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Journal articles on the topic 'Personalized educational process'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Wang, Mingwen, and Yulin Wang. "Personalized Educational Process Models with Spacing Effect." International Journal of Hybrid Information Technology 9, no. 1 (2016): 153–62. http://dx.doi.org/10.14257/ijhit.2016.9.1.14.

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Nagaeva, I. A., A. B. Frolov, and I. A. Kuznetsov. "Mobile learning software in the educational process." Informatization and communication, no. 2 (February 16, 2021): 95–100. http://dx.doi.org/10.34219/2078-8320-2021-12-2-95-100.

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The relevance of the announced subject consists in identification of m-learning opportunities in educational process. The purpose of m-learning – to make educational process flexible, available and personalized. M-learning represents innovative means for new generation of trainees.

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Shvardak, M., and O. Popovych. "PERSONALIZED LEARNING USING ARTIFICIAL INTELLIGENCE." Scientific Herald of Sivershchyna. Series: Education. Social and Behavioural Sciences 2025, no. 1 (2025): 139–56. https://doi.org/10.32755/sjeducation.2025.01.139.

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The relevance of the topic lies in the need to modernize the educational process in accordance with the individual needs of education seekers. Artificial intelligence creates new opportunities for personalizing learning, which is a key direction in the development of modern education. The purpose of the article: to explore the possibilities of using artificial intelligence to personalize learning in today’s conditions. Methodology: analysis and synthesis of scientific literature to determine the essence of personalized learning; analysis of educational strategies using artificial intelligence;

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Volkov, Aleksandr, Yury Rishko, Diana Boboshko, Evgeniya Eliseeva, Dmitrii Treistar, and Antonina Kulapova. "Multitrack Educational Programs as a Method of Educational Process Personalization at Universities." Education Sciences 14, no. 1 (2023): 22. http://dx.doi.org/10.3390/educsci14010022.

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This study analyzes the approach towards educational process organization at a university. We study existing practices of educational program personalization (and the relevant existing limitations) in the Russian system of university-level education. This study reviewed the general trends in university program personalization and performed an in-depth analysis of actual cases of personalized study plan implementation at Russian universities (we compared the declared objectives of such programs with their real-life implementation results). In this article, the authors also highlight the limitat

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Tkachuk, Halyna. "Model of realization of personalized learning of students of higher education institution." Engineering and Educational Technologies 9, no. 3 (2021): 8–17. http://dx.doi.org/10.30929/2307-9770.2021.09.03.01.

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In the article, the author considers the organization of personalized learning of students of higher education by designing a pedagogical model of education. Personalized learning improves the educational process, as the student can build their own educational trajectory according to their individual needs, interests, preferences, and abilities. The proposed pedagogical model reveals the structure and content of personalized education of higher education institutions, the central figure of which is the student, his educational needs, interests, and abilities. We have identified the purpose of

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Вариясова, Е. В., Ж. В. Зайцева, and Н. С. Кульчицкая. "ON THE EXPERIENCE OF CREATING EDUCATIONAL CONTENT FOR PERSONALIZED LEARNING PLATFORMS." Severny region: nauka, obrazovanie, cultura 25, no. 2 (2024): 62–68. http://dx.doi.org/10.35266/2949-3463-2024-2-6.

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The article considers the experience of creating content for an educational platform based on the principles of personalized learning. The relevance of the article is due to the fact that the development of information technologies has made it possible to create personalized educational programs and use online platforms for one-to-one teaching. It creates yet more possibilities for teachers to personalize the teaching and learning process so that each student could achieve best results. The purpose of the article is to share the experience of creating a format of teaching and learning in which

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Hrytsenko, Valerii, Anna Tkachenko, Oksana Podolyan, Kostiantyn Dieiev, and Liubomyr Ilyn. "The role of artificial intelligence in personalisation of the learning process." Revista EDaPECI 24, no. 3 (2024): 152–65. https://doi.org/10.29276/redapeci.2024.24.320987.152-165.

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The study aims to empirically test the role of artificial intelligence in personalizing student learning through surveys, quantitative and qualitative data analysis, and modeling. To determine the role of artificial intelligence in personalizing the educational process, a survey of students was conducted on the organization of the educational process using artificial intelligence (format, form of education, educational environment, consulting communication tools, motivations). Based on the preferences and requests of students, an experimental program of personalized learning was generated and

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Hovakimyan, Anna, and Siranush Sargsyan. "Data Mining Methods in Educational Process Management." WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 21 (November 6, 2024): 110–16. http://dx.doi.org/10.37394/232010.2024.21.13.

