Doctoral Consortium for Students in AI; July 28, 1997; Providence, RI

人工智能学生博士联盟；

• 批准号: 9708559
• 负责人: Bruce Buchanan
• 金额: $ 1.19万
• 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: 1997
• 资助国家: 美国
• 起止时间: 1997-04-01 至 1998-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9708559&HistoricalAwards=false
• 关键词: Doctoral; Consortium; Students; AI; July

One of the most important aspects in learning how to do research is early and detailed discussions on various aspects of the research plan with experienced mentors. Unfortunately, most students get little feedback on their thesis at the initial stages except from their immediate advisors. Attending conferences can be a good way to meet other researchers and discuss work. However, the chances of a student at an early stage of his/her work getting the opportunity to discuss their preliminary ideas with well established researchers are not very good. The Doctoral Consortium (DC) at the American Association for Artificial Intelligence conference was planned as a way to improve this situation. Students about to begin their thesis work are chosen to present their research plans in front of a group of carefully selected faculty panelists. Unlike traditional conference presentations, the bulk of the time in a DC is devoted to discussions between the students and the faculty panelists. Each student is also assigned a faculty mentor who works with the student at a more detailed level. Students learn from both their own presentations as well as watching other students being advised by the faculty panelists. A survey taken after the first DC held last year found all participants (both students and faculty) agreeing that the DC was tremendously useful. We anticipate that all students attending the DC this year (both as presenters as well as those just attending) will leave with renewed confidence in their work, suggestions for improving their thesis plans and a clearer idea of what constitutes good research practice.

学习如何做研究最重要的方面之一是及早与经验丰富的导师就研究计划的各个方面进行详细的讨论。不幸的是，除了他们的直接导师，大多数学生在最初阶段几乎没有得到关于他们论文的反馈。参加会议是与其他研究人员见面和讨论工作的好方法。然而，学生在其工作的早期阶段有机会与知名研究人员讨论他们的初步想法的机会并不是很好。美国人工智能协会会议上的博士联盟(DC)就是为了改善这种情况而计划的。即将开始他们的论文工作的学生被挑选出来，在一群精心挑选的教师小组成员面前展示他们的研究计划。与传统的会议演示不同，DC中的大部分时间都用于学生和教师小组成员之间的讨论。每名学生还被分配了一名教职导师，他将在更详细的层面上与学生合作。学生们既可以从自己的演讲中学习，也可以通过观看其他学生接受教员小组成员的建议来学习。在去年举行的第一届区议会后进行的一项调查发现，所有参与者(包括学生和教职员工)都同意区议会非常有用。我们预计，今年参加DC的所有学生(无论是作为演讲者还是刚刚参加的学生)都会对他们的工作重新充满信心，对改进他们的论文计划提出建议，并对什么是好的研究实践有了更清晰的认识。