Abstract
This article examines the process of digitizing National Occupational Classification (NOC-2025) in Uzbekistan, developed on the basis of the International Standard Classification of Occupations (ISCO-08), and the possibilities of applying artificial intelligence technologies to it. Although this classification exists today in a national form, and its digitization and the introduction of artificial intelligence elements to it based on modern technologies remain a pressing issue. In order to digitize the classification, international systems such as the International Standard Classification of Occupations (ISCO-08, ILO), European Skills, Competences, Qualifications and Occupations (ESCO), Occupational Information Network (O*NET, USA) and National Occupational Classification (NOC, Canada) have been analysed, and their approaches to digitization, automation and the application of artificial intelligence technologies has been studied. The results of the study show that digitizing the NOC-2025 classification based on artificial intelligence will expand the possibilities for effective management of the Uzbek labour market, the quick addition of new professions to the classification, and the recognition of professional qualifications in international labour migration processes.
First Page
103
Last Page
114
References
- Kamble, S.S., Gunasekaran, A., and Gawankar, S.A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. https://doi.org/10.1016/j.psep.2018.05.009.
- Kolade, O., Owoseni, A. (2022). Employment 5.0: The work of the future and the future of work. Technology in Society, 71, 102086. https://doi.org/10.1016/J.TECHSOC.2022.102086.
- Mitchell, R., Shen, Y., Snell, L. (2022). The future of work: a systematic literature review. Accounting and Finance, 62(2), 2667-2686. https://doi.org/10.1111/ACFI.12878.
- National Academies of Sciences, Engineering, and Medicine. 2025. Artificial Intelligence and the Future of Work. Washington, DC: The National Academies Press. https://doi.org/10.17226/27644.
- Bankins, S., Hu, X., Yuan, Y. (2024). Artificial intelligence, workers, and future of work skills. Current opinion in psychology, 58, 101828.
- Schneider, P., Sting, F.J. (2020). Employees’ perspectives on digitalization-induced change: Exploring frames of industry 4.0. Academy of Management Discoveries, 6(3), 406-435.
- Kolade, O., Owoseni, A. (2022). Employment 5.0: The work of the future and the future of work. Technology in Society, 71, 102086.
- Stryzhak, O. (2023). Analysis of labor market transformation in the context of industry 4.0. Studia Universitatis Vasile Goldiş, Arad-Seria Ştiinţe Economice, 33(4), 23-44.
- Abduraxmanov K.X. (2023). Transformatsiya rinka truda v usloviyax vnedreniya iskusstvennogo intellekta [Labor market transformation in the context of the introduction of artificial intelligence]. Ekonomika truda, 10 (2), 227246. doi: 10.18334/ et.10.2.117364. (in Russian).
- Poláková, M., Suleimanová, J. H., Madzík, P., Copuš, L., Molnárová, I., & Polednová, J. (2023). Soft skills and their importance in the labour market under the conditions of Industry 5.0. Heliyon, 9(8).
- Bispo, L.G.M., Amaral, F.G. (2024). The impact of Industry 4.0 on occupational health and safety: A systematic literature review. Journal of Safety Research, 90, 254-271.
- Kergroach, S. (2017). Industry 4.0: New challenges and opportunities for the labour market. Forsayt, 11(4), 6-8.
- Abduraxmanov, K.X. (2023). Iskustvenniy intellekt – osnova ustoychivogo razvitiya eko-nomike [Artificial intelligence - the basis of sustainable development of the economy], RЕU im. G.V.Plexanova, Moscow. (in Russian).
- Begmamat, D., Sanjarbek, B., Utkirjon, U., Shohrukh, N., Umidjon, K. (2023). SMART Education Framework to Assess the Knowledge of Engineering Students. In: Cioboată, D.D. (eds) International Conference on Reliable Systems Engineering (ICoRSE) - 2023. ICoRSE 2023. Lecture Notes in Networks and Systems, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-031-40628-7_7.
- Tojiyeva, Z., Ibragimov, L. (2021). Labour market and employment in Uzbekistan. Geografický časopis. Geographical Journal, 73(4), 359-374. DOI: https://doi.org/10.31577/geogrcas.2021.73.4.19.
- O‘zMSt 641:2025 Mashgulotlarning milliy klassifikatori (MMK-2025) Milliy standarti [National Classifier of Occupations (NCC-2025) National Standard]. https://uzsti.uz/shop/33051.
- International Labour Organization (ILO), The International Standard Classification of Occupations (ISCO-08) companion guide, Geneva: ILO Publications, 2023. [Online]. Available: https://www.ilo.org/public/english/bureau/stat/isco/.
- International Labour Organization (ILO), International Standard Classification of Occupations: ISCO-08, Geneva: ILO Publications, 2012. [Online]. Available: https://www.ilo.org/public/english/bureau/stat/isco/.