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The paper addresses the challenge of effectively managing the educational process by leveraging intelligent data analysis of student performance during learning activities. It introduces an approach centered around data clustering, specifically applied to the study of programming disciplines and languages. By utilizing clustering techniques, the paper aims to identify the most challenging topics within a given academic subject, track students’ learning paths, evaluate and enhance teaching methodologies, and create personalized learning plans tailored to individual students’ needs. This approac

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S.M., Levin. "PERSPECTIVES OF DATA ANALYSIS IN THE EDUCATIONAL PROCESS OF HIGHER EDUCATIONAL INSTITUTIONS." ИННОВАЦИОННЫЕ НАУЧНЫЕ ИССЛЕДОВАНИЯ 2022. 11-1(23) (November 17, 2022): 196–205. https://doi.org/10.5281/zenodo.7331529.

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The article observes the perspectives of data analysis of learning management systems (LMS) to improve the quality of education and focus on the individually-oriented educational process of students of higher educational institutions. The author classifies groups of data generated in connection with student learning and also evaluates the effectiveness of the analysis of each of them. The article discusses the practical application of analysis in today's conditions of using distance learning platforms by universities and the prospects for developing a more personalized approach to students

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Ogata, Hiroaki, Brendan Flanagan, Kyosuke Takami, Yiling Dai, Ryosuke Nakamoto, and Kensuke Takii. "EXAIT: Educational eXplainable Artificial Intelligent Tools for personalized learning." Research and Practice in Technology Enhanced Learning 19 (August 28, 2023): 019. http://dx.doi.org/10.58459/rptel.2024.19019.

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As artificial intelligence systems increasingly make high-stakes recommendations and decisions automatically in many facets of our lives, the use of explainable artificial intelligence to inform stakeholders about the reasons behind such systems has been gaining much attention in a wide range of fields, including education. Also, in the field of education there has been a long history of research into self-explanation, where students explain the process of their answers. This has been recognized as a beneficial intervention to promote metacognitive skills, however, there is also unexplored pot

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Quandeng, Gou. "DEVELOPMENT OF A DIGITAL EDUCATIONAL ENVIRONMENT IN CHINA." Scientific Journal of Polonia University 56, no. 1 (2023): 40–46. http://dx.doi.org/10.23856/5606.

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Current model design research in the field of personalized learning mainly includes three categories: personalized learner model, personalized learning resource recommendation model, and personalized learning path model. The development of a personalized student model mainly depends on the basic information of the student, his interests and preferences, learning style, behavioral characteristics, etc.; and all learner information and data are also used to develop and implement a personalized learning resource recommendation model and an individualized learning path model Essential framework. T

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Spivak, Svitlana. "USING CLOUD-ORIENTED PERSONALIZED EDUCATION ENVIRONMENT IN THE ORGANIZATION OF EDUCATIONAL PROCESS." OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY, no. 4 (2018): 83–90. http://dx.doi.org/10.28925/2414-0325.2018.4.8390.

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This article covers the creation and use of a cloud-oriented personalized learning environment in the organization of the learning process. And focuses on specific factors of creating modern PLE (Personal learning environment) and the structure of the cloud-oriented personalized learning environment model was developed in the organization of the educational process in universities, using cloud services and electronic resources of the online services for the education of Office 365 of Microsoft, which takes into account the needs and peculiarities of the use of ICTs for all subjects educational

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Rybalchenko, Andrey, and Gulmira Abildinova. "Personalizing the learning process through data mining in higher education." Scientific Herald of Uzhhorod University Series Physics, no. 56 (April 9, 2024): 1580–88. http://dx.doi.org/10.54919/physics/56.2024.158fw0.

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Relevance. The rapid evolution of the modern labor market necessitates a shift from traditional, standardized career counseling to personalized educational trajectories that account for individual qualities, skills, and preferences, ensuring better professional adaptability and satisfaction. Purpose. The purpose of the study is to develop and validate a model that will facilitate the selection of a person-centred educational and professional pathway for students. Methodology. The research methods are comparative, qualitative, and statistical analysis, experimental method, modelling, and protot

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Semenov, A. L., A. E. Abylkassymova, and T. A. Rudchenko. "AI Methods in Control of Personalized General Education." Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleniâ 517, no. 1 (2024): 5–11. http://dx.doi.org/10.31857/s2686954324030011.

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The paper proposes a new approach to control the process of general education. Digital technology tools are used to form spaces of goals, tasks and learning activities, and to record the educational process of each student. Artificial intelligence tools are used when choosing a student’s personal goals and ways to achieve them, to make forecasts and recommendations to participants in the educational process. Big data from the entire education system and big linguistic models are used. The effects of the approach include ensuring the success of each student, objective assessment of the work of

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Вилкова, Алевтина Владимировна, Светлана Александровна Фадеева, Ольга Вячеславовна Алексеева, and Наталья Викторовна Александрова. "FROM PERSONALIZED EDUCATION OF ADULTS TO PERSONALIZED EDUCATION OF CHILDREN." Pedagogical Review, no. 6(34) (December 14, 2020): 23–30. http://dx.doi.org/10.23951/2307-6127-2020-6-23-30.

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Рассмотрена проблема, связанная с выявлением особенностей персонифицированного подхода в педагогическом процессе детей и взрослых. На основе историко-педагогического анализа характеризуется феномен персонификации, выявляются его особенности. Доказывается, что персонификация в образовании как педагогический принцип обусловливает возможность самоорганизации личностного образовательного пространства, так как связана, прежде всего, с максимальной активностью обучающегося. Определены основные пути персонификации в дополнительном профессиональном образовании взрослых. Представлены ориентиры организа

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Makhambetova, Aliya, Nadezhda Zhiyenbayeva, and Elena Ergesheva. "Personalized Learning Strategy as a Tool to Improve Academic Performance and Motivation of Students." International Journal of Web-Based Learning and Teaching Technologies 16, no. 6 (2021): 1–17. http://dx.doi.org/10.4018/ijwltt.286743.

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The purpose of the research is to study and scientifically substantiate personalized strategies as the basis for personalized learning aimed at improving academic performance and student motivation. The study involved 65 teachers and 700 students of various specialties from three Russian and Kazakhstan universities. The survey analysis showed that the modern generation of students is no longer motivated to receive standardized educational services, and in this context, personalized strategies as a form of personalized learning are a key trend that has permeated all aspects of professional trai

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Beese, Elizabeth Brott. "A process perspective on research and design issues in educational personalization." Theory and Research in Education 17, no. 3 (2019): 253–79. http://dx.doi.org/10.1177/1477878519893963.

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This article proposes that educational personalization may be usefully understood from a process perspective. It defines educational personalization as that which occurs within any planning or decision-making process which runs for one student at a time, and uses information from or about individual students, in order to output educational plans and decisions for them. Importantly, understanding personalization as a process illuminates what it is that we design when we design for personalized education (that is, planning and decision-making processes) – and helpfully suggests familiar process-

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Abdigapbarova, U., and N. Zhienbaeva. "Social activity of the future teacher's personality in the process of SCL transformation." Bulletin of the Karaganda University. Pedagogy series 102, no. 2 (2021): 112–18. http://dx.doi.org/10.31489/2021ped2/112-118.

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This article reveals the transformation of student-centered learning (SCL) in the process of forming the social activity of Kazakhstani students, which is a fundamental aspect that has an effective impact on the professional training of future teachers. The author substantiates the fact of the importance of the social activity of the modern student, the success factor of student-centered learning: emotional support and psychological support of teachers to improve the educational potential of student-centered learning. The conceptual provisions of the features of the introduction of student-cen

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19

Yulina, Galina, and P. Alieva. "Integration of Personalized and Adaptive Approaches in Modern Educational Practice." Scientific Research and Development. Socio-Humanitarian Research and Technology 13, no. 4 (2024): 36–41. https://doi.org/10.12737/2306-1731-2024-13-4-36-41.

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This article compares personalized and adaptive learning in the modern educational process. It analyzes the main features of each approach, focusing on the goals and methods of implementation. Particular attention is paid to the impact of these approaches on meeting the individual needs of students and improving their academic performance. The technologies used to monitor and analyze students and allow adapting the course content in real time are also discussed. The relevance of this article is due to the growing interest in adaptive learning technologies that can significantly improve the eff

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Berezhnaya, O. V., and A. L. Kobleva. "A model of a personalized educational program of advanced training courses for teaching staff: andragogical approach." Вестник Северо-Кавказского федерального университета, no. 2 (107) (2025): 205–12. https://doi.org/10.37493/2307-907x.2025.2.19.

