用虚拟现实与机器学习识别读写困难，跨语言测试有效提升诊断效率。
Combine Virtual Reality and Machine-Learning to Identify the Presence of Dyslexia: A Cross-Linguistic Approach
- 通过虚拟现实阅读任务和自尊评估收集数据，结合机器学习分析。
- 意大利学生识别准确率达87.5%，西班牙为66.6%，合并组为75.0%。
- 速度差异是关键指标，适合教育筛查与跨语言研究者参考。
本研究探讨了虚拟现实（VR）与人工智能（AI）在意大利语和西班牙语大学生中预测读写困难（dyslexia）的存在性。具体而言，研究考察了来自无声阅读（SR）测试和自尊评估的VR数据能否区分有无读写困难的学生，并采用机器学习（ML）算法进行分析。参与者完成了基于VR的阅读表现与自尊评估任务。初步统计分析（t检验与曼-惠特尼检验）显示，读写困难组与正常组在SR测试完成时间上存在显著差异，但准确率与自尊水平无显著差别。随后训练并测试了监督式机器学习模型，在意大利学生中达到87.5%的分类准确率，西班牙学生为66.6%，合并群体为75.0%。结果表明，结合虚拟现实与机器学习可作为读写困难辅助评估工具，尤其能捕捉任务完成速度差异，但语言因素可能影响分类精度。
原文摘要 · Abstract (English)
This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t tests and Mann Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5 per cent for Italian, 66.6 per cent for Spanish, and 75.0 per cent for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy.
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