This article describes the development of a multimodal learning system for the Kazakh language intended for digital educational environments. The study focuses on the lack of avatar-based learning systems adapted to the linguistic properties of the Kazakh language and the limited integration of verbal and non-verbal components in existing solutions. The proposed system combines syntactic and morphological text analysis with sentiment processing and intonation control. Speech synthesis, gesture generation, facial expression control, and lip synchronization are implemented within a single system architecture. Prosodic parameters are formed based on sentence structure and sentence-level emotional indicators, while visual articulation is synchronized with audio output. The system was tested in speech synthesis scenarios relevant to interactive educational use. The results show that the system can be used for automated lecture narration, voice-over of instructional materials, and basic learner interaction in avatar-based educational settings.
Ukenova et al. (Wed,) studied this question.