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Introduction. The article examines organizational and methodological issues of the system of advanced training for teaching staff based on the individual characteristics and professional experience of adult learners. The authors focus on the importance of introducing personalized practices of advanced training of teachers and organizing relevant educational needs of teachers and the teaching labor market. As a tool for solving this problem, it is proposed to develop and implement personalized educational programs in the organization of advanced training courses for teachers. Goal. The study ai

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Aksenova, Alla. "Principles for personification of the educational process in post-nonclassical didactics (anthropological-axiological approach)." Man and Education, no. 1 (70) (2022): 177. http://dx.doi.org/10.54884/s181570410019988-1.

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The paper addresses the problem of the transformation of didactic principles at the present stage of education development. Much attention is given to scientific research of this change which is conducted using the anthropological-axiological approach. The authors take it within the framework of the post-non-classical understanding of modern didactics and the critical consideration of postmodern ideas.Rethinking of the study process is seemed mainly as personalized educational process and concerns changes in basic didactic communication. The findings show that personalized learning is the proc

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Sysoyev, P. V. "Personalized Learning Based on Artificial Intelligence: How Ready Are Modern Students for New Educational Opportunities." Vysshee Obrazovanie v Rossii = Higher Education in Russia 34, no. 2 (2025): 51–71. https://doi.org/10.31992/0869-3617-2025-34-2-51-71.

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One of the key advantages of integrating artificial intelligence (AI) technologies into education is the creation of conditions for the implementation of a personalized learning model – a system developing individual potential, in which the learner is the main subject of the educational process and, in accordance with individual abilities, interests and needs, selects the educational content and chooses methods, techniques, and means of teaching, determines the pace of mastering the educational material and takes responsibility for the process and outcome of the learning. At the same time, the

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Bodnar, G., and O. Bodnar. "OPPORTUNITIES FOR IMPROVING THE EDUCATIONAL PROCESS USING ARTIFICIAL INTELLIGENCE IN HIGHER MEDICAL EDUCATIONAL INSTITUTIONS." Sciences of Europe, no. 146 (August 8, 2024): 28–30. https://doi.org/10.5281/zenodo.13267838.

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Modern technologies are rapidly evolving, and artificial intelligence (AI) is becoming an integral part of many areas of life, including education. In medical training, AI opens new horizons, significantly improving the quality of future doctors' education. The application of AI in medical education contributes to the personalization of learning by providing an individual approach to each student, analyzing their progress, and offering the most effective teaching methods. Furthermore, AI can aid in simulating clinical situations, giving students the opportunity to practice in conditions as clo

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Shuyu, Deng. "PERSONALIZED LEARNING AS A TREND IN INTERNATIONAL EDUCATION." Arts education and science 2, no. 31 (2022): 70–75. http://dx.doi.org/10.36871/hon.202202009.

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The article describes the role and place of personalized learning in the development of world scientific and pedagogical thought, paying special attention to the relevance of this approach in modern international education. Personalized education is seen as an approach that responds to the interests of scientific and pedagogical schools around the world. Followers of this concept recognize that the term itself is in the process of formation. Personalized education is considered as an approach that satisfies the interests of scientific and pedagogical schools in various countries of the world.

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Bukina, T. V., M. V. Khramova, S. A. Kurkin, et al. "Neuroeducational software recommendational service as a tool for personalizing the educational process." Informatics and education 39, no. 5 (2024): 50–62. https://doi.org/10.32517/0234-0453-2024-39-5-50-62.

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The problem of insufficient cognitive development in schoolchildren can be solved through the organization of personalized extracurricular activities with the help of a recommendation service based on neuroscientific research and modern knowledge of brain function. The basis for compiling a personalized list of extracurricular activities is data obtained through neuroimaging technology in the process of testing the cognitive and personality characteristics of the student combined with information about their interests and preferences. Regular testing, conducted every three to six months, helps

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Sherayzina, Roza. "Pedagogical potential of personalized teaching of modern school students." Man and Education, no. 4 (65) (2020): 29. http://dx.doi.org/10.54884/s181570410020460-1.

