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This paper investigates the role of artificial intelligence (AI) in promoting inclusive education, specifically for students with hearing and speech impairments. We introduce and evaluate “LearnHack,” an innovative application designed to enhance communication in classrooms by translating sign language into text and speech in real-time. LearnHack utilizes advanced AI techniques, including computer vision, natural language processing (NLP) [1], and neural networks, to improve accessibility for students with disabilities. Our preliminary findings suggest that this tool significantly enhances classroom interaction, minimizes dependence on human interpreters, and reduces cognitive strain for both educators and learners. By bridging communication gaps, LearnHack fosters a more inclusive learning environment. Existing AI-based tools either rely on expensive hardware, lack educational integration, or fail to provide contextual interpretation of sign language in real-time. LearnHack addresses these limitations by offering a lightweight, browser-based solution designed for classroom interaction. This tool aims to solve key barriers to inclusive education, such as limited interpreter availability and high cognitive load during communication, making it a practical and scalable approach to accessibility in learning environments.

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LearnHack an Artificial Intelligence Application to Improve Learning for People with Hearing or Language Disabilities

  • 会议论文

  • 发表日期：

    2025年

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  • Lucía de la Vega Encinas,
  • Hugo de Castro Roncero,
  • Sofía Alejandra García Arrojo,
  • Udane Murgia Aramendi

摘要

This paper investigates the role of artificial intelligence (AI) in promoting inclusive education, specifically for students with hearing and speech impairments. We introduce and evaluate “LearnHack,” an innovative application designed to enhance communication in classrooms by translating sign language into text and speech in real-time. LearnHack utilizes advanced AI techniques, including computer vision, natural language processing (NLP) [1], and neural networks, to improve accessibility for students with disabilities. Our preliminary findings suggest that this tool significantly enhances classroom interaction, minimizes dependence on human interpreters, and reduces cognitive strain for both educators and learners. By bridging communication gaps, LearnHack fosters a more inclusive learning environment. Existing AI-based tools either rely on expensive hardware, lack educational integration, or fail to provide contextual interpretation of sign language in real-time. LearnHack addresses these limitations by offering a lightweight, browser-based solution designed for classroom interaction. This tool aims to solve key barriers to inclusive education, such as limited interpreter availability and high cognitive load during communication, making it a practical and scalable approach to accessibility in learning environments.