SBIR Phase I: Adaptive Dialog Systems As Second Language Learning Partners

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响集中在提供一个成本更低，更容易获得的替代方案，以帮助世界各地的人们学习说一种新的语言。通过使用最先进的会话人工智能（AI），该项目为学生提供个性化的，基于知识的学习，每天24小时都可以负担得起，并且对他们可以练习的时间没有限制。此外，人工智能系统将能够分析学习者的语音，以提供有关如何提高语法和会话流利性的建议，就像训练有素的导师帮助学生一样。该项目将对话式人工智能引入语言学习市场，使其成为一种更有效、更有趣、更低成本的产品，以改变当前的语言学习模式。这个小型企业创新研究（SBIR）第一阶段项目是一项创新的整合努力，旨在将最先进的联合收割机人工智能技术结合起来，用于教育应用。该项目将为语言学习任务的对话策略培训提供新的用户自适应算法和数据集。该项目将有助于该公司的开源，多模态，对话系统框架，使对话社区，以减少进入障碍的多模态对话系统的研究。该项目还将为社区提供用于语法错误纠正的注释会话数据集沿着经过训练的语法纠正模型和语法错误类型检测器。开发的第二语言交际学习的教育对话系统也可以作为教育专家测试不同学习理论的一般框架。该项目还将探讨是否适应性会话经验和不同的反馈结构会影响学习者的信心和学习增益的假设。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project centers on offering a lower-cost, more accessible alternative to help people everywhere learn to speak a new language. By using state-of-the-art conversational artificial intelligence (AI), the project offers students individualized, curriculum-based learning that is affordable and accessible 24 hours a day, with no limits on the amount of time they can practice. Additionally, the AI system will be able to analyze learners’ speech to offer suggestions on ways to improve their grammar and conversational fluency, in much the same way a trained tutor would assist a student. The project brings conversational AI to the language learning market, enabling a more effective, entertaining, and low-cost product to transform the current language learning paradigm.This Small Business Innovation Research (SBIR) Phase I project is an innovative integration effort to combine state-of-the-art AI technologies for education applications. The project will contribute new user-adaptive algorithms and datasets for dialog policy training on language learning tasks. The project will contribute to the company’s open-source, multimodal, dialog system framework to enable the dialog community to reduce the entry barrier for multimodal dialog system research. The project will also provide the community with an annotated conversational dataset for grammatical error correction along with a trained grammar correction model and grammar error type detector. The developed educational dialog systems for second language communication learning can also serve as a general framework for education experts to test different learning theories. This project will also explore hypotheses of whether adaptive conversation experience and different feedback structures will impact learner confidence and learning gain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.