Let AI Read First: Enhancing Reading Abilities for Individuals with Dyslexia through Artificial Intelligence
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916669989847040 |
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| author | Zhao, Sihang Xiong, Shoucong Carol Pang, Bo Tang, Xiaoying He, Pinjia |
| author_facet | Zhao, Sihang Xiong, Shoucong Carol Pang, Bo Tang, Xiaoying He, Pinjia |
| contents | Dyslexia, a neurological condition affecting approximately 12% of the global population, presents significant challenges to reading ability and quality of life. Existing assistive technologies are limited by factors such as unsuitability for quiet environments, high costs, and the risk of distorting meaning or failing to provide real-time support. To address these issues, we introduce LARF (Let AI Read First), the first strategy that employs large language models to annotate text and enhance readability while preserving the original content. We evaluated LARF in a large-scale between-subjects experiment, involving 150 participants with dyslexia. The results show that LARF significantly improves reading performance and experience for individuals with dyslexia. Results also prove that LARF is particularly helpful for participants with more severe reading difficulties. Furthermore, this work discusses potential research directions opened up by LARF for the HCI community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Let AI Read First: Enhancing Reading Abilities for Individuals with Dyslexia through Artificial Intelligence Zhao, Sihang Xiong, Shoucong Carol Pang, Bo Tang, Xiaoying He, Pinjia Human-Computer Interaction Dyslexia, a neurological condition affecting approximately 12% of the global population, presents significant challenges to reading ability and quality of life. Existing assistive technologies are limited by factors such as unsuitability for quiet environments, high costs, and the risk of distorting meaning or failing to provide real-time support. To address these issues, we introduce LARF (Let AI Read First), the first strategy that employs large language models to annotate text and enhance readability while preserving the original content. We evaluated LARF in a large-scale between-subjects experiment, involving 150 participants with dyslexia. The results show that LARF significantly improves reading performance and experience for individuals with dyslexia. Results also prove that LARF is particularly helpful for participants with more severe reading difficulties. Furthermore, this work discusses potential research directions opened up by LARF for the HCI community. |
| title | Let AI Read First: Enhancing Reading Abilities for Individuals with Dyslexia through Artificial Intelligence |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.00941 |