通过学生视角，揭示AI评分在理解上下文与个性化上的不足。
Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency
- 对比学生对AI与人工评分反馈的感知差异
- 27名本科生反映AI缺乏情境理解与情感共鸣
- 建议将AI作为人工监督下的辅助工具
本研究基于Jobin（2019）提出的伦理原则框架，调查了27名本科生在计算机科学课程中对人工智能（AI）评分系统的感知，聚焦于基于代码块的期末项目评分。通过对比AI生成反馈与原始人工评分反馈，研究发现学生普遍担忧AI缺乏上下文理解与个性化表达能力。研究建议，公平且可信的AI系统应体现人类判断力、灵活性与同理心，作为人工监督下的辅助工具使用。本工作通过倾听学生声音，为以伦理为中心的评估实践提供了设计原则，推动学习环境中的AI人性化设计。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。