[论文解读] The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges

Okan Bulut, Maggie Beiting-Parrish+15 more|arXiv (Cornell University)|Jun 27, 2024

Online Learning and Analytics被引用 18

一句话总结

本论文综述了在教育评估中使用人工智能的机会及伦理挑战，涵盖题目生成、自动评分、监考和反馈，并强调偏见、透明度与公平性关切及拟议的缓解措施。

ABSTRACT

The integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes pose risks of perpetuating inequalities and affecting assessment outcomes. Responding to these concerns, various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. The National Council of Measurement in Education's Special Interest Group on AI in Measurement and Education (AIME) also focuses on establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI's responsible and effective use in education.

研究动机与目标

• 在AI工具改变评估实践之时，推动对AI驱动的教育评估进行伦理层面的审查。
• 解释AI应用如自动题目生成、多模态刺激和自动评分在教育中的运作方式。
• 识别关键伦理关切（偏见、公平性、透明度、考试安全性、环境影响）并提出缓解策略。
• 强调来自 NCME、ITC、ATP、ETS 和 Duolingo 的现有准则与标准，以规范评估中伦理AI的使用。

提出的方法

• 对教育测量中当前的AI应用（AIG、多模态刺激生成、自动评分）进行回顾与综合分析。
• 讨论来自专业组织（AERA/APA/NCME、ITC/ATP）及行业标准的伦理框架与标准。
• 对AI评分中的偏差类型及检测与纠正方法（DIF、公平性类型、子群分析）进行分析。
• 以AP中文评分为例，对比人类与AI的表现及其理由。

Figure 1: A writing sample in Mandarin.

实验结果

研究问题

• RQ1 AI在教育测量中所带来的主要机遇（题目生成、评分、反馈、考试监控）有哪些，并伴随哪些伦理风险？
• RQ2 AI基于评估中的偏见如何产生，公平性如何定义与衡量，有哪些缓解偏见的策略？
• RQ3 有哪些准则、标准和最佳实践可以规范教育测量中对AI的伦理使用，以及如何应用？

主要发现

• AI 实现了自动评分和快速内容分析，有潜力提供个性化反馈和可扩展的评估分析。
• 伦理关切包括效度、信度、透明度、公平性、偏见和考试安全性，尤其是鉴于许多AI模型的黑箱特性。
• AI评分中的偏见可能来自历史、表示、测量和部署因素，需要进行严格的DIF分析和公平性标准。
• 知名标准与指南（AERA/APA/NCME、ITC/ATP、ETS最佳实践、Duolingo Responsible AI Standards）倡导验证、透明度和人工监督。
• 建议采用人机协同、多样化且无偏数据以及持续监控以缓解问责性与公平性风险。

Figure 2: Rationales provided by ChatGPT 3.5.