发表机构
Independent Researcher（独立研究者）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对视障儿童教育技术依赖视觉界面的问题，提出Kutti AI语音优先学习伙伴。它有实时学习困难检测、跨语言答案匹配及离线语音管道三种机制，能降低无障碍性和经济障碍，助力视障儿童早期教育。
AI 中文摘要
全球大多数儿童教育技术都围绕视觉界面构建，这将全球众多视力障碍儿童排除在外，估计有140万儿童失明，更多儿童视力低下。我们提出了Kutti AI，这是一款语音优先的学习伙伴，音频是主要且充分的界面：儿童通过口语对话学习课程概念、用语音回应并接收语音反馈，无需依赖视觉元素。该系统为在普通移动硬件上实现无障碍、自适应学习提供了三种实用机制：一是多信号学习困难检测引擎，结合响应延迟分析、错误尝试跟踪和基于关键词的犹豫检测，实时决定何时提供提示或简化问题；二是多层跨语言答案匹配管道，结合语言感知翻译/转写、基于莱文斯坦距离的模糊匹配和文本规范化，使儿童不因代码切换或发音变化而受罚；三是使用设备上自动语音识别模型的离线优先语音管道，可在服务不足社区常见的低连接环境中使用。我们描述了其架构、交互流程和优先考虑无障碍性的设计决策，并报告了支持英语和泰米尔语的黑客马拉松原型的定性观察结果。我们讨论了经验教训，并概述了对目标用户进行正式评估的路径。Kutti AI说明了一个精心设计的小型语音优先系统如何降低早期教育的无障碍性和经济障碍。
英文摘要
Most educational technology for children is built around visual interfaces, which excludes the many children worldwide who live with visual impairment -- an estimated 1.4 million children are blind and many more have low vision. We present Kutti AI, a voice-first learning companion designed so that audio is the primary and sufficient interface: children learn curriculum concepts through spoken conversation, respond by speaking, and receive spoken feedback, with no reliance on visual elements. The system contributes three practical mechanisms for accessible, adaptive learning on commodity mobile hardware: (1) a multi-signal struggle-detection engine that combines response-latency analysis, wrong-attempt tracking, and keyword-based hesitation detection to decide, in real time, when to offer hints or simplify a question; (2) a multi-layered cross-language answer-matching pipeline that combines language-aware translation/transliteration, Levenshtein-based fuzzy matching, and text normalization so that children are not penalized for code-switching or pronunciation variation; and (3) an offline-first speech pipeline using an on-device automatic speech recognition (ASR) model, enabling use in low-connectivity settings common in underserved communities. We describe the architecture, the interaction flow, and the design decisions that prioritize accessibility, and we report qualitative observations from a hackathon prototype supporting English and Tamil. We discuss lessons learned and outline a path toward formal evaluation with target users. Kutti AI illustrates how a small, carefully-engineered voice-first system can lower both accessibility and financial barriers to early education.
Comments8 pages. Voice-first, offline-capable learning companion for visually-impaired children with multi-signal struggle detection and cross-language answer matching. Prototype built at the Half Baked hackathon (English and Tamil)
