Research on a virtual teacher personalized interaction model integrating affective computing and multi-agent systems

This research develops a novel virtual teacher personalized interaction model integrating multimodal affective computing with multi-agent coordination mechanisms to address fundamental limitations in emotional intelligence and adaptive capabilities within contemporary educational technology systems. A three-layer distributed architecture was implemented, incorporating synchronized multimodal emotion recognition through confidence-weighted fusion of facial, vocal, and textual data streams, Byzantine Fault Tolerant consensus algorithms for coordinated multi-agent decision-making, and dynamic personality adaptation mechanisms based on Big Five psychological modeling. Experimental validation employed 500 participants across diverse educational contexts using established emotion recognition benchmarks supplemented with domain-specific educational interaction datasets. The multimodal emotion fusion component achieved 91.2% recognition accuracy, with overall system performance reaching 89.7% under realistic educational conditions while demonstrating substantial educational effectiveness improvements, including 43% higher learner engagement scores, 37% emotional satisfaction enhancement, 30% learning effectiveness increase, and 40% knowledge retention improvement compared to traditional virtual teaching approaches. Multi-agent coordination exhibited superior decision quality with 31% improvement over single-agent baselines, though personality adaptation effectiveness varied significantly across learner populations with 88% success rates for extraverted individuals compared to 65% for high-neuroticism learners. The integrated approach successfully bridges the emotional intelligence gap in virtual educational systems through sophisticated technological convergence, establishing theoretical foundations for distributed educational intelligence while revealing important implementation challenges. This research enables the development of emotionally responsive virtual teachers capable of sustained personalized instruction across diverse educational contexts, though deployment requires careful consideration of privacy protection and institutional adaptation requirements for broader educational technology transformation.