The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact. Across many disciplines, enhancing the quality of mathematics education is crucial in building critical thinking and problem-solving skills at younger ages. Conversational AI systems have started maturing to a point where they could play a significant role in helping students learn fundamental math concepts. This work presents a task-oriented Spoken Dialogue System (SDS) built to support play-based learning of basic math concepts for early childhood education. The system has been evaluated via real-world deployments at school while the students are practicing early math concepts with multimodal interactions. We discuss our efforts to improve the SDS pipeline built for math learning, for which we explore utilizing MathBERT representations for potential enhancement to the Natural Language Understanding (NLU) module. We perform an end-to-end evaluation using real-world deployment outputs from the Automatic Speech Recognition (ASR), Intent Recognition, and Dialogue Manager (DM) components to understand how error propagation affects the overall performance in real-world scenarios.
翻译:用于建设教育应用的基于语言的人工智能(AI)技术的进步,可以带来具有更广泛积极影响的社会良机。在许多学科中,提高数学教育的质量对于在更年轻的时候培养批判性思维和解决问题的技能至关重要。相互交流的人工智能系统已经开始成熟,在帮助学生学习基本数学概念方面可以发挥重要作用。这项工作是一个面向任务的、以玩耍为基础学习幼儿教育基本数学概念的假话对话系统(SDS),该系统是通过学校实际部署来评估的,而学生们则在与多式联运进行早期数学互动。我们讨论了我们改进为数学学习而建造的SDS管道的努力,为此我们探索利用数学、生物、生物、生物、科学、科学、科学、科学、科学、科学、对话管理等模块,以潜在地加强自然语言理解模块。我们利用自动语音识别、认知和对话管理员(DM)的实时部署产出,进行端到端评价,以了解错误传播如何影响现实世界情景的总体表现。