Student Perceptions Of AI-Assisted Grading And Feedback In Education
TL;DR
Students’ perceptions of Gradify are examined, indicating that students primarily used AI-generated feedback as an early-stage debugging aid to interpret failed test cases and refine implementations and AI-assisted grading was viewed as a supportive tool rather than a replacement for instructor judgment.
Abstract
The growing integration of artificial intelligence in programming education has led to the adoption of AI-assisted grading and feedback systems in large-enrollment courses. This study examines students’ perceptions of Gradify, an AI-assisted autograding platform with optional AI-generated feedback, deployed in a second-year undergraduate course (Advanced Object-Oriented Programming). An anonymous end-of-term survey was administered to the enrolled students. Findings indicate that students primarily used AI-generated feedback as an early-stage debugging aid to interpret failed test cases and refine implementations. While many reported improved debugging efficiency and solution quality, perceptions of its contribution to deeper conceptual understanding were mixed. Trust in AI feedback was conditional and influenced by concerns about occasional inaccuracies. Students were mostly comfortable with automated grading when paired with human oversight, particularly for open-ended assessments. Overall, AI-assisted grading was viewed as a supportive tool rather than a replacement for instructor judgment.