ARTH: Algorithm For Reading Text Handily -- An AI Aid for People having Word Processing Issues
Project Overview
The document discusses the innovative application of generative AI in education through a program called 'ARTH', aimed at supporting individuals with word processing challenges, particularly those with mild intellectual disabilities. Utilizing natural language processing, ARTH analyzes texts to pinpoint difficult words based on syllable count and frequency of use, subsequently generating quizzes to evaluate user comprehension. This approach not only provides definitions for challenging vocabulary but also enhances the overall reading experience, promoting a greater appreciation for literature among users who typically face difficulties with language. By focusing on personalized learning and engagement, ARTH exemplifies the potential of generative AI to improve educational outcomes, fostering both understanding and enjoyment of reading for those with specific learning needs. Through these key applications, the document highlights the transformative role of AI in creating accessible and effective learning environments.
Key Applications
ARTH - Algorithm For Reading Text Handily
Context: Educational context for individuals with mild intellectual disabilities, particularly targeting those unable to progress beyond elementary schooling.
Implementation: The ARTH algorithm operates in two stages: first, it identifies difficult words in a text through a clustering algorithm, and second, it generates quizzes to evaluate user comprehension and displays definitions of challenging words.
Outcomes: Improved reading comprehension and engagement for users with word processing issues, enabling them to enjoy reading without the need to consult a dictionary.
Challenges: Challenges include accurately identifying difficult words and generating effective quizzes that cater to individual user needs.
Implementation Barriers
Technical Barrier
Challenges related to accurately identifying difficult words and assessing user comprehension.
Proposed Solutions: Potential solutions include refining the algorithm for better accuracy in word difficulty assessment and enhancing quiz generation methods.
Project Team
Akanksha Malhotra
Researcher
Sudhir Kamle
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Akanksha Malhotra, Sudhir Kamle
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai