AI for Accessible Education: Personalized Audio-Based Learning for Blind Students

• URL: http://arxiv.org/abs/2504.17117v1
• Date: Wed, 23 Apr 2025 21:57:53 GMT
• Title: AI for Accessible Education: Personalized Audio-Based Learning for Blind Students
• Authors: Crystal Yang, Paul Taele,
• Abstract summary: This paper presents Audemy, an AI-powered audio-based learning platform for blind and visually impaired (BVI) students.<n>Audemy uses adaptive learning techniques to customize content based on student accuracy, pacing preferences, and engagement patterns.
• Score: 2.9951933774126993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blind and visually impaired (BVI) students face significant challenges in traditional educational settings. While screen readers and braille materials offer some accessibility, they often lack interactivity and real-time adaptability to individual learning needs. This paper presents Audemy, an AI-powered audio-based learning platform designed to provide personalized, accessible, and engaging educational experiences for BVI students. Audemy uses adaptive learning techniques to customize content based on student accuracy, pacing preferences, and engagement patterns. The platform has been iteratively developed with input from over 20 educators specializing in accessibility and currently serves over 2,000 BVI students. Educator insights show key considerations for accessible AI, including the importance of engagement, intuitive design, compatibility with existing assistive technologies, and the role of positive reinforcement in maintaining student motivation. Beyond accessibility, this paper explores the ethical implications of AI in education, emphasizing data privacy, security, and transparency. Audemy demonstrates how AI can empower BVI students with personalized and equitable learning opportunities, advancing the broader goal of inclusive education.

Related papers

• INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models [0.7499722271664147]
  INSIGHT is a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises.<n>We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions.
  arXiv  Detail & Related papers  (2025-04-24T15:47:20Z)
• Form-Substance Discrimination: Concept, Cognition, and Pedagogy [55.2480439325792]
  This paper examines form-substance discrimination as an essential learning outcome for curriculum development in higher education.<n>We propose practical strategies for fostering this ability through curriculum design, assessment practices, and explicit instruction.
  arXiv  Detail & Related papers  (2025-04-01T04:15:56Z)
• Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users [42.132487737233845]
  This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify adoption patterns and key challenges users face with such technologies.
  arXiv  Detail & Related papers  (2025-03-28T16:54:25Z)
• Assessment of Personalized Learning in Immersive and Intelligent Virtual Classroom on Student Engagement [5.982610439839458]
  The study aims to provide insights into how personalized learning approaches can enhance student participation, motivation, and academic performance.<n>The eye movement paradigm has the potential to assess student engagement and promote better educational outcomes.
  arXiv  Detail & Related papers  (2025-01-14T06:49:22Z)
• Understanding Student Acceptance, Trust, and Attitudes Toward AI-Generated Images for Educational Purposes [1.0878040851637998]
  This study assesses students' acceptance, trust, and positive attitudes towards AI-generated images for educational tasks. The results reveal high acceptance, trust, and positive attitudes among students who value the ease of use and potential academic benefits. These findings suggest a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues.
  arXiv  Detail & Related papers  (2024-11-24T04:39:48Z)
• I would love this to be like an assistant, not the teacher: a voice of the customer perspective of what distance learning students want from an Artificial Intelligence Digital Assistant [0.0]
  This study examined the perceptions of ten online and distance learning students regarding the design of a hypothetical AI Digital Assistant (AIDA) All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalisation and accessibility, together with emotional and social support. Students' concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education.
  arXiv  Detail & Related papers  (2024-02-16T08:10:41Z)
• Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education [0.2812395851874055]
  This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA) for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform.
  arXiv  Detail & Related papers  (2023-09-19T19:31:15Z)
• AGI: Artificial General Intelligence for Education [41.45039606933712]
  This position paper reviews artificial general intelligence (AGI)'s key concepts, capabilities, scope, and potential within future education. It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures. The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions.
  arXiv  Detail & Related papers  (2023-04-24T22:31:59Z)
• Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task [32.6507926764587]
  Reinforcement learning could be a key tool to reduce the development cost and improve the effectiveness of intelligent tutoring software. We show that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume.
  arXiv  Detail & Related papers  (2023-04-11T02:11:24Z)
• A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions [41.647427931578335]
  AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience. We present the results of a comparative head-to-head study on learning outcomes for two popular online learning platforms.
  arXiv  Detail & Related papers  (2022-03-03T18:55:33Z)
• Comparative Study of Learning Outcomes for Online Learning Platforms [47.5164159412965]
  Personalization and active learning are key aspects to successful learning. We run a comparative head-to-head study of learning outcomes for two popular online learning platforms.
  arXiv  Detail & Related papers  (2021-04-15T20:40:24Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.