MODELS FOR DEVELOPING STUDENTS’ COMPETENCE IN THE FIELD OF ARTIFICIAL INTELLIGENCE

ABSTRACT

This paper examines modern models for developing students’ competencies in the field of artificial intelligence. The study focuses on the integration of artificial intelligence technologies into higher education, the alignment of theoretical knowledge with practical skills, and the implementation of interdisciplinary and learner-centered educational approaches. Particular attention is paid to the formation of technical, professional, ethical, and research competencies among students. The proposed conceptual model aims to enhance students’ digital thinking, increase their competitiveness in the labor market, and promote a culture of responsible use of artificial intelligence technologies.

АННОТАЦИЯ

В данной статье рассматриваются современные модели развития компетентности студентов в области искусственного интеллекта. В исследовании анализируются вопросы интеграции технологий искусственного интеллекта в систему высшего образования, сочетания теоретических знаний с практическими навыками, а также применения междисциплинарного и личностно-ориентированного подходов к обучению. Особое внимание уделяется формированию у студентов технических, профессиональных, этических и исследовательских компетенций. Предложенная концептуальная модель направлена на развитие цифрового мышления обучающихся, повышение их конкурентоспособности на рынке труда и формирование культуры ответственного использования искусственного интеллекта.

Keywords: artificial intelligence, competency development, higher education, adaptive learning, interdisciplinary approach, digital transformation.

Ключевые слова: искусственный интеллект, компетентность, высшее образование, адаптивное обучение, междисциплинарный подход, цифровая трансформация.

Introduction.

In recent years, the rapid development of artificial intelligence (AI) technologies has deeply penetrated all spheres of social life, particularly the education system. Under conditions of digital transformation, the mere acquisition of knowledge is no longer sufficient; instead, the ability to analyze, model, and apply knowledge to solve practical problems, as well as to independently acquire new knowledge and technologies, has become critically important. From this perspective, higher education institutions face the urgent task of forming and developing students’ competence in the field of artificial intelligence.

Traditional educational approaches are primarily oriented toward the transmission of theoretical knowledge and often fail to fully address the practical, interdisciplinary, and innovative competencies demanded by the modern labor market. Artificial intelligence, however, offers opportunities to fundamentally transform the educational process, turning it into a learner-centered, adaptive, and outcome-oriented system. Through AI-based educational technologies, it becomes possible to design individualized learning trajectories tailored to students’ knowledge levels, interests, and learning pace, automate assessment processes, and manage educational activities more effectively.

Today, competence in artificial intelligence is not limited to programming or purely technical skills. It encompasses a wide range of dimensions, including algorithmic thinking, data literacy, systematic problem analysis, awareness of ethical and legal responsibilities, and the establishment of effective collaboration between humans and artificial intelligence systems. Consequently, the development of students’ AI competence requires a comprehensive approach that integrates theoretical knowledge, practical activities, interdisciplinary learning, and ethical values.

This study analyzes contemporary pedagogical models aimed at developing students’ competence in the field of artificial intelligence and examines their role and significance within the educational process. The primary objective of the research is to substantiate a conceptual approach that facilitates the formation of sustainable, practice-oriented, and responsible AI competencies among students in higher education. This introductory section serves to highlight the relevance of the research, justify the problem statement, and provide a methodological foundation for the theoretical and practical analyses presented in subsequent sections. The structural composition of artificial intelligence competence is presented in Table 1.

Table 1.

Structural Components of Students’ Competence in the Field of Artificial Intelligence

Methodology.

This study is aimed at the scientifically grounded investigation and evaluation of models for developing students’ competence in the field of artificial intelligence and employs a comprehensive methodological approach. The research methodology is based on the integration of pedagogy, information technologies, and artificial intelligence, and incorporates theoretical analysis, empirical observation, and modeling methods.

At the first stage of the study, a systematic review and analysis of scientific literature was conducted. This stage focused on examining both domestic and international scholarly sources related to AI-based education, competency-oriented approaches, adaptive learning systems, and interdisciplinary integration. The analysis enabled the identification of the strengths and limitations of existing models, as well as the extraction of key structural elements essential for the development of students’ AI competence.

At the second stage, a conceptual modeling approach was applied. During this phase, the process of forming students’ competence in the field of artificial intelligence was modeled using a systems-based perspective. The model incorporates theoretical training, practical sessions, project-based and research activities, as well as ethical and social responsibility components. To ensure the progressive development of competencies, the interrelationships among competence components and their developmental dynamics were defined.

At the third stage, empirical research methods were employed. The effectiveness of the proposed model was examined through observations of students, analysis of learning outcomes, and evaluation of practical assignments and project-based work. In addition to test results, the assessment process considered students’ abilities to solve problem-based tasks, demonstrate independent thinking, and apply practical skills in real-world contexts.

