Assessment and Improvement of Avatar-Based Learning System: From Linguistic Structure Alignment to Sentiment-Driven Expressions.

This research investigates the improvement of learning systems that utilize avatars by shifting from elementary language compatibility to emotion-driven interactions. An assessment of various instructional approaches indicated marked differences in overall effectiveness, with the system showing steady but slight improvements and little variation, suggesting it has the potential for consistent use. Analysis through one-way ANOVA identified noteworthy disparities in post-test results across different teaching strategies. However, the pairwise comparisons with Tukey's HSD did not reveal significant group differences. The group variation and limited sample sizes probably affected statistical strength. Evaluation of effect size demonstrated that the traditional approach had an edge over the avatar-based method, with lessons recorded on video displaying more moderate distinctions. The innovative nature of the system might account for its initial lower effectiveness, as students could need some time to adjust. Participants emphasized the importance of emotional authenticity and cultural adaptation, including incorporating a Kazakh accent, to boost the system's success. In response, the system was designed with sentiment-driven gestures and facial expressions to improve engagement and personalization. These findings show the potential of emotionally intelligent avatars to encourage more profound learning experiences and the significance of fine-tuning the system for widespread adoption in a modern educational context.