Skip to content Skip to navigation

First-of-its-kind Stanford machine learning tool streamlines student feedback process for computer science professors

The Code In Place interface that shows a student has an error in their code.
(Image credit: Code In Place 2021)
Jul 27 2021
Faculty, Fellow, Research, Stanford

This past spring, Stanford University computer scientists unveiled their pandemic brainchild, Code In Place, a project where 1,000 volunteer teachers taught 10,000 students across the globe the content of an introductory Stanford computer science course.

While the instructors could share their knowledge with hundreds, even thousands, of students at a time during lectures, when it came to homework, large-scale and high-quality feedback on student assignments seemed like an insurmountable task.

“It was a free class anyone in the world could take, and we got a whole bunch of humans to help us teach it,” said Chris Piech, assistant professor of computer science and co-creator of Code In Place. “But the one thing we couldn’t really do is scale the feedback. We can scale instruction. We can scale content. But we couldn’t really scale feedback.”

To solve this problem, Piech worked with Chelsea Finn, assistant professor of computer science and of electrical engineering, and PhD students Mike Wu and Alan Cheng to develop and test a first-of-its-kind artificial intelligence teaching tool capable of assisting educators in grading and providing meaningful, constructive feedback for a high volume of student assignments.

Their innovative tool, which is detailed in a Stanford AI Lab blogpost, exceeded their expectations.

Study co-reseacher, Mike Wu is a 2020 SIGF Fellow.

Read the full article