At the fourth stage, comparative and generalization methods were used. The outcomes of the proposed model were compared with traditional educational approaches to assess the effectiveness of AI-based competence development. Based on the obtained results, methodological conclusions were drawn, and recommendations for further improvement of the model were formulated. The research methods applied in this study and their respective objectives are summarized in Table 2.

Table 2.

Research Methods and Their Application Areas

Overall, the research methodology enables a comprehensive and systematic examination of the process of developing students’ competence in the field of artificial intelligence. The applied methods ensure the reliability and scientific validity of the research findings and provide a solid methodological foundation for the results and discussions presented in the subsequent sections.

Literature review.

In recent years, the development of students’ competence in the field of artificial intelligence has emerged as a significant research direction in international scholarly studies. Researchers emphasize that the integration of artificial intelligence technologies into education not only contributes to the automation of learning processes but also plays a crucial role in developing students’ cognitive, practical, and creative competencies (Ghamrawi, 2023; Walter, 2024).

In foreign academic literature, AI-based educational models are predominantly analyzed within the framework of competency-oriented approaches. These studies extensively address adaptive learning systems, learner-centered education, and mechanisms for analyzing educational processes using artificial intelligence (Luckin et al., 2016; Holmes, Bialik, & Fadel, 2019). Such approaches are particularly valuable as they enable the consideration of students’ individual needs, real-time assessment of learning progress, and the design of personalized learning trajectories.

A number of studies consider artificial intelligence as a key tool for ensuring interdisciplinary integration within the educational process. Researchers argue that the integration of AI with fields such as economics, medicine, engineering, and the social sciences facilitates the formation of comprehensive and practice-oriented competencies among students (Zawacki-Richter et al., 2019; Chen, Xie, & Hwang, 2020). This integration plays an important role in enhancing graduates’ adaptability to the real labor market.

Moreover, scholarly sources place particular emphasis on ethical and social issues related to the use of artificial intelligence in education. Algorithmic fairness, data security, privacy protection, and human–AI collaboration are identified as essential competencies that should be developed among students (Floridi et al., 2018; UNESCO, 2021). This perspective highlights the necessity of developing AI competence not only from a technical standpoint but also in terms of responsible and conscious use.

In local studies, the implementation of artificial intelligence in education has primarily been examined in relation to improving digital literacy, applying innovative pedagogical technologies, and enhancing the quality of education (Karimov, 2022; Usmonov, 2023). However, existing research has not sufficiently developed a holistic, systematic, and stage-based model for developing students’ competence in the field of artificial intelligence.

The analysis of the literature indicates that, although various approaches to AI-based education currently exist, there remains a need to integrate them within a unified conceptual model (Ghamrawi, 2023; Walter, 2024). In particular, a competency-oriented approach that harmoniously integrates theoretical knowledge, practical activities, interdisciplinary integration, and ethical responsibility remains a pressing scientific issue. A summary of international research findings is presented in Table 3.

Table 3.

Analysis of International Studies on Artificial Intelligence and Competency Development

Therefore, this study aims to address the identified gap in existing research by substantiating a systematic and comprehensive approach to the development of students’ competence in the field of artificial intelligence.

Results and discussion.

Within the framework of this study, the effectiveness of the proposed conceptual model for developing students’ competence in the field of artificial intelligence was evaluated through both theoretical and empirical analyses. The obtained results demonstrate that the application of the proposed approach in higher education contributes to the comprehensive development of students’ knowledge, skills, and competencies.

Table 4.

Competencies Developed as a Result of AI-Based Education

The research findings indicate that the use of artificial intelligence–based educational models significantly increases students’ level of mastery of theoretical knowledge. In particular, skills related to algorithmic thinking, data handling, and the analysis of problem-based situations demonstrated higher performance compared to traditional teaching methods. This improvement can be attributed to the capabilities of AI technologies to support visualization, modeling, and individualized learning approaches within the educational process.

Figure 1. Comparison of the effectiveness of traditional and artificial intelligence–based education

The analysis of outcomes from practice-oriented activities and project-based learning demonstrates an accelerated formation of students’ professional competencies. During activities involving real-world datasets, the development of artificial intelligence models, and their evaluation, students exhibited independent decision-making and creative approaches. These findings confirm the practical significance of AI-based educational models and strengthen their role in preparing specialists who meet labor market demands.

Learning activities organized on the basis of interdisciplinary integration were particularly effective in enhancing students’ ability to apply knowledge across different domains. The study revealed that tasks integrating artificial intelligence with economics, education, and social sciences fostered students’ systematic thinking and competencies in solving complex problems. These results are consistent with conclusions in the literature regarding the effectiveness of interdisciplinary approaches (Zawacki-Richter et al., 2019; Walter, 2024).

The results also indicate that incorporating ethical and social responsibility components into the educational process positively influences the formation of a culture of conscious and responsible use of artificial intelligence among students. Discussions on algorithmic fairness, data security, and human–AI collaboration were actively engaged by students, enhancing their ability to consider social implications when making technological decisions.

