Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency
Bahare Riahi, Veronica Catete
TL;DR
This study investigates how undergraduate students perceive AI-assisted grading in a block-based programming project, using Jobin's ethical framework to assess transparency, fairness, consistency, and trust. It employs a mixed-methods approach, comparing AI-generated feedback with TA feedback across 27 participants, with IRB clearance and FERPA-consent procedures. Quantitative results show AI feedback is clear and reasonably consistent but considered less fair than TA feedback, while qualitative analysis reveals nuanced views on accuracy and contextual understanding and suggests a need for human-in-the-loop design. The work contributes design principles for humanizing AI in education, highlighting that AI can scale assessment when paired with human oversight and empathetic feedback to preserve fairness and trust for students, including those from marginalized backgrounds.
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.