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The article solves the problem of identifying the pedagogical potential of personalized teaching of students of a modern school, the solution of which will improve the educational process, enrich the types of educational activities in accordance with the individual characteristics of the student.

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Kostina, L. M., A. V. Nikolaeva, and N. V. Shvetsova. "STUDENTS' VIEWS ABOUT ARTIFICIAL INTELLIGENCE IN THE ORGANIZATION OF PERSONALIZED EDUCATION." Educational Psychology in Polycultural Space 68, no. 4 (2024): 63. https://doi.org/10.24888/2073-8439-2024-68-4-63-71.

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The article presents data from a study of the ideas of primary and secondary school students about artificial intelligence, its use in the educational process in ensuring the personalization of students' education. In modern conditions of the development of society and the demands of the state, expressed in Federal educational standards, much attention is paid to the individualization of the educational process of schoolchildren. One of the means to ensure this process can be the use of modern information technologies. However, science and practice may face the problem of children and adolesce

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Shvetsova, Olena, Iryna Tamozhska, Tetiana Nyzhnyk, Tetiana Zhytnik, and Viktoriia Kuleshova. "Personalized Educational Routes with Integration of Media Communication Space." Metaverse Basic and Applied Research 4 (July 6, 2025): 186. https://doi.org/10.56294/mr2025186.

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Introduction: The growing practical impact of innovative media and communication technologies on the educational environment is currently gaining particular relevance. The dynamics of educational needs within modern higher education and the new challenges of global social digitalization require the development and implementation of individual learning paths. The research is devoted to the formation and development of individual learning trajectories for students of the third (educational and scientific) level of higher education (field of knowledge 01 "Education/Pedagogy", specialty 011 "Educa

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Mindigulova, A. A., V. V. Vikhman, and M. V. Romm. "Artificial intelligence and personalized learning: Scaffolding technology." Professional education in the modern world 13, no. 4 (2024): 613–22. http://dx.doi.org/10.20913/2618-7515-2023-4-3.

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Introduction. The purpose of the study is to try to answer this question: what are the conditions and strategies for the implementation of AI technologies to solve the strategic task of the transition from mass educational technologies inherent in Industry 4.0 to individualized educational technologies of the upcoming Industry 5.0 using the example of scaffolding subtechnology (derived from the English word «scaffolding» meaning «staging»). The latter in educational discourse is understood as providing support to a student, solely as necessary, with a gradual decrease in the amount of such sup

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Tanashchuk, Kateryna, Oleksandr Derkach, Serhii Bazyka, and Bohdana Yamnyuk. "ADAPTIVE EDUCATIONAL ECOSYSTEMS USING ARTIFICIAL INTELLIGENCE FOR FORMING STUDENTS’ EDUCATIONAL TRAJECTORIES." Zeszyty Naukowe Wyższej Szkoły Humanitas Zarządzanie 25, no. 3 (2024): 97–106. http://dx.doi.org/10.5604/01.3001.0054.7772.

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The modern era of digitalization in education presents a particularly important task – developing effective methods for individualized learning. This approach should consider the personal needs of the student, their achievements, motivations, and adapt the learning process accordingly. Adaptive educational technologies allow for determining the state of the learning process, analyzing the student’s thought process, identifying challenging topics, and adapting the content for a deeper understanding of the subject matter.In our view, the use of artificial intelligence is one of the most effectiv

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Starostina, Svetlana E. "Implementation of the Pedagogical Path at the University: Problems and Prospects." Scholarly Notes of Transbaikal State University 18, no. 3 (2023): 41–47. http://dx.doi.org/10.21209/2658-7114-2023-18-3-41-47.

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Understanding the need to change education in the context of its personalization is aimed at studying this problem, both in theoretical and applied aspects. The purpose of the study: to analyze the activities of the population in the design of pedagogical trajectories; identification of tools for individualization of the educational process; substantiation of the generalized structure of the educational program, which makes it possible to implement flexible educational trajectories. Both theoretical (comparative analysis and synthesis, design) and empirical (observation, conversation, experime

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Wang, Yicong. "Strategies for Promoting Educational Equity through Educational Informatization." Journal of Education and Educational Research 9, no. 2 (2024): 100–103. http://dx.doi.org/10.54097/mp811093.

