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

TL;DR

This study explores how structured peer assessment in programming courses can serve as a reliable, engaging alternative to traditional grading, especially in the context of AI coding assistants impacting assessment integrity.

Contribution

It provides empirical evidence that peer review can approximate instructor grading and enhances student engagement in programming education amidst AI tools.

Findings

Peer review correlates moderately with instructor grades

Students perceive peer assessment as fair and engaging

Peer feedback fosters evaluative skills and interest in coding

Abstract

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.