Learning and Intelligent Systems: Simulating Tutors with Natural Dialog and Pedagogical Strategies

该项目由学习和智能系统倡议资助。 本研究的长期实用目标是开发一种全自动的计算机辅导器。导师将能够（a）从学生输入键盘的内容中提取意义，（B）制定具有教学价值和会话适当性的对话内容。导师的话语动作包括：泵，提示，暗示，提问，回答，总结，拼接在正确的信息，提供即时反馈，并改写学生的贡献。导师的对话贡献将以不同的格式和媒体：打印文本，合成语音，模拟面部运动，图形显示和动画。这样的成就将需要来自认知心理学，话语处理，计算语言学，人工智能，人机交互和教育领域的理论和实证研究的跨学科整合。辅导的主题将在计算机扫盲和介绍医学领域。 以前开发全自动导师的尝试受到了一些技术和理论障碍的严重挑战。这些问题包括：（a）当自然语言在语义和语法上不规范时，如何解释自然语言的问题;（B）世界知识是巨大的、开放的和不完整的问题;以及（c）缺乏对人类指导对话的研究。最近的进步已经大大减少了这些障碍，所以现在是时候重新审视开发自动导师的使命了。根据最近对人类教学的研究，有效教学的一个关键特征在于生成话语贡献，帮助学习者积极构建材料的解释，阐述和心理模型。这项拟议的研究将促进对导师如何管理流畅、礼貌的对话的科学理解，以促进对材料的深度学习。

This project is being funded through the Learning and Intelligent Systems (LIS) Initiative. The long-term practical objective of the research is to develop a fully automated computer tutor. The tutor would be able to (a) extract meaning from the contributions that the student types into a keyboard and (b) formulate dialog contributions with pedagogical value and conversational appropriateness. The tutor's discourse moves include: pumping, prompting, hinting, questioning, answering, summarizing, splicing in correct information, providing immediate feedback, and rewording student contributions. The dialog contributions of the tutor would be in different formats and media: printed text, synthesized speech, simulated facial movements, graphic displays, and animation. Such an achievement will require an interdisciplinary integration of theory and empirical research from the fields of cognitive psychology, discourse processing, computational linguistics, artificial intelligence, human-computer interaction, and education. The tutoring topics will be in the domains of computer literacy and introductory medicine. Previous attempts to develop a fully automated tutor have been seriously challenged by some technical and theoretical barriers. These include (a) the problem of interpreting natural language when it is not well-formed semantically and grammatically, (b) the problem of world knowledge being immense, open-ended and incomplete, and (c) the lack of research on human tutorial dialog. Recent advances have dramatically reduced these barriers, so it is time to revisit the mission of developing an automated tutor. According to the recent research on human tutoring, a key feature of effective tutoring lies in generating discourse contributions that assist learners in actively constructing explanations, elaborations, and mental models of the material. The proposed research will advance scientific understanding of how a tutor can manage a smooth, polite dialog that promotes deep learning of the material.