With the recent development of AI, there has been tremendous changes in both offline and online education. Entire in-class interactions and behaviors between students and instructors have been structured and stored, which provide valuable information for analyzing class performance and improving the learning experience. In this talk, I will first show some successful applications we deployed in TAL's offline and online classrooms. Then I will outline the challenges we meet during the course of building real-world AI+Edu applications.

After that, I will talk about the two initiatives we developed on (1) building a cost-effective and consistent approach of automatic oral language skills evaluation, which reduces the monotonous and tedious grading workloads from teaching professionals and (2) developing a multimodal learning framework of classroom activity detection, which break the blackbox of traditional learning environments.

参考译文：

随着AI的最新发展，离线和在线教育都发生了巨大变化。整个课堂里学生和教师之间的互动和行为都经过结构化设计，并存储起来用于分析，这为课堂表现和学习体验改善提供了有价值的信息。本次演讲中，我会展示我们在TAL的离线和在线教室中部署的一些成功案例，也会大概介绍在构建实际“AI + Edu”应用过程中遇到的挑战。

之后，我将讨论我们基于以下两点而制定的两项倡议：一是建立一种低成本且一致的自动口语技能评估方法，这将减少教学专业人员的单调乏味的评分工作量；二是开发多模式学习课堂活动检测的框架，有助于打破传统学习环境的障碍。

听众受益点：

1. 最新AI算法在教育场景中应用
2. 如何把经典AI灵活运用在真实场景中