Asee peer logo

Board 62: Applications of Artificial Intelligence in Peer Assessment

Download Paper |


2018 ASEE Annual Conference & Exposition


Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Computers in Education Division Poster Session

Tagged Division

Computers in Education

Page Count




Permanent URL

Download Count


Request a correction

Paper Authors


Edward F. Gehringer North Carolina State University

visit author page

Dr. Gehringer is an associate professor in the Departments of Computer Science, and Electrical & Computer Engineering. His research interests include computerized assessment systems, and the use of natural-language processing to improve the quality of reviewing. He teaches courses in the area of programming, computer architecture, object-oriented design, and ethics in computing.

visit author page


Ferry Pramudianto North Carolina State University

visit author page

Dr. Ferry Pramudianto is a Senior Research Engineer at Computer Science Department in NC State University. He has more than seven years of experience in European projects, during which he has led three multinational teams, organized technology transfer workshops, and held presentations in international conferences, as well as for the European Commission. His main research area includes Peer Assessment, Learning Analytics, Service-Oriented Architecture, Model Driven Development, and the Internet of Things.

visit author page

author page

Abhinav Medhekar North Carolina State University


Chandrasekar Rajasekar North Carolina State University

visit author page

Master of Computer Science Student at North Carolina State University.

visit author page

author page

Zhongcan Xiao North Carolina State University

Download Paper |


Peer assessment has at least a 50-year history in academia, and online applications peer assessment have been available for more than 20 years. Until recently, online applications simply transmitted classmates’ feedback to each other. But in the past decade, facilities have been incorporated to automatically recognize good reviews. This helps authors know which suggestions to follow and helps reviewers improve their reviews. It can also aid in assigning peer grades. Several types of data can be used to determine review quality. These metrics can be combined using machine-learning and neural-network models to produce better estimates of review quality, and hence better estimates of the quality of reviewed work. This paper discusses past work in automatically assessing reviews, and summarizes our current efforts to build on that work.

Gehringer, E. F., & Pramudianto, F., & Medhekar, A., & Rajasekar, C., & Xiao, Z. (2018, June), Board 62: Applications of Artificial Intelligence in Peer Assessment Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30073

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2018 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015