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

• URL: http://arxiv.org/abs/2602.07754v1
• Date: Sun, 08 Feb 2026 01:18:10 GMT
• Title: Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency
• Authors: Bahare Riahi, Veronica Catete,
• Abstract summary: This study investigates students' perceptions of Artificial Intelligence (AI) grading systems in an undergraduate computer science course.<n>Findings reveal concerns about AI's lack of contextual understanding and personalization.<n>This work contributes to ethics-centered assessment practices by amplifying student voices and offering design principles for humanizing AI in designed learning environments.
• Score: 0.6138671548064355
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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