Repository Feedback

Your feedback helps us improve the repository's content relevance and usability. Please share your thoughts to help us better serve researchers and practitioners.

Submit feedback

Submit a research study

Contribute to the repository:

Add a paper

From Coders to Critics: Empowering Students through Peer Assessment in the Age of AI Copilots

Authors
Santiago Berrezueta-Guzman,
Stephan Krusche,
Stefan Wagner
Date
Publisher
arXiv
Link
http://arxiv.org/pdf/2505.22093v1
The rapid adoption of AI powered coding assistants like ChatGPT and other coding copilots is transforming programming education, raising questions about assessment practices, academic integrity, and skill development. As educators seek alternatives to traditional grading methods susceptible to AI enabled plagiarism, structured peer assessment could be a promising strategy. This paper presents an empirical study of a rubric based, anonymized peer review process implemented in a large introductory programming course. Students evaluated each other's final projects (2D game), and their assessments were compared to instructor grades using correlation, mean absolute error, and root mean square error (RMSE). Additionally, reflective surveys from 47 teams captured student perceptions of fairness, grading behavior, and preferences regarding grade aggregation. Results show that peer review can approximate instructor evaluation with moderate accuracy and foster student engagement, evaluative thinking, and interest in providing good feedback to their peers. We discuss these findings for designing scalable, trustworthy peer assessment systems to face the age of AI assisted coding.
Study at a glance
  • Question: How well does peer assessment evaluate coding projects compared to instructor grading, and how do students perceive it?
  • Setting and data: A large introductory programming course; student 2D game final projects and reflective surveys from 47 teams.
  • Method: Comparison of anonymized rubric-based peer assessments with instructor grades, combined with reflective student surveys.
Why it matters

The rapid adoption of AI coding assistants is transforming programming education, raising questions about academic integrity and skill development. Consequently, educators seek alternatives to traditional grading methods that are susceptible to AI-enabled plagiarism.

Key findings
  • In a large introductory programming course, peer assessments of student projects approximated instructor evaluations with moderate accuracy.
  • Participating in peer review fostered student engagement and evaluative thinking.
  • Peer assessment fostered students' interest in providing good feedback to their peers.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

Who age?
Post-Secondary
Why use AI?
Outcomes – Durable Skills
Study design
Quantitative – Others
Download PDF