During the discussion, the obtained results were compared with existing scientific studies. In particular, the findings align with prior research suggesting that AI-based adaptive learning models support individualized learning trajectories for students (Ghamrawi, 2023; Holmes et al., 2019). At the same time, a distinctive contribution of this study lies in its integrated treatment of technical, practical, and ethical components of competence development within a unified system.

Table 5.

Recommendations for Integrating Artificial Intelligence into Education.

Overall, the results and discussion indicate that the proposed model is effective in developing students’ competence in the field of artificial intelligence and enables their preparation in alignment with the demands of the modern digital society. The conclusions drawn in this section serve as a basis for generalization in subsequent stages and for the formulation of the study’s final conclusions and practical recommendations.

Conclusions and Recommendations for Integrating Artificial Intelligence into Education.

The results of this study indicate that integrating artificial intelligence technologies into the educational process represents one of the key strategic directions for the development of modern higher education systems. Approaches aimed at developing students’ competence in the field of artificial intelligence not only enhance the effectiveness of knowledge transfer but also foster students’ independent thinking, problem analysis, and capacity to generate innovative solutions. The conceptual model developed in this study confirms that an AI-based educational environment enables the balanced development of students’ technical, professional, and ethical competencies.

In conclusion, the integration of artificial intelligence into education requires the harmonization of traditional pedagogical approaches with modern digital technologies. In this context, artificial intelligence should be viewed not merely as a technical tool, but as an effective mechanism for planning, managing, and assessing the learning process. Furthermore, the success of AI-based educational models is closely linked to the digital and methodological preparedness of educators, the level of educational infrastructure development, and the availability of appropriate regulatory and legal frameworks.

Based on the findings of this study, several practical recommendations can be proposed. First, higher education institutions should design curricula focused on developing artificial intelligence competencies and organize them on the basis of interdisciplinary integration. Alongside theoretical instruction, particular attention should be given to practical training, project-based learning, and research activities. Second, it is recommended to implement AI-based adaptive learning platforms to create personalized learning trajectories tailored to students’ individual educational needs. This approach contributes to improving the effectiveness of the learning process and enhancing learning outcomes.

In addition, ethical and legal issues related to the use of artificial intelligence in education should be incorporated into academic curricula. Emphasizing topics such as algorithmic fairness, data security, and privacy protection promotes the formation of responsible technological thinking among students. Moreover, the establishment of professional development programs for educators focused on the use of artificial intelligence technologies represents an important factor in improving the overall quality of education.

In summary, the systematic and consistent integration of artificial intelligence into the education system facilitates the preparation of competitive, innovative, and socially responsible professionals capable of meeting the demands of a digital society. The conclusions and recommendations presented in this study may serve as a methodological foundation for the implementation of AI-based educational models in higher education institutions and hold significant scientific and practical value for future research in this field.

References:

1. Chen, L., Xie, H., & Hwang, G. J. (2020). A multi-perspective analysis of artificial intelligence in education: A systematic review. Educational Technology & Society, 23(4), 1–15.
2. Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
3. Ghamrawi, N. (2023). Competency-based education in the age of artificial intelligence. International Journal of Educational Research, 118, 102147. https://doi.org/10.1016/j.ijer.2023.102147
4. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Boston: Center for Curriculum Redesign.
5. Karimov, A. A. (2022). Raqamli ta’lim muhitida innovatsion pedagogik texnologiyalar. Toshkent: O‘zbekiston Milliy universiteti nashriyoti.
6. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. London: Pearson Education.
7. UNESCO. (2021). Ethics of artificial intelligence in education: Policy guidance. Paris: UNESCO Publishing.
8. Usmonov, B. R. (2023). Oliy ta’limda sun’iy intellekt texnologiyalarini joriy etish masalalari. Oliy ta’lim muammolari jurnali, 2(3), 45–52.
9. Walter, S. (2024). Artificial intelligence and student competency development: Educational perspectives. Computers & Education: Artificial Intelligence, 5, 100158. https://doi.org/10.1016/j.caeai.2024.100158
10. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39). https://doi.org/10.1186/s41239-019-0171-0
11. Berdiev G.R., Norboev B.U., Normurodov B.B. USING AI TO MAKE A SOPHISTICATED DECISION-MAKING SYSTEM THAT CAN ADVISE YOU IN REAL TIME WHETHER TO STOP OR KEEP GOING AT PEDESTRIAN CROSSINGS // Universum: технические науки : электрон. научн. журн. 2026. 1(142). URL: https://7universum.com/ru/tech/archive/item/21672 (дата обращения: 28.12.2025).
12. Shoniyozova YQ, Turdiyeva MA, and Norboyev BU TARMOQ HUJUMLARINING OLDINI OLISH TIZIMLARI // INNOVATSION MEXANIZMLAR VA STRATEGIK USTIBORIYATLAR ILMIY. - 2020. - 46-bet