[论文解读] The Responsible Development of Automated Student Feedback with Generative AI

Euan Lindsay, Mike Zhang+2 more|arXiv (Cornell University)|Aug 29, 2023

Intelligent Tutoring Systems and Adaptive Learning参考文献 8被引用 6

一句话总结

本文提出了一套负责任的框架，用于开发生成式人工智能系统，以在高等教育中提供可扩展、个性化的反馈。该框架利用大语言模型，克服传统自动化反馈系统的局限性。框架强调伦理设计、包容性、人类专业知识的整合以及持续的模型优化，以确保所有学生（而不仅仅是‘中位数’学习者）都能获得公平且高质量的反馈。

ABSTRACT

Providing rich, constructive feedback to students is essential for supporting and enhancing their