Building a Personalized Learning Model in a Virtual Environment for Learning the Kazakh Language

Recent developments in digital technologies and pedagogically innovative approaches have transformed traditional education into personalized and adaptive learning. The paper proposes a new framework embedding VR with advanced machine learning for the underrepresented Kazakh language acquisition. Presented model will be dynamically adapted to individual learner profiles by tailoring content based on prior knowledge, learning preference, and real-time performance feedback. Quantitative results are obtained using interaction logs, response times, accuracy rates, and standardized language assessments, while qualitative insights obtained from user feedback and observational notes are used in a pilot study involving diverse participants. Evaluations from our pilot study indicate substantial improvements: vocabulary proficiency increased by approximately 25%, grammatical accuracy by 20%, listening comprehension by 18%, and speaking fluency by 30%. Moreover, the adaptive recommendation system-either by using collaborative filtering and reinforcement learning-effectively modulated task difficulty, sustaining an optimal learning curve. Although fine-tuning the reward function of reinforcement learning and scaling with limited participant samples proved challenging, our results emphasize the role that could be played by fully immersive, data-driven learning environments in the transformation of language learning. This work helps not only with the preservation of Kazakh cultural heritage but also sets up a feasible framework for broader applications in diverse languages and modern educational contexts.