DyslexiEase: Enhancing Dyslexic Learning Through Deep Learning-Based Multimedia Content Generation
According to research, around 10-15 percent of students have some form of dyslexia. This neurological disorder necessitates addressing it at a younger age in order to deal with its consequences quickly and effectively. This paper presents DyslexiEase, an innovative, child-friendly multimedia content...
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| Published in: | 2025 International Conference on Computer Technology Applications (ICCTA) pp. 70 - 78 |
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| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
21.05.2025
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| Abstract | According to research, around 10-15 percent of students have some form of dyslexia. This neurological disorder necessitates addressing it at a younger age in order to deal with its consequences quickly and effectively. This paper presents DyslexiEase, an innovative, child-friendly multimedia content generation tool designed to enhance classroom experiences for dyslexic students by incorporating a multimedia learning approach. By leveraging generative models, DyslexiEase transforms textual prompts into multimedia learning aids. Subject extraction is performed to identify key concepts, which are used by a diffusion model to generate reference images. Simultaneously, a self-trained Generative Adversarial Network (GAN) on the CleanVid15M dataset produces contextual videos, while Tacotron2 generates high-quality speech narrations. Additionally, a Conditional Variational Autoencoder (CVAE) creates captioned images using the OpenDyslexic font, with commonly misread letters highlighted for improved readability. The tool's intuitive interface helps educators tailor content to students' needs, promoting a more inclusive and engaging learning experience. |
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| AbstractList | According to research, around 10-15 percent of students have some form of dyslexia. This neurological disorder necessitates addressing it at a younger age in order to deal with its consequences quickly and effectively. This paper presents DyslexiEase, an innovative, child-friendly multimedia content generation tool designed to enhance classroom experiences for dyslexic students by incorporating a multimedia learning approach. By leveraging generative models, DyslexiEase transforms textual prompts into multimedia learning aids. Subject extraction is performed to identify key concepts, which are used by a diffusion model to generate reference images. Simultaneously, a self-trained Generative Adversarial Network (GAN) on the CleanVid15M dataset produces contextual videos, while Tacotron2 generates high-quality speech narrations. Additionally, a Conditional Variational Autoencoder (CVAE) creates captioned images using the OpenDyslexic font, with commonly misread letters highlighted for improved readability. The tool's intuitive interface helps educators tailor content to students' needs, promoting a more inclusive and engaging learning experience. |
| Author | Jandhyala, Ananya Agarwal, Pooja Krishnan, Gokul Rao, Dhruthi Sandur, Anshu |
| Author_xml | – sequence: 1 givenname: Gokul surname: Krishnan fullname: Krishnan, Gokul email: krishnangokul1029@gmail.com organization: PES Univeristy,Dept. of CSE,Bangalore,India – sequence: 2 givenname: Dhruthi surname: Rao fullname: Rao, Dhruthi email: kvdhruthi@gmail.com organization: PES Univeristy,Dept. of CSE,Bangalore,India – sequence: 3 givenname: Anshu surname: Sandur fullname: Sandur, Anshu email: anshuuu362@gmail.com organization: PES Univeristy,Dept. of CSE,Bangalore,India – sequence: 4 givenname: Ananya surname: Jandhyala fullname: Jandhyala, Ananya email: anij0110@gmail.com organization: PES Univeristy,Dept. of CSE,Bangalore,India – sequence: 5 givenname: Pooja surname: Agarwal fullname: Agarwal, Pooja email: poojaagarwal@pes.edu organization: PES Univeristy,Dept. of CSE,Bangalore,India |
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| Snippet | According to research, around 10-15 percent of students have some form of dyslexia. This neurological disorder necessitates addressing it at a younger age in... |
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| SubjectTerms | Adaptation models AI for Special Education Computational modeling Dyslexia Dyslexia Assistive Tools Generative adversarial networks Generative Models Multimedia Learning Streaming media Text to image Text to speech Transforms Videos Visualization |
| Title | DyslexiEase: Enhancing Dyslexic Learning Through Deep Learning-Based Multimedia Content Generation |
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