INTERFACE DESIGN OF AN INTELLIGENT INTERACTIVE LEARNING SYSTEM

Аru Ukenova, Gulmira Bekmanova, Banu Yergesh, Mamyr Altaibek

Scientific Journal of Astana IT University, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.37943/24vewa2615
Data confidence 85 high
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Abstract

This study presents the design, implementation, and evaluation of an Intelligent Interactive Learning System that employs multimodal interaction to improve the adaptability, accessibility, and engagement of digital education. Conventional e-learning platforms typically rely on static and text-based resources, which restrict personalization and reduce learner motivation. The proposed system integrates natural language processing, speech synthesis, and avatar-based interfaces to deliver lectures through synchronized speech, gestures, and facial expressions. The system automatically processes uploaded lecture scripts and slide presentations, segmenting and aligning them to generate interactive video lectures. A novel contribution of this work is the incorporation of customized Kazakh-language support, implemented through intonation modeling, dependency parsing, and gesture mapping to enhance inclusivity for underrepresented linguistic communities. The system performance was evaluated using Facebook’s variational inference text-to-speech model. Experimental results demonstrate real-time capability, with an average latency of 25.5 ms, throughput exceeding 4,200 characters per second, and low computational resource requirements. These findings confirm the suitability of the system for deployment in resource-constrained environments without compromising speech quality or responsiveness. Compared with conventional tutoring and static e-learning platforms, the system additionally provides automated assessment generation, multimodal feedback, and accessibility functions such as subtitles and adjustable playback controls. The study contributes a scalable model for intelligent, avatar-based learning that integrates speech synthesis, real-time interaction, and cultural-linguistic inclusivity. Future work will focus on extending personalization through adaptive learner modeling, incorporating affective computing for emotion-sensitive interaction, and enabling interoperability with established learning management systems.