ENHANCING EDUCATIONAL COMPETENCY FRAMEWORKS IN KAZAKHSTAN THROUGH A HYBRID BLOOM’S TAXONOMY AND GENERATIVE AI APPROACH WITH PCA-BASED VALIDATION

Ainur Mukashova, Muslim Sergaziyev, Ayagoz Mukhanova, Zhanat Kenzhebayeva, Ainur Shekerbek, A. Zh. Akhmetova

Scientific Journal of Astana IT University, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.37943/ilrf5900
Data confidence 85 high
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Abstract

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.