AI自动为多语言文本匹配合适的图标，帮助特殊需求儿童阅读
Robust Multilingual Text-to-Pictogram Mapping for Scalable Reading Rehabilitation
- 用AI动态识别关键词并匹配对应图标，跨语言适配
- 五种语言覆盖率高，专家评审准确率超95%（阿拉伯语约90%）
- 响应快，适合实时教学，特别适合教育工作者使用
阅读理解对有特殊教育需求和残疾（SEND）的儿童构成重大挑战，通常需要一对一密集辅导。为帮助治疗师扩大支持范围，我们开发了一种多语言、基于AI的界面，可自动为文本添加视觉辅助。该系统动态识别关键概念，并将其映射到上下文相关的图标，支持多种语言学习者。我们在五种类型差异大的语言（英语、法语、意大利语、西班牙语和阿拉伯语）中进行了评估，包括多语言覆盖分析、言语治疗师与特殊教育专家的临床审查以及延迟测试。结果显示，在五种语言中图标覆盖率和视觉辅助密度均较高。专家评审表明，自动生成的图标语义合理，四种欧洲语言的正确与可接受评级总和超过95%，阿拉伯语约为90%，尽管其图标库覆盖较少。系统延迟保持在适合实时教育应用的交互阈值内。这些发现支持了自动化多模态辅助在技术可行性、语义安全性和可接受性方面的有效性，有助于提升神经多样性学习者的可及性。
原文摘要 · Abstract (English)
Reading comprehension presents a significant challenge for children with Special Educational Needs and Disabilities (SEND), often requiring intensive one-on-one reading support. To assist therapists in scaling this support, we developed a multilingual, AI-powered interface that automatically enhances text with visual scaffolding. This system dynamically identifies key concepts and maps them to contextually relevant pictograms, supporting learners across languages. We evaluated the system across five typologically diverse languages (English, French, Italian, Spanish, and Arabic), through multilingual coverage analysis, expert clinical review by speech therapists and special education professionals, and latency assessment. Evaluation results indicate high pictogram coverage and visual scaffolding density across the five languages. Expert audits suggested that automatically selected pictograms were semantically appropriate, with combined correct and acceptable ratings exceeding 95% for the four European languages and approximately 90% for Arabic despite reduced pictogram repository coverage. System latency remained within interactive thresholds suitable for real-time educational use. These findings support the technical viability, semantic safety, and acceptability of automated multimodal scaffolding to improve accessibility for neurodiverse learners.
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