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<p>Deaf and Hard of Hearing students face persistent educational inequities due to sensory barriers in traditional pedagogy. Intelligent multimedia systems show potential for perceptual compensation but lack evidence-based frameworks. This study examines how multimodal integration in intelligent systems enhances learning through three dimensions: knowledge transfer efficiency, cognitive engagement, and ecological adaptability. A mixed-methods design was implemented with 132 DHH students (10–18&#xa0;years) from three East China special education schools. The experimental group (<i>n</i> = 66) used an AI-driven system with visual-tactile feedback, while controls (<i>n</i> = 66) received traditional instruction. Data from behavioral analytics (4,320 logs), eye-tracking, and the NASA Task Load Index were analyzed via Analysis of Moment Structures (AMOS) path modeling. Dual-channel feedback increased knowledge retention by 41.7% (β = 0.37, <i>p</i> &lt; 0.01) and reduced cognitive load (d = 1.21). Adaptive interfaces improved engagement duration by 73.5% (32.7 vs. 18.9&#xa0;min/class). The system achieved 92.3% sign language recognition accuracy with 80% cost reduction in resource-scarce regions. The proposed "cognitive enhancement framework" demonstrates that technology-ecology synergy can transform special education from basic access to quality development. Findings provide empirical guidelines for WHO ICF-CY compliant educational product design.</p>

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Analysis of the impact of intelligent multimedia educational systems on the learning outcomes of deaf and hard of hearing students

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Sihan Yue,
  • Xing Tang

摘要

Deaf and Hard of Hearing students face persistent educational inequities due to sensory barriers in traditional pedagogy. Intelligent multimedia systems show potential for perceptual compensation but lack evidence-based frameworks. This study examines how multimodal integration in intelligent systems enhances learning through three dimensions: knowledge transfer efficiency, cognitive engagement, and ecological adaptability. A mixed-methods design was implemented with 132 DHH students (10–18 years) from three East China special education schools. The experimental group (n = 66) used an AI-driven system with visual-tactile feedback, while controls (n = 66) received traditional instruction. Data from behavioral analytics (4,320 logs), eye-tracking, and the NASA Task Load Index were analyzed via Analysis of Moment Structures (AMOS) path modeling. Dual-channel feedback increased knowledge retention by 41.7% (β = 0.37, p < 0.01) and reduced cognitive load (d = 1.21). Adaptive interfaces improved engagement duration by 73.5% (32.7 vs. 18.9 min/class). The system achieved 92.3% sign language recognition accuracy with 80% cost reduction in resource-scarce regions. The proposed "cognitive enhancement framework" demonstrates that technology-ecology synergy can transform special education from basic access to quality development. Findings provide empirical guidelines for WHO ICF-CY compliant educational product design.