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With the advent of a new era, China's education development has entered a new stage, and the expectation for "quality education equity" is increasingly strong. Educational informatization, as an important force driving education modernization, plays a crucial role in promoting educational equity. Leveraging the advantages of information technology can break geographical limitations, widely disseminate high-quality educational resources, and provide a fair starting point for basic education. The popularization of online courses, distance education, and other forms allows students in remote area

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Ji, Xingle, Lu Sun, Xueyong Xu, and Xiaobing Lei. "Construction and Innovative Exploration of Personalized Learning Systems in the Context of Educational Data Mining." International Journal of Information and Communication Technology Education 20, no. 1 (2024): 1–14. http://dx.doi.org/10.4018/ijicte.346992.

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This study examines the current research on educational data mining, educational learning support services, personalized learning services, and personalized learning paths in education. The authors aim to integrate personalized learning concepts into traditional support services by drawing on the latest theoretical and practical research. Using multimodal data fusion techniques, the study conduct exploratory analyses on various data types, including learner academic performance, psychological assessments, learning behavior, and physiological information. This leads to the construction of a per

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Zhilmagambetova, Raushan, Zhanat Kopeyev, Akhan Mubarakov, and Ainagul Alimagambetova. "The Role of Adaptive Personalized Technologies in the Learning Process." International Journal of Virtual and Personal Learning Environments 13, no. 1 (2023): 1–15. http://dx.doi.org/10.4018/ijvple.324079.

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This article is devoted to the development of an adaptive personalized online course in mathematics for students, which was implemented on the Stepik platform. The authors discuss a non-linear approach to online learning that adapts to the needs of the student as they progress through the course content, which leads to an individual learner's experience based on prior knowledge. Within the framework of this article, the authors consider the experience of Aktobe Higher Polytechnic College in the task of implementing an adaptive personalized online course, and also offer specific methods and tec

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Amoako, Kwame, Akosua Asante, and Kwabena Owusu. "AI-Powered Tools for Personalized Learning in Educational Technology." International Journal of Technology and Modeling 3, no. 1 (2024): 46–56. https://doi.org/10.63876/ijtm.v3i1.115.

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In the digital era, the integration of Artificial Intelligence (AI) in educational technology has opened new avenues for optimizing the learning process through personalized approaches. This article proposes an innovative AI-based framework that combines predictive analytics, dynamic modelling of student learning profiles, and adaptive algorithms to craft learning experiences tailored to individual needs. The research methodology encompasses a systematic literature review, empirical case studies, and controlled experiments to evaluate the effectiveness of AI-powered educational tools. Findings

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Motorina, Valentinа, Oleksandr Khodorkovskyi, Valentina Olianich, Oksana Kyrychenko, and Larysa Yaroshevska. "Adaptation of the educational process in Ukraine to artificial intelligence technologies: A systematic review." Periodicals of Engineering and Natural Sciences (PEN) 13, no. 1 (2025): 55–68. https://doi.org/10.21533/pen.v13.i1.249.

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Artificial intelligence (AI) is crucial in transforming the modern educational system. This article analyses the key processes involved in adapting Ukraine's educational system to AI technologies through a systematic review. To achieve this aim, PRISMA guidelines were employed, enabling the identification of the most relevant literature on the subject. The selected academic sources were analyzed using the content analysis method. The findings highlight that the primary objectives of AI integration in education include facilitating personalized learning, enhancing digital skills, automating tas

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Boratalievna, Atajonova Saidakhon. "Models and Mechanisms for Implementing an Inclusive Approach in Engineering Education Based on Artificial Intelligence." International Journal of Pedagogics 5, no. 5 (2025): 110–14. https://doi.org/10.37547/ijp/volume05issue05-27.

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The article analyzes the models and mechanisms for implementing inclusive education in engineering education using artificial intelligence (AI) technologies. Modern digital tools that facilitate the adaptation of the educational process for students with different educational needs are analyzed. The principles of personalized learning, controlled knowledge control and intelligent decision support systems are described. Recommendations for the implementation of AI in the educational process with increased accessibility of the engineering educational process are given.

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Todino, Michele Domenico. "Educational Technologies." Encyclopedia 5, no. 1 (2025): 23. https://doi.org/10.3390/encyclopedia5010023.

