Abstract
Graded lexical resources aligned with the Common European Framework of Reference for Languages (CEFR) and lexical complexity prediction remain limited for low-resource Turkic languages, and the extent to which existing predictive models generalize to agglutinative morphology is unresolved. We introduce the first CEFR-graded lexicon for Kazakh, containing 4561 lemma–part-of-speech (POS) entries across A1–C1, and use it to test whether explicit morphology improves lexical complexity prediction. We compare handcrafted morphological features, XLM-RoBERTa contextual embeddings, and fusion models that combine both signal types on held-out CEFR classification. Our best model, a gated fusion of contextual embeddings with morphological features, achieves a macro-averaged F1 score of 0.360 and a mean absolute error of 1.125 on the held-out test set. Morphology provides useful information beyond character-level cues, contextual representations are strong on their own, and combining them yields the best supervised performance for this task. The paper therefore contributes a new CEFR resource for Turkic languages and evidence that morphology-aware modeling is useful for Kazakh lexical difficulty prediction. The results support Sustainable Development Goal 4 (Quality Education) by enabling objective assessment of learning-material complexity and adaptive Kazakh language learning. The derived lexicon and code are publicly available.