Dissertation Defense for the Degree of Doctor of Philosophy (PhD) by Mukashova Ainur in the educational program «8D06103 - Information systems»
The dissertation defense of Ainur Onlasynovna Mukashova for the degree of Doctor of Philosophy (PhD) in the specialty “8D06103 – Information Systems” will be held at L.N. Gumilyov Eurasian National University. The dissertation topic is “Intellectual information system of generation of professional competencies of a learner.”
The dissertation was carried out at the «Information Systems education department» of L.N. Gumilyov Eurasian National University.
The language of defense is kazakh
Official reviewers:
Akerke Saparovna Akanova – Doctor of Philosophy (PhD), Associate Professor, Head of the Educational Programs Group “Computer Science,” NJSC “S. Seifullin Kazakh Agrotechnical University” (Astana, Republic of Kazakhstan);
Abdildayeva Assel Assylbekovna – Doctor of Philosophy (PhD), Associate Professor, Vice-Rector for Academic Affairs and International Relations at the International University of Engineering and Technology (Almaty, Republic of Kazakhstan.
Temporary members of the Dissertation Committee:
Vladimir Barakhnin – Doctor of Technical Sciences, Associate Professor, Leading Researcher at the Federal Research Center for Information and Computing Technologies (FRC ICT) (Novosibirsk, Russia);
Mamyrbayev Orken Zhumazhanovich – Doctor of Philosophy (PhD), Professor, Deputy Director for Science at the RSE "Institute of Information and Computational Technologies" of the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Almaty, Republic of Kazakhstan);
Zhaxybayev Darkhan Orakbayevich – Doctor of Philosophy (PhD), Senior Lecturer of the Educational Program "Information Systems", S. Seifullin Kazakh Agrotechnical Research University (Astana, Republic of Kazakhstan).
Scientific advisors:
Mukhanova Ayagoz Asanbekovna – Doctor of Philosophy (PhD), Associate Professor of the “Information Systems” Department at the L.N. Gumilyov Eurasian National University (Astana, Republic of Kazakhstan).
Leonid Kupriyanovich Bobrov – Doctor of Technical Sciences, Professor of the Department of Applied Informatics, Novosibirsk State University of Economics and Management “NSUEM” (Novosibirsk, Russia).
The defense will take place on May 22, 2026, at 12:00 PM in the Dissertation Council for the training direction «8D061 – Information and communication technologies» in the educational program «8D06103 – Information systems» of L.N. Gumilyov Eurasian National University. The Dissertation Council meeting will be held offline and online.
Link: https://teams.microsoft.com/meet/43341357332282?p=e8aVsGtsJCc2lmgUJl
Address: Astana city, Pushkin Street 11, Room 222.
Abstract (English): ABSTRACT of the dissertation work by Ainur Onlasinovna Mukashova on the topic «Intellectual information system of generation of professional competencies of a learner» submitted for the degree of Doctor of Philosophy (PhD) under the educational program «8D06103 - Information Systems» Relevance of the study. In the context of the digital transformation of the economy and the education system, the volume of regulatory, methodological, and professional information used in the development and updating of educational programs has significantly increased. A central role in this information landscape is played by professional standards, sectoral qualification requirements, the Atlas of Emerging Professions, as well as related descriptions of labor functions, knowledge, skills, and abilities. Their analysis, comparison, and integration into educational programs are currently performed largely manually, which substantially reduces the efficiency, consistency, and reproducibility of decision-making processes. The formalization and classification of professional standards and competency descriptions represent a key stage in the digitalization of educational program design and the management of learners’ competencies. At this stage, expert involvement remains dominant due to the low level of structure in the source data, differences in terminology, and the absence of unified formats for representing knowledge, skills, and abilities. This results in the fragmented use of professional standards, formalistic alignment with learning outcomes, and limited adaptability of educational programs to labor market requirements. Modern artificial intelligence methods, including natural language processing, machine learning, and deep learning, open fundamentally new opportunities for the automated analysis, formalization, and generation of professional competencies. The application of intelligent algorithms enables the identification of latent semantic structures in regulatory and professional texts, the detection of stable patterns of labor functions and competencies, and the prediction of their alignment with qualification levels and educational outcomes. Unlike traditional approaches, such methods ensure scalability, interpretability, and a reduction in subjectivity in decision-making. In practice, educational institutions and authorized bodies operate with large volumes of weakly structured professional data presented in the form of textual descriptions of standards, reports, methodological materials, and registries of educational programs. These data often lack formalized metadata, are characterized by terminological heterogeneity, and vary in the level of detail, which significantly complicates their intelligent processing and alignment. As a result, there is a need to develop an intelligent information system capable of automatically transforming such data into formalized models of professional competencies and learning outcomes. The relevance of this research is обусловлена the necessity to develop and implement an intelligent information system that ensures the automated formalization, generation, classification, and alignment of learners’ professional competencies based on professional standards and the Atlas of Emerging Professions. In the context of the digitalization of the education system and the dynamic evolution of labor market requirements, the volume of regulatory, professional, and educational data subject to analysis and interpretation in the design