- European Commission, European Skills, Competences, Qualifications and Occupations (ESCO), 2023. [Online]. Available: https://esco.ec.europa.eu.
- National Center for ONET Development, ONET Resource Center, U.S. Department of Labor/Employment and Training Administration, 2022. [Online]. Available: https://www.onetcenter.org.
- Government of Canada, National Occupational Classification (NOC) 2021 Version 1.0, Ottawa: Employment and Social Development Canada, 2021. [Online]. Available: https://www.jobbank.gc.ca/noc.
- Ospino, C. (2018). Occupations: Labor market classifications, taxonomies, and ontologies in the 21st century.
- Vrolijk, J., Mol, S. T., Weber, C., Tavakoli, M., Kismihók, G., Pelucchi, M. (2022). OntoJob: Automated ontology learning from labor market data. In 2022 IEEE 16th International Conference on Semantic Computing (ICSC). 195-200.
- Sibarani, E. M., Scerri, S., Morales, C., Auer, S., Collarana, D. (2017, September). Ontology-guided job market demand analysis: a cross-sectional study for the data science field. In Proceedings of the 13th international conference on semantic systems. 25-32.
- European Commission. ESCO: European Skills, Competences, Qualifications and Occupations. Brussels, 2020.
- European Commission, Machine Learning Assisted Mapping of Multilingual Occupational Data in ESCO (Part I & II), Brussels, 2022. [Online]. Available: https://esco.ec.europa.eu
- Peterson, N.G., Mumford, M.D., Borman, W.C., Jeanneret, P.R., Fleishman, E.A., Levin, K.Y., ... Dye, D.M. (2001). Understanding work using the Occupational Information Network (O* NET): Implications for practice and research. Personnel psychology, 54(2), 451-492.
- Mehdi, T., Morissette, R. (2024). Experimental estimates of potential artificial intelligence occupational exposure in Canada. Statistics Canada.
- Bao, H., Baker, C. J., Adisesh, A. (2020). Occupation coding of job titles: iterative development of an Automated Coding Algorithm for the Canadian National Occupation Classification (ACA-NOC). JMIR Formative Research, 4(8), e16422 (2020).
- Saudi General Authority for Statistics. Saudi Standard Classification of Occupations (SSCO). Riyadh, 2021. [Online]. Available: https://www.stats.gov.sa/en/w/saudi-standard-classification-of-occupations
- Narzullayev, Sh.N., Jalilov, A.A. (2025).Yevropa va Osiyo mamlakatlari tajribasi asosida O‘zbekistonda mashg‘ulotlar milliy klassifikatorini ishlab chiqish [Development of a national classifiyer of training in Uzbekistan based on the experiyence of European and Atsian countriyes.]. Modern Education and Development, 28(7), 221-226. Retriyeved from https://inlibrary.uz/index.php/mead/article/view/116374
- Begmamat, D., Sanjarbek, B., Utkirjon, U., Shohrukh, N., Umidjon, K. Comparative Analysis of SMART Education Framework and Traditional Assessment Techniques in Evaluating the Knowledge of Engineering Students. In: Cioboată, D.D. (eds) International Conference on Reliable Systems Engineering (ICoRSE) - 2023. Lecture Notes in Networks and Systems, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-031-40628-7_8
- World Economic Forum. (2025). The Future of Jobs Report 2025. Geneva: World Economic Forum. https://www.weforum.org/publications/the-future-of-jobs-report-2025/.
- Autor, D. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3.
- Mikolov, T., Chen, K., Corrado, G., Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
- OECD. AI in Labour Market Governance. Paris, 2021.
- Beverley, J., Smith, S., Diller, M., Duncan, W. D., Zheng, J., Judkins, J. W., ... He, Y. (2023, July). The Occupation Ontology (OccO): Building a Bridge between Global Occupational Standards. In JOWO.
- Sabiri, B., Khtira, A., El Asri, B., Rhanoui, M. (2023). Analyzing BERT's Performance Compared to Traditional Text Classification Models. In ICEIS (1) 572-582.
- Euzenat, J., Shvaiko, P. (2013). Overview of matching systems. In Ontology Matching. Berlin, Heidelberg: Springer Berlin Heidelberg.
- Vysotska, V., Berko, A., Chyrun, L., Chyrun, S., Panasyuk, V., Budz, I., Shakleina, I., Garbich-Moshora, O., Andrusyak, I. (2021). Semantic data integration methods based on ontologies in intelligent business analytics systems. CEUR Workshop Proceedings, 3722, 457-489.
- European Commission, The crosswalk between ESCO and O*NET, Brussels, 2022. [Online]. Available: https://esco.ec.europa.eu/en/about-esco/data-science-and-esco/crosswalk-between-esco-and-onet.
