Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency

TL;DR

This study explores students' perceptions of AI grading in a computer science course, highlighting concerns about fairness, trust, and transparency, and proposing human-centered design principles for ethical AI assessment.

Contribution

It provides empirical insights into student perceptions and offers design principles for humanizing AI grading systems in educational settings.

Findings

Students perceive AI grading as lacking contextual understanding.

Concerns about fairness and transparency in AI grading.

Recommendations for AI systems to incorporate human judgment and empathy.

Abstract

This study investigates students' perceptions of Artificial Intelligence (AI) grading systems in an undergraduate computer science course (n = 27), focusing on a block-based programming final project. Guided by the ethical principles framework articulated by Jobin (2019), our study examines fairness, trust, consistency, and transparency in AI grading by comparing AI-generated feedback with original human-graded feedback. Findings reveal concerns about AI's lack of contextual understanding and personalization. We recommend that equitable and trustworthy AI systems reflect human judgment, flexibility, and empathy, serving as supplementary tools under human oversight. This work contributes to ethics-centered assessment practices by amplifying student voices and offering design principles for humanizing AI in designed learning environments.