Speed Reading Tool Powered by Artificial Intelligence for Students with ADHD, Dyslexia, or Short Attention Span

TL;DR

This paper introduces an AI-powered speed reading tool designed to help students with dyslexia, ADHD, and attention issues by utilizing NLP models, readability enhancements, and a user-friendly web application.

Contribution

It presents a novel speed reading application combining transformer-based text summarization, readability principles, and web deployment for students with learning difficulties.

Findings

Effective text summarization for quick reading

Enhanced readability through Bionic Reading principles

Successful deployment as a web-based tool

Abstract

This paper presents a novel approach to assist students with dyslexia, ADHD, and short attention span in digesting any text-based information more efficiently. The proposed solution utilizes the Multilayer Perceptron (MLP) algorithm for complex text processing and summarization tasks. The tool leverages the T5 (Text-to-Text Transfer Transformer) model from Hugging Face, which treats every NLP task as a text generation task. The model is fine-tuned on specific tasks using a smaller dataset. The NLTK's Punkt Sentence Tokenizer is used to divide a text into a list of sentences. The application is served using Flask, a lightweight web server and framework. The tool also applies principles from Bionic Reading to enhance readability, which includes a bolding function and adjustments to line, word, and character spacing. The paper discusses the methodology, implementation, and results of the AI-based speed reading tool.