- Liu, J., Ng, Y.C., Gui, Z. et al. (2022). Title2Vec: a contextual job title embedding for occupational named entity recognition and other applications. J Big Data, 9, 99. https://doi.org/10.1186/s40537-022-00649-5.
- https://nexpath.eu/blog/o-net-esco-data-crosswalk/.
- Ikudo, A., Lane, J. I., Staudt, J., Weinberg, B.A. (2019). Occupational classifications: A machine learning approach. Journal of Economic and Social Measurement, 44(2-3), 57-87.
- https://www.joveo.com/blog/using-sentencebert-to-generate-job-embeddings-for-applications-at-joveo
- Shimizu, C., Hitzler, P. (2025). Accelerating knowledge graph and ontology engineering with large language models. Journal of Web Semantics, 85, 100862.
- European Commission, Linked Open Data, 2022. [Online]. Available: https://esco.ec.europa.eu/en/about-esco/escopedia/escopedia/linked-open-data
- Uljayev, E., Narzullayev, S., Shoazimova, U., Atadjanova, M., Sirojiddinov, S. (2024, November). Modeling of wheat moisture measuring device based on fuzzy logic. In AIP Conference Proceedings (Vol. 3244, No. 1, p. 030015). AIP Publishing LLC.
- Blagec, K., Barbosa-Silva, A., Ott, S., Samwald, M. (2022). A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks. Scientific Data, 9(1), 322.
- Tekdogan, T., Cakmak, A. (2021, August). Benchmarking apache spark and hadoop mapreduce on big data classification. In Proceedings of the 2021 5th International Conference on Cloud and Big Data Computing. 15-20.
- Lazarev, I. D., Narozniak, M., Byrnes, T., Pyrkov, A. N. (2025). Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map. Physical Review, 111(1), 012416.
- Poggiali, A., Berti, A., Bernasconi, A., Del Corso, G. M., Guidotti, R. (2024). Quantum clustering with k-means: A hybrid approach. Theoretical Computer Science, 992, 114466.
- Bermejo, P., Orús, R. (2023). Variational quantum and quantum-inspired clustering. Sci Rep 13, 13284. https://doi.org/10.1038/s41598-023-39771-6.
- Heshmatisafa, S., Seppänen, M. (2023). Exploring API-driven business models: Lessons learned from Amadeus's digital transformation. Digital Business, 3(1), 100055.
- Brownlee, J. (2020). How to calculate precision, recall, and F-measure for imbalanced classification. Machine learning mastery, 1.
- Narzullayev Sh.N., Jalilov A.A. (2025). Rivojlangan mamlakatlarning mashg‘ulotlar milliy klassifikatorini mehnat bozoriga joriy etish amaliyotini tahlil qilish (Qozog‘iston) [Analysis of the implementation practices of the National Classification of Occupations of developed countries into the labor market (Kazakhstan)]. World Scientific Research Journal, 42(1), 179-182 https://scientific-jl.com/wsrj/article/view/26603.
- Uljayev E., Ubaydullayev U.M., Tadjitdinov G.T., Narzullayev S. (2021) “Development of Criteria for Synthesis of the Optimal Structure of Monitoring and Control Systems”. In: Aliev R.A., Yusupbekov N.R., Kacprzyk J., Pedrycz W., Sadikoglu F.M. (eds) 11th World Conference “Intelligent System for Industrial Automation” (WCIS-2020). WCIS 2020. Advances in Intelligent Systems and Computing, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-68004-6_73E.
- De Groot, M., Schutte, J., Graus, D. (2021). Job posting-enriched knowledge graph for skills-based matching. arxiv preprint arxiv: 2109.02554.
- Achananuparp, P., Lim, E. P., & Lu, Y. (2025). A multi-stage framework with taxonomy-guided reasoning for occupation classification using large language models. arXiv preprint arxiv:2503.12989.
- Narzullayev Sh.N. (2025). Mehnat bozori raqobatbardoshligini ta’minlashda texnologik ko‘nikmalarning ahamiyati [The importance of technological skills in ensuring the competitiveness of the labor market]. DEVOS, 1(9), 377-386.
- Lennon, C., Zilian, L. S., Zilian, S. S. (2023). Digitalisation of occupations – Developing an indicator based on digital skill requirements. Plos one, 18(1), e0278281.
Recommended Citation
Narzullayev, Shohrux Nurali o‘g‘li
(2025)
"POSSIBILITIES OF DIGITIZING AND APPLYING ARTIFICIAL INTELLIGENCE TO NATIONAL OCCUPATIONAL CLASSIFICATION (NOC-2025) IN UZBEKISTAN,"
Chemical Technology, Control and Management: Vol. 2025:
Iss.
5, Article 14.
DOI: https://doi.org/10.59048/2181-1105.1681