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Educational technologies refer to the set of digital tools, resources, applications, and methodologies used to facilitate the teaching–learning process. These technologies include software, online platforms, hardware devices (such as tablets, computers, and interactive whiteboards), and, if one wants to give special relevance, as it should be these days, to the issue of inclusion in education, assistive technologies, all aimed at improving access to education, personalizing learning paths, and fostering a more dynamic interaction between students and teachers. This entry explores the integrati

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Bilyakovska, Olha, Nataliya Horuk, and Vitaliy Kukharskyy. "Personalized Learning in the Context of a Competence-Based Paradigm of Higher Education." International Scientific Journal of Universities and Leadership, no. 19 (June 30, 2025): 27–36. https://doi.org/10.31874/2520-6702-2025-19-27-36.

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One of the challenges facing the Ukrainian education system is organizing a learning framework that takes into account the individual capabilities and needs of each student. This challenge is addressed by the modern model of personalized learning, which creates conditions for developing students' professional interests, allows them to choose an optimal pace, content, and complexity of educational material, and effectively utilizes modern digital technologies to establish an innovative learning environment. The main features of personalized learning include individualization, differentiation, i

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Benkovska, Natalia, Nataliia Kharchenko, Larysa Kulbach, Ganna Rabokorovka, and Tetiana Bolotnykova. "Analyzing pedagogical strategies for personalized learning to compensate for students' learning losses." Eduweb 18, no. 1 (2024): 235–44. http://dx.doi.org/10.46502/issn.1856-7576/2024.18.01.17.

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The article is a comprehensive study of current methods and approaches used in modern educational practice to support students facing educational difficulties. The purpose is to analyze the effectiveness of individualization and personalization of the educational process in restoring educational losses. The literature review allows us to cover the existing theoretical frameworks of individualization and personalization in education. The analysis of pedagogical practice includes case studies and expert interviews, which provides an overview of successful strategies in real educational scenarios

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Αnastasia, Papadopoulou1* Antonios Bouras2. "Utilization of Artificial Intelligence in the Educational Process: Evaluation of Its Effective Implementation." ISRG Journal of Education, Humanities and Literature (ISRGJEHL) II, no. II (2025): 88–95. https://doi.org/10.5281/zenodo.15234805.

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<em>Artificial Intelligence (AI) is playing an increasingly significant role in the educational process, transforming teaching practices, assessment, and personalized learning. This study focuses on evaluating the utilization of AI in education, exploring its benefits, challenges, and implementation prospects.</em> <em>Through an analysis of existing tools, such as adaptive learning platforms, intelligent tutoring systems, and automated assessment, the study examines AI’s contribution to enhancing the learning experience. Additionally, challenges such as the need for technological infras

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Toktarova, Vera Ivanovna, and Dina Alekseevna Semenova. "The model of integrating a neurocognitive assistant into the educational process of the HEI: Structural and content analysis." Science for Education Today 15, no. 3 (2025): 159–79. https://doi.org/10.15293/2658-6762.2503.08.

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Introduction. The article is devoted to the problem of personalization of the educational process through neurocognitive technologies in the context of digital transformation of higher education. The purpose of the article is to theoretically substantiate and present a structural and content model of integrating a neurocognitive assistant into the educational environment of a HEI, to determine the components and describe the results of assessing its effectiveness in the process of preparing future teachers. Materials and Methods. The methodological basis of the study was formed by the systemic

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Kholmakhamatovich, Ikromov Khusan. "Analysis of Student Academic Performance Through Machine Learning Methods in The Field of Pedagogy." European International Journal of Pedagogics 5, no. 5 (2025): 138–42. https://doi.org/10.55640/eijp-05-05-30.

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This article explores the application of machine learning algorithms to analyze, predict, and personalize the educational process based on students' academic performance. Through statistical and computational methods, various student-related attributes were analyzed, with the Random Forest algorithm identified as the most accurate predictive model. The study led to the development of an intelligent system for diagnostic evaluation, personalized approaches, and automated pedagogical recommendations. The findings highlight the significant potential of artificial intelligence tools in enhancing t

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Dipanwita Bit, Souvik Biswas, and Mrinmoy Nag. "The Impact of Artificial Intelligence in Educational System." International Journal of Scientific Research in Science and Technology 11, no. 4 (2024): 419–27. http://dx.doi.org/10.32628/ijsrst2411424.