of educational programs is rapidly increasing. These materials are typically presented as weakly structured texts characterized by terminological heterogeneity and varying levels of detail, which significantly complicates their use in automated systems. Modern artificial intelligence methods, including natural language processing and machine learning, enable the identification of latent semantic relationships between labor functions, knowledge, skills, and abilities, as well as their formalization and intelligent classification. The application of these methods makes it possible to automatically transform professional standards and the Atlas of Emerging Professions into formalized models of competencies and learning outcomes, aligned with qualification levels and individual educational trajectories of learners. Information systems based on machine learning methods provide automated and high-precision solutions for the generation and alignment of professional competencies, reducing reliance on expert judgment and minimizing subjective factors. This enhances the reproducibility and validity of decisions made in the development and updating of educational programs, while also ensuring their adaptability to changes in the socio-economic environment and labor market requirements. Particular relevance is associated with the task of intelligent alignment between professional standards and the Atlas of Emerging Professions, aimed at identifying both correspondences and discrepancies between current and prospective requirements for professional training. The development of an intelligent information system capable of automatically analyzing, formalizing, and harmonizing these data sources contributes to the formation of a coherent, non-contradictory, and dynamically updatable model of learners’ professional competencies. Issues related to the digitalization of educational process management and the development of professional competencies have been addressed in the works of a number of domestic and international scholars. In particular, studies by Kaibassova, Barlybayev, Tapalova, Minaeva, Jaber A. H., and other researchers analyze approaches to the development of intelligent educational systems, including the design of models and algorithms that ensure the dynamic alignment of course content with professional competency requirements and the formation of labor market–relevant skills among graduates. The conducted review of contemporary research has served as the basis for refining the direction of the present dissertation and formulating its objectives. In particular, the tasks of automated formalization, classification, and intelligent alignment of professional standards and the Atlas of Emerging Professions with learning outcomes currently represent a significant scientific and practical challenge. The accumulation of large volumes of professional-regulatory and educational data, along with the need to transform them into digital and formalized formats, requires the management of information systems at a qualitatively new level. Professional standards, the Atlas of Emerging Professions, and descriptions of labor functions, knowledge, skills, and abilities are predominantly presented as weakly structured textual documents, which significantly complicates their analysis, alignment, and use in the processes of designing and updating educational programs. Under these conditions, the development of methods that ensure interpretable and reproducible assessment of the relevance of professional data becomes particularly important. The advancement of modern artificial intelligence methods, including natural language processing and machine learning, opens new opportunities for the intelligent management of large-scale professional data. However, most existing approaches are primarily focused on vector-based text representations and do not explicitly account for structural relationships between elements of knowledge, skills, and abilities. In this context, the present study applies and further develops the Cartesian Text Relevance (CTR) method, which enables the formalized alignment of professional standards and the Atlas of Emerging Professions based on a Cartesian matrix of correspondences between competency components and the calculation of a structural coverage measure. The application of the CTR method makes it possible to identify latent semantic and structural relationships between labor functions, knowledge, skills, and abilities, providing an interpretable assessment of their degree of alignment. Unlike traditional text similarity methods, CTR is not based on aggregated vector similarity but rather on the analysis of intersection and coverage of semantic elements, which is particularly important when working with regulatory and professional texts characterized by high terminological variability and heterogeneity. The integration of the CTR method into an intelligent information system ensures the automated classification, alignment, and harmonization of professional standards and the Atlas of Emerging Professions, forming coherent and non-contradictory models of learners’ professional competencies. Accurate and efficient intelligent classification based on CTR optimizes the processes of designing and updating educational programs, enhances their adaptability to labor market requirements, and strengthens the validity of managerial decision-making. The advantage of the proposed approach lies in the ability of the CTR method to account for complex semantic and structural features of professional standards, providing more precise formalization and alignment of competencies compared to traditional machine learning methods. The incorporation of CTR into the architecture of an intelligent information system enables a transition from formal text matching to a content-driven and interpretable model of managing learners’ professional competencies, thereby confirming the relevance and scientific and practical significance of this research. The objective of the dissertation research is to develop an intelligent information system that ensures the automated formation, classification, and alignment of learners’ professional competencies and learning outcomes based on professional standards and the Atlas of Emerging Professions, with the aim of enhancing the relevance and adaptability of educational programs to labor market requirements. To achieve the stated objective, the following tasks have been defined: 1. To analyze contemporary methods for processing and analyzing professional standards and the Atlas of Emerging Professions, including natural language processing techniques and traditional machine learning algorithms, with the identification of their advantages and limitations. 