The emergence of ChatGPT has been rapid, and although it has demonstrated
positive impacts in certain domains, its influence is not universally
advantageous. Our analysis focuses on ChatGPT's capabilities in Mathematics
Education, particularly in teaching basic Linear Algebra. While there are
instances where ChatGPT delivers accurate and well-motivated answers, it is
crucial to recognize numerous cases where it makes significant mathematical
errors and fails in logical inference. These occurrences raise concerns
regarding the system's genuine understanding of mathematics, as it appears to
rely more on visual patterns rather than true comprehension. Additionally, the
suitability of ChatGPT as a teacher for students also warrants consideration.

本文探讨了 ChatGPT 在数学教育中的能力，尤其是在基本线性代数的教学方面。虽然有时 ChatGPT 可以提供准确且有动机的答案，但显然它更多地依赖于视觉模式而非真正的理解，而且作为学生的老师是否合适也值得考虑。

ChatGPT 在州医学许可考试方面表现出色，但在基础线性代数方面表现不一

ChatGPT may excel in States Medical Licensing Examination but falters in  basic Linear Algebra

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.

本研究采用 Spoken Dialogue System 通过多模式互动为幼儿教育提供基础数学概念的学习，使用 MathBERT 来增强自然语言理解模块，对于自动语音识别（ASR）、意图识别和对话管理器（DM）模块的实际部署输出进行端到端评估，以了解错误传播如何影响实际场景的综合表现。

面向基础数学学习的口语对话系统的端到端评估

End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics

TalkMoves is an innovative application designed to support K-12 mathematics
teachers to reflect on, and continuously improve their instructional practices.
This application combines state-of-the-art natural language processing
capabilities with automated speech recognition to automatically analyze
classroom recordings and provide teachers with personalized feedback on their
use of specific types of discourse aimed at broadening and deepening classroom
conversations about mathematics. These specific discourse strategies are
referred to as "talk moves" within the mathematics education community and
prior research has documented the ways in which systematic use of these
discourse strategies can positively impact student engagement and learning. In
this article, we describe the TalkMoves application's cloud-based
infrastructure for managing and processing classroom recordings, and its
interface for providing teachers with feedback on their use of talk moves
during individual teaching episodes. We present the series of model
architectures we developed, and the studies we conducted, to develop our
best-performing, transformer-based model (F1 = 79.3%). We also discuss several
technical challenges that need to be addressed when working with real-world
speech and language data from noisy K-12 classrooms.

TalkMoves 是一款应用程序设计用于支持 K-12 数学教师反思和持续改进他们的教学实践。该应用程序结合了最先进的自然语言处理技术和自动语音识别技术，自动分析课堂录音，并为教师提供有关他们使用特定类型的讨论来扩宽和深化数学课堂对话的个性化反馈。