DIP: Teaching Writing and Argumentation with AI-Supported Diagramming and Peer Review

DIP：利用人工智能支持的图表和同行评审来教授写作和论证

• 批准号: 1122504
• 负责人: Kevin Ashley
• 金额: $ 135万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1122504&HistoricalAwards=false
• 关键词: DIP; Teaching; Writing; Argumentation; AI

The PIs are investigating the design of intelligent tutoring systems (ITSs) that are aimed at learning in unstructured domains. Such systems are not able to do as much automatically as ITSs working in traditionally narrow and well-structured domains, but rather they need to share responsibilities for scaffolding learning with a teacher and/or peers. In the work proposed, the three PIs, who share expertise in automated natural language understanding, intelligent tutoring systems, machine learning, argumentation (especially in law), complex problem solving, and engineering education, are integrating intelligent tutoring, data mining, machine learning, and language processing to design a socio-technical system (people and machines working together) that helps undergraduates and law students write better argumentative essays. The work of helping learners derive an argument is shared by the computer and peers, as is the work of helping peer reviewers review the writing of others and the work of learners to turn their argument diagrams into well-written documents. Research questions address the roles computers might take on in promoting writing and the technology that enables that, how to distribute scaffolding between an intelligent machine and human agents, how to promote better writing (especially the relationship between diagramming and writing), and how to promote learning through peer review of the writing of others. This project is bringing together outstanding researchers from a variety of different disciplines -- artificial intelligence, law education, engineering and science education, and cognitive psychology -- to address an education issue of national concern -- writing, especially writing that makes and substantiates a point -- and to explore ways of extending intelligent tutoring systems beyond fact-based domains. It fulfills all aims of the Cyberlearning program -- to imagine, design, and learn how to best design and use the next generation of learning technologies, to address learning issues of national importance, and to contribute to understanding of how people learn.

PIS正在研究旨在非结构化领域中学习的智能教学系统(ITSS)的设计。这种系统不能像在传统上狭窄和结构良好的领域中工作的信息技术支持系统那样自动完成许多工作，而是需要与教师和/或同行分担支架式学习的责任。在拟议的工作中，这三个PI共享在自动自然语言理解、智能辅导系统、机器学习、辩论(特别是在法律方面)、复杂问题解决和工程教育方面的专业知识，正在整合智能辅导、数据挖掘、机器学习和语言处理，以设计一个社会技术系统(人和机器一起工作)，帮助本科生和法律系学生撰写更好的议论文。帮助学习者推导论点的工作由计算机和同行分担，帮助同行评审员审查其他人的写作以及学习者将他们的论点图转化为良好的文档的工作也是如此。研究问题涉及计算机在促进写作方面可能扮演的角色，以及能够实现这一点的技术，如何在智能机器和人类代理之间分配脚手架，如何促进更好的写作(特别是图表和写作之间的关系)，以及如何通过同行评议他人的写作来促进学习。该项目汇集了来自不同学科的杰出研究人员--人工智能、法律教育、工程和科学教育以及认知心理学--以解决国家关注的教育问题--写作，特别是提出并证实观点的写作--并探索将智能教学系统扩展到基于事实的领域之外的方法。它实现了网络学习计划的所有目标--想象、设计和学习如何最好地设计和使用下一代学习技术，解决具有国家重要性的学习问题，并有助于理解人们是如何学习的。