让AI评分更人性化：以学生为中心的公平性、信任、一致性与透明性洞察
Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency
AI总结：
本研究通过比较AI与人工评分反馈，探讨学生对AI评分系统在公平性、信任、一致性与透明性方面的看法，并提出人本化AI的设计原则。
摘要：
本研究调查了本科生计算机科学课程中学生对人工智能（AI）评分系统的看法，重点是一块式编程期末项目（n=27）。基于Jobin（2019）提出的伦理原则框架，我们的研究通过比较AI生成的反馈与原始人工评分反馈，探讨了AI评分中的公平性、信任、一致性和透明性。研究发现，学生对AI缺乏情境理解和个性化表示担忧。我们建议，公平且可信的AI系统应反映人类判断、灵活性和同理心，作为在人类监督下的补充工具。本研究通过放大学生声音，为以伦理为中心的评估实践做出贡献，并为设计学习环境中的人格化AI提供了设计原则。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
