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

arXiv: https://arxiv.org/abs/2602.07754 · HTML: https://mlsanity.com/paper/2602.07754/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023)
- [Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks](https://mlsanity.com/paper/2310.07879/) (2023)
- [Large Language Models for Education: Grading Open-Ended Questions Using ChatGPT](https://mlsanity.com/paper/2307.16696/) (2023)
- [Comparative Analysis of STEM and non-STEM Teachers' Needs for Integrating AI into Educational Environments](https://mlsanity.com/paper/2509.16276/) (2025)

## Abstract (arXiv)

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.