From Coders to Critics: Empowering Students through Peer Assessment in the Age of AI Copilots
This addresses the problem of academic integrity and skill development for educators in programming courses, offering an incremental alternative to traditional grading methods.
The paper tackled the challenge of assessing student programming work in the presence of AI coding assistants by implementing a rubric-based, anonymized peer review process in an introductory course, finding that peer evaluations approximated instructor grades with moderate accuracy and increased student engagement and evaluative thinking.
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.