Evaluation Through Predictive Student Assessment Modeling. Computers and Education: Artificial Intelligence, 6, 100244.
17. Ding, A.-C.E., et al. (2024). Enhancing Teacher AI Literacy and Integration Through Different Types of Cases in Teacher Professional Development. Computers and Education Open, 6, 100178.
18. Costa, E.B., et al. (2017). Evaluating the Effectiveness of Educational Data Mining Techniques for Early Prediction of Students’ Academic Failure in Introductory Programming Courses. Computers in Human Behavior, 73, 247–256.
19. Gašević, D., Siemens, G., & Sadiq, S. (2023). Empowering Learners for the Age of Artificial Intelligence. Computers and Education: Artificial Intelligence, 4(4), 100130.
20. Ma, W., et al. (2014). Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis. Journal of Educational Psychology, 106(4), 901–918.
21. Karram, O. (2021). The Role of Computer Games in Teaching Object-Oriented Programming in High Schools — CodeCombat as a Game Approach. WSEAS Transactions on Advances in Engineering Education, 18, 37–46.
22. König, C.M., Karrenbauer, C., & Breitner, M.H. (2024). Development Guidelines for Individual Digital Study Assistants in Higher Education. International Journal of Educational Technology in Higher Education, 21, 9.
23. Lin, C.-C., Huang, A., & Lu, O. (2023). Artificial Intelligence in Intelligent Tutoring Systems Toward Sustainable Education: A Systematic Review. Smart Learning Environments, 10(1), 41.
24. Memarian, B. & Doleck, T. (2024) A review of assessment for learning with artificial intelligence. Computers in Human Be havior: Artificial Humans. 2 (1), 100040. Available from: doi:10.1016/j.chbah.2023.100040.
25. Ormerod, C., Lottridge, S. & Harris, A.E. et al. (2023) Automated Short Answer Scoring Using an Ensemble of Neural Net works and Latent Semantic Analysis Classifiers. International Journal Artificial Intelligence in Education. 33, 467–496. Available from: doi:10.1007/s40593–022–00294–2.
26. Qi Xia, Chiu, T. C. T., Zhou, X., Chai, C.S. & Cheng, M. (2023) Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education Artificial Intelligence. 4, 100118. Available from: doi:10.1016/j.caeai.2022.100118.
27. Rezaev, A.V. & Tregubova, N.D. (2023) ChatGPT and AI in the Universities: An Introduction to the Near Future. Higher Education in Russia. 32 (6), 19–37. Available from: doi:10.31992/0869–3617–2023–32–6–19–37. (In Russian)
28. Shobonov, N.A., Bulaeva, M.N. & Zinovieva, S. (2023) Artificial Intelligence in Education. Problems of Modern Pedagogical Education. (79–4), 288–290. (In Russian)
29. Starodubtsev, V.A. & Neradovskaya, O.R. (2024) Artificial Intelligence and Immersive Technologies in Higher Pedagogical Education. Open Education. 28 (2), 13–23. Available from: doi:10.21686/1818–4243–2024–2–13–23. (In Russian)
30. Su, J., Tsz Kit Ng, D. & Kai Wah Chu, S. (2023) Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education: Artificial Intelligence. 4, 100124. Available from: doi:10.1016/j.caeai.2023.100124.
31. Sun, Y. (2023) Research of the usage of artificial intelligence in teaching English at a university in China. Scientific-Method ological Electronic Journal “Koncept”. (12), 88–103. Available from: doi:10.24412/2304–120X-2023–11128. (In Russian)
32. Titova, S.V. (2024) Intelligent learning systems for personalizing and adapting language courses. Lomonosov Linguistics and Intercultural Communication Journal. 27 (4), 84–99. (In Russian)
33. Tyack, L., Khorramdel, L. & von Davier, M. (2024) Using convolutional neural networks to automatically score eight TIMSS 2019 graphical response items. Computers and Education: Artificial Intelligence. 6, 100249. Available from: doi:10.1016/j.caeai.2024.100249.
34. Vandewaetere, M., Desmet, P. & Clarebout, G. (2011) The contribution of learner characteristics in the development of computer based adaptive learning environments. Computers in Human Behavior. 27 (1), 118–130. Available from: doi:10.1016/j.chb.2010.07.038.
35. VanLehn, K., Jordan, P.W. & Rosé, C.P. et al. (2002) The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing. In: Cerri, S.A., Gouardères, G. & Paraguaçu, F. (eds.) Intelligent Tutoring Systems. ITS 2002. Lecture Notes in Computer Science. Vol. 2363. Berlin, pp. 158–167. Available from: doi:10.1007/3–540–47987–2_20.
36. Xuan, L.Ph., Mai, Ph.Q.H., Nguyen, Q.H., Pham, T., Nguyen, T. H. H. & Nguyen, T.H. (2024) Enhancing educational evalua tion through predictive student assessment modeling. Computers and Education: Artificial Intelligence. 6, 100244. Available from: doi:10.1016/j.caeai.2024.100244.