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The way we teach and learn could be completely transformed by artificial intelligence (AI), which could make the process more efficient, personalized, and interesting. Artificial intelligence (AI) in education is the application of AI technologies, like natural language processing and machine learning, to improve the educational process. Through the use of algorithms, teachers are able to customize learning for each student by analyzing data, finding trends, and making predictions. Artificial intelligence (AI) and its potential impact on education have gained widespread attention because of Ch

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Gavrisenko, Elena Andreevna, and Natalia Viktorovna Frolova. "Formation of the basic logical activity “identification of cause-and-effect relationships in the study of phenomena and processes” in schoolchildren." Pedagogy. Issues of Theory and Practice 9, no. 4 (2024): 330–39. http://dx.doi.org/10.30853/ped20240042.

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The aim of the research is to substantiate the need to introduce an educational module aimed at developing the basic logical activity “identification of cause-and-effect relationships” into the school educational process from the perspective of personalized learning. The paper proposes the structure of the specified educational module and substantiates its compliance with the personalized learning model. The scientific novelty of the research lies in elaborating a technology for the consistent formation of the basic logical activity “identification of cause-and-effect relationships in the stud

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Veretennikova, L. A. "DESIGNING OF PERSONALIZED TRAINING FOR YOUNG TEACHERS." Vestnik Altaiskogo Gosudarstvennogo Pedagogiceskogo Universiteta, no. 49 (December 1, 2021): 7–11. http://dx.doi.org/10.37386/2413-4481-2021-4-7-11.

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The article analyzes the main qualitative characteristics that determine the development process of additional education at the pedagogical university. The potential of the postgraduate education system as a resource for the development of the regional education system is revealed. The diagnostic potential of projects of postgraduate support of young teachers has been determined. The mechanism of designing personalized educational routes for advanced training of young teachers is disclosed.

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Somova, M. V. "METHODOLOGICAL MODEL OF PERSONALIZED EDUCATIONAL PROCESS BASED ON PREDICTION OF SUCCESS IN SUBJECT TRAINING." Современные наукоемкие технологии (Modern High Technologies) 1, no. 12 2023 (2023): 165–70. http://dx.doi.org/10.17513/snt.39877.

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Sadykova, F. E. "Personalization of learning in programming on the example of the application of internet services." Informatics and education 37, no. 6 (2023): 62–68. http://dx.doi.org/10.32517/0234-0453-2022-37-6-62-68.

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The article deals with the concept of personalization of learning, actualizes the need to develop a model of the personalized learning in the field of vocational education in the context of the implementation of the National Program “Digital Economy”. Existing learning models created on the basis of a personalized approach are analyzed. A comparative analysis of the personalized, differentiated, individualized and adaptive learning is given. A model of personalization of learning in the study of programming through Internet services has been developed. The practical significance of the study l

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Vieilandie, Liliia. "Personalized Learning: Development and Implementation of Individual Educational Trajectories to Enhance the Quality of Students' Knowledge." Problems of Education, no. 1(102) (June 16, 2025): 486–500. https://doi.org/10.52256/2710-3986.1-102.2025.33.

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The article explores the relevance of personalized learning within the context of educational reforms in Ukraine, particularly concerning the New Ukrainian School (NUS), upper secondary school reforms, the challenges of wartime, and blended learning. The potential of personalized learning, through the development and implementation of individual educational trajectories, to enhance the quality of students' academic achievements is substantiated. The scientific works of leading domestic and foreign researchers, which reveal the theoretical foundations and practical aspects of personalized learn

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Abdykerimova, Elmira Altynbekovna, and Aray Myrzabayqyzy Duisekenova. "INNOVATIVE TEACHING METHODS AND THEIR EFFECTIVENESS ON THE MAGIC SCHOOL PLATFORM BASED ON ARTIFICIAL INTELLIGENCE." Yessenov Science Journal 49, no. 4 (2024): 117–25. https://doi.org/10.56525/lriz3456.

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This article analyzes innovative teaching methods and their effectiveness on the Magic School platform based on artificial intelligence. In today's educational system, the use of AI technologies offers new opportunities to personalize the learning process, address students' needs, and improve the quality of education. The Magic School platform provides learning programs tailored to students' ability levels and enables educators to efficiently monitor the learning process. The article explores the effectiveness of innovative methods such as adaptive learning, personalized tasks, and gamificatio

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