2. To develop methods for the preprocessing, formalization, and structuring of textual descriptions of professional standards (labor functions, knowledge, skills, and abilities), ensuring their suitability for intelligent processing. 3. To construct an intelligent model based on a vector of informative features, capable of performing automated classification, identification, and determination of qualification levels of professional competencies and learning outcomes, as well as to evaluate its effectiveness using key performance metrics. 4. To develop and validate an intelligent information system designed for the automated formation, analysis, and alignment of professional competencies and learning outcomes. The main results presented for defense are as follows: 1. A unique dataset has been constructed, comprising formalized professional standards and data from the Atlas of Emerging Professions, including descriptions of labor functions, knowledge, skills, and abilities, adapted for intelligent processing and subsequent analysis. 2. A method for the formalization and intelligent alignment of professional standards has been developed, based on the construction of a vector of informative features and the application of machine learning, enabling the automated classification of professional competencies and learning outcomes by qualification levels. 3. An intelligent information system has been developed and validated, designed for the automated formation, analysis, and alignment of professional competencies and learning outcomes. The effectiveness of the proposed models has been confirmed through experimental studies, and the interpretability of the obtained results is ensured through the use of structural relevance methods and feature analysis. The scientific novelty of the research is as follows: 1. A model for the intelligent formalization of professional standards has been proposed, based on natural language processing and machine learning methods, enabling the automatic transformation of unstructured descriptions of labor functions, knowledge, skills, and abilities into formalized professional competencies and learning outcomes. 2. A method for constructing a vector of informative features has been developed, reflecting the semantic, functional, and level-based characteristics of professional competencies and learning outcomes, which enables their automated classification and identification by qualification levels. 3. The Cartesian Text Relevance (CTR) method has been proposed for the interpretable assessment of relevance and structural alignment between professional standards, the Atlas of Emerging Professions, and educational outcomes, based on the construction of a Cartesian matrix of correspondences between elements of knowledge, skills, and abilities. 4. An intelligent information system has been developed that provides automated formation, analysis, and alignment of learners’ professional competencies and learning outcomes. The object of the research is the process of formation, structuring, and correlation of professional competencies within the higher education system of the Republic of Kazakhstan. The subject of the research is the models and methods for the automated formalization, classification, and intelligent alignment of learners’ professional competencies and learning outcomes based on textual data from professional standards and the Atlas of Emerging Professions, using natural language processing and machine learning techniques. Research methodology. To address the stated objectives, algebraic formulations, machine learning methods, and data analysis techniques were employed. Research methods. The study applied methods of text data preprocessing, semantic analysis, and vector representation of data, as well as machine learning and classification algorithms for the automated identification of professional competencies and qualification levels. Software. The methodological foundation for the development of algorithms and software tools was supported by Python and PostgreSQL. Theoretical significance of the research. This study extends the theoretical foundations in the field of intelligent formalization and automated classification of professional competencies and learning outcomes based on the analysis of unstructured textual data from professional standards and the Atlas of Emerging Professions. The work addresses theoretical aspects of modeling competencies as formalized objects suitable for intelligent processing, as well as the adaptation of natural language processing and machine learning methods to the tasks of generation, classification, and alignment of professional competencies. Practical significance of the research.The results of the study have significant practical value for automating the management of professional competencies and improving educational programs. They ensure a substantial reduction in the time required for updating educational programs, enhance the precision of competency formulations, and improve the alignment between learning outcomes and employer requirements. The key advantage of the developed Cartesian Text Relevance (CTR) method lies in its ability to increase the reliability of textual data analysis by enabling a more accurate assessment of semantic similarity between professional standards, sectoral requirements, and educational outcomes. The use of CTR allows for a deeper analysis of textual structures and minimizes errors in competency alignment. The application of the intelligent system and the CTR method contributes to improving the quality of workforce training, strengthening the linkage between education and the labor market, and advancing digitalization