Edward F. Gehringer

Edward F. Gehringer is a Professor in the College of Engineering at North Carolina State University, holding appointments in both the Department of Computer Science and the Department of Electrical and Computer Engineering. His academic journey includes a Ph.D. in Computer Science from Purdue University (1979), an M.S. in Computer Science from Purdue (1974), and dual undergraduate degrees in Mathematics from the University of Detroit (Mercy) and Wayne State University (1972).

• Ph.D. in Computer Science, Purdue University (1979)
• M.S. in Computer Science, Purdue University (1974)
• B.S. in Mathematics, University of Detroit (Mercy) (1972)
• B.A. in Math/Computer Science, Wayne State University (1972)

Gehringer's research spans Advanced Learning Technologies, Computer Architecture, Operating Systems, High-Performance Computing, and Software Engineering. He is a pioneer in AI-enhanced peer assessment systems and collaborative learning in computing education, with a focus on leveraging large language models (LLMs) for feedback automation, rubric generation, and test-skeleton creation. His work addresses challenges in student team formation, GitHub contribution analysis, and ethical computing.

His publications highlight trends in AI-driven educational tools, peer-review systems, and software engineering pedagogy. Recent studies explore LLM applications for code refactoring, chatbot-assisted teaching, and GitHub analytics for predicting student performance. Earlier work includes foundational contributions to distributed pair programming (Sangam/FaceTop) and hardware-assisted memory management.

Scientific Awards:

• Google Research Award (2014)
• Sloan Consortium Effective Practice Award (2008)
• NC State Gertrude L. Cox Award Honorable Mention (2007)

Gehringer has led multiple NSF-funded projects, including Collaborative Research: Research in Student Peer Review ($1.07M, 2014–2019) and Production and Assessment of Student-authored Wiki Textbooks ($110K, 2010–2012). His Expertiza platform enables peer-reviewed learning objects and active learning in large classes. He has mentored numerous students, with research groups focusing on educational data mining, collaborative coding, and ethical computing.