ENHANCING EDUCATIONAL COMPETENCY FRAMEWORKS IN KAZAKHSTAN THROUGH A HYBRID BLOOM’S TAXONOMY AND GENERATIVE AI APPROACH WITH PCA-BASED VALIDATION
DOI:
https://doi.org/10.37943/ILRF5900Keywords:
generative artificial intelligence , educational standards , competencies , Bloom’s taxonomy , principal component analysis (PCA) , machine learning , competency-based educationAbstract
Competency-based education frameworks require clear differentiation of learning outcomes across qualification levels to support effective curriculum design and ensure alignment with dynamic labor market requirements. However, national professional standards frequently contain overlapping, ambiguous, or weakly differentiated competency statements, which complicates the systematic development of educational programs. This study proposes a hybrid methodology that integrates the hierarchical framework of Bloom’s Taxonomy with generative artificial intelligence to improve the structural differentiation of competency statements within Kazakhstan’s professional standards.
A comprehensive dataset of 595 professional standards containing 8,736 competency records was compiled and analyzed using natural language processing techniques. The textual competencies were vectorized utilizing TF-IDF and examined through Principal Component Analysis (PCA) to assess structural differentiation across qualification levels. To address existing gaps and enrich the dataset, 4,024 additional competencies for advanced qualification levels 6-8 were generated using GPT-4. This process was guided by Bloom’s Taxonomy action verbs to accurately reflect progressive cognitive complexity and depth of knowledge.
Comparative analysis conducted before and after data augmentation showed a moderate increase in explained variance in the first principal components, rising from approximately 4.5% to 7.5%. This shift suggests improved structural differentiation within the augmented competency dataset. Additionally, classification experiments using multiple machine-learning models demonstrated notably enhanced performance metrics after augmentation, indicating greater separability of competency statements across the targeted qualification levels.
The findings suggest that combining established pedagogical taxonomies with generative AI can efficiently support the systematic refinement of competency frameworks. Rather than replacing expert curriculum designers, generative AI functions as a powerful analytical support tool for identifying structural gaps and improving competency differentiation within large collections of educational standards.
References
Yaskevich В., Kuspangaliev B., Tagliabue L., & Umar Т. (2024). Designing a master degree program in building information modeling (BIM) using national professional standards: a case study in Kazakhstan. Bulletin of L.N. Gumilyov Eurasian National University Technical Science and Technology Series, 149(4), 326–350. https://doi.org/10.32523/2616-7263-2024-149-4-326-350
Magauova, A., Makhambetova, Z., Iskakova, A., Yessim, U., & Narkulova, B. (2021). Experience in developing professional competences of social pedagogues in higher educational institutions of Kazakhstan. 98, 01001. https://doi.org/10.1051/SHSCONF/20219801001
Li, G. & Qin, Y. (2024). An Exploration of the Application of Principal Component Analysis in Big Data Processing. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0664
Ramasubramanian, S., Sowmyarani, C. N., Athreya, A. J., Devarajan, A., Shankar, A. U., & Kumar, R. (2024, May). Data dimensionality reduction using principal component analysis: a case study. In 2024 1st International Conference on Communications and Computer Science (InCCCS) (pp. 1-6). IEEE. https://doi.org/10.1109/InCCCS60947.2024.10593421.
Tonin, F., Tao, Q., Patrinos, P., & Suykens, J. A. K. (2024). Deep Kernel Principal Component Analysis for multi-level feature learning. Neural Networks, 170, 578–595. https://doi.org/10.1016/j.neunet.2023.11.045
Allam, H., Makubvure, L., Gyamfi, B., Graham, K. N., & Akinwolere, K. (2025). Text classification: How machine learning is revolutionizing text categorization. Information, 16(2), 130. https://doi.org/10.3390/info16020130
Liu, O. L. (2023). Assessing student learning outcomes of higher education (pp. 283–295). Edward Elgar Publishing. https://doi.org/10.4337/9781802204193.00029
Nazina, L. I., Kleymenova, N. L., Nikulcheva, O. S., Pegina, A. N., Pribytkova, O. V., & Egorova, G. N. (2020). Assessment of Learning Outcomes in the University Quality Management System. Conference Digital Economy, 478-483. https://doi.org/10.2991/AEBMR.K.200730.087
Adhikari, Y. (2024). A Review of Revised Bloom’s Taxonomy of Educational Objectives. Education Review Journal, 1(1), 115-126. https://doi.org/10.3126/erj.v1i1.82852
Gfrerer, Margareth. (2026). Bloom’s Taxonomy to Innovate Learning Objectives at Ethiopian University of Applied Sciences Study Programmes. https://link.springer.com/chapter/10.1007/978-3-032-18891-5_31
Calma, A., & Cotronei-Baird, V. (2021). Assessing critical thinking in business education: Key issues and practical solutions. The International Journal of Management Education, 19(3), 100531. https://doi.org/10.1016/j.ijme.2021.100531
Shaw, C. S., & Holmes, K. E. (2014). Critical thinking and online supplemental instruction: A case study. Learning Assistance Review, 19(1), 99–119. https://eric.ed.gov/?id=EJ1034026
Shaikh, F. (2025). Triangulating Effective Teaching: Flipped Learning, Bloom’s Taxonomy and Outcome Based Education in Review. International Journal of Psychological Science, 5(1), 8–17. https://doi.org/10.11648/j.ijps.20250501.12
Gonsalves, C. (2024). Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy. Journal of Marketing Education. https://doi.org/10.1177/02734753241305980
Elim, E. H. S. Y. (2026). Promoting cognitive skills in AI-supported learning environments: the integration of bloom’s taxonomy. Education 3-13, 54(3), 612–622. https://doi.org/10.1080/03004279.2024.2332469
Helal, M. Y., Elgendy, I. A., Albashrawi, M. A., Dwivedi, Y. K., Al-Ahmadi, M. S., & Jeon, I. (2025). The impact of generative AI on critical thinking skills: a systematic review, conceptual framework and future research directions. Information Discovery and Delivery. https://doi.org/10.1108/IDD-05-2025-0125
Waladi, C. & Lamarti, M. S. (2024). Adaptive AI-Driven Assessment for Competency-Based Learning Scenarios. In M. Khaldi (Ed.), Innovative Instructional Design Methods and Tools for Improved Teaching (pp. 215-226). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-3128-6.ch010
Lindín, C. (2024). Estrategias para la incorporación de la inteligencia artificial en educación a partir de ChatGPT: Oportunidades y dilemas para profesorado, alumnado e investigación-publicación. Didacticae. https://doi.org/10.1344/did.43107
Lubbe, A., Marais, E., & Kruger, D. (2025). Cultivating independent thinkers: The triad of artificial intelligence, Bloom’s taxonomy and critical thinking in assessment pedagogy. Education and Information Technologies, 30(12), 17589-17622. https://doi.org/10.1007/s10639-025-13476-x
Mukashova, A. (2025). Professional Standards Structured Dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/ainurmukashova/professional-standards-structured-dataset
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