Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911462327320576 |
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| author | Riahi, Bahare Storozhevykh, Viktoriia Catete, Veronica |
| author_facet | Riahi, Bahare Storozhevykh, Viktoriia Catete, Veronica |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07754 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency Riahi, Bahare Storozhevykh, Viktoriia Catete, Veronica Artificial Intelligence Human-Computer Interaction I.2.6; I.2.7 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. |
| title | Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency |
| topic | Artificial Intelligence Human-Computer Interaction I.2.6; I.2.7 |
| url | https://arxiv.org/abs/2602.07754 |