mechanisms in the management of educational programs. The results of the research can be applied both in scientific studies and in higher education practice for the development and modernization of educational programs, ensuring increased efficiency and reduced risks of misalignment with the demands of a rapidly evolving economy. Personal contribution of the doctoral candidate.The research presented in the dissertation was carried out independently by the candidate in the course of their scientific work. The candidate personally developed the software implementing the proposed algorithms and analytical methods and obtained the experimental and theoretical results submitted for defense. The individual contribution of the author to co-authored publications consists in the development of methods and algorithms, as well as in the preparation and presentation of the research results for publication. Validity, reliability, and substantiation of the research findings are ensured through their approbation at international scientific and scientific-practical conferences; the publication of results in peer-reviewed journals indexed in international scientometric databases such as Scopus and Web of Science (Clarivate Analytics); as well as through the receipt of implementation certificates (Appendix A), copyright certificates, and patents (Appendices Ә, Б, В). Approbation of the dissertation results. The main results of the dissertation were tested and implemented during the development of educational programs at the School of Artificial Intelligence and Data Science of Astana IT University. The implementation was carried out within the framework of designing and updating educational programs aligned with the requirements of professional standards and the Atlas of Emerging Professions. Publications. The main provisions of the dissertation have been published in the following scientific works. A total of 5 publications have been produced on the research topic, including 1 article in a journal indexed in the Scopus database, 2 articles in journals recommended by the Committee for Quality Assurance in Science and Higher Education of the Ministry of Science and Higher Education of the Republic of Kazakhstan, and 2 papers in the proceedings of international and national conferences. Based on the results of the dissertation, 3 certificates of state registration of copyright objects and 1 patent for a utility model have been obtained. Scope and structure of the dissertation. The dissertation is written in the Kazakh language and consists of an introduction, four interrelated chapters divided into subsections, a conclusion, and a list of references. The total length of the work is 81 pages, including 32 figures and 8 tables. The reference list comprises 92 sources. The Introduction substantiates the relevance of the chosen dissertation topic, formulates the research objectives and main tasks, highlights the scientific novelty and practical significance of the work, and presents a dynamic literature review on the research topic. The first chapter examines the theoretical and methodological foundations of the process of generating professional competencies. It analyzes the role and significance of automated competency generation within the higher education system. A review of traditional algorithms and machine learning–based approaches used for generating professional competencies is provided, along with a comparative analysis of these methods. The second chapter develops and justifies the architecture of an intelligent information system designed to automate the formation, analysis, and formalization of professional competencies. The modular structure of the system is described, integrating natural language processing, machine learning, and generative models. A mechanism for the automatic generation of competencies and learning outcomes based on KSA analysis is presented. In addition, the Cartesian Text Relevance (CTR) method is developed and investigated, providing an interpretable assessment of textual similarity and structural alignment between professional standards and educational requirements. It is demonstrated that the proposed methods and architectural solutions form the basis for the intelligent alignment of educational programs with labor market demands. The third chapter presents the development and implementation of an intelligent information system for generating professional competencies and learning outcomes, aimed at automating the alignment of professional standards, the Atlas of Emerging Professions, and educational programs. A multi-level system architecture is described, including the user interface, server-side components, intelligent analysis and generation modules, and a data storage layer, ensuring scalability and extensibility. A multi-stage process for the automated generation of competencies and learning outcomes is implemented based on KSA analysis and Bloom’s taxonomy, along with a mechanism for constructing competency maps. It is shown that the integration of the proposed Cartesian Text Relevance (CTR) method enables the accurate identification of similar and cross-sectoral competencies, thereby improving the alignment of educational programs with labor market requirements. The Conclusion summarizes the main scientific findings of the dissertation research and presents the key propositions submitted for defense. Acknowledgements. The author expresses sincere gratitude to Professor Tusupov Dzhamalbek Aliaskarovich, as well as to the scientific supervisors - Associate Professor Mukhanova Ayagoz Asanbekovna of the Department of Information Systems at the L. N. Gumilyov Eurasian National University - for formulating relevant and substantive research tasks, providing valuable recommendations, and offering continuous scientific support at all stages of the dissertation research. The author also extends sincere thanks to the international scientific consultant, Professor Bobrov of the Department of Applied Informatics at the Novosibirsk State University of Economics and Management, for methodological guidance, professional advice, and expert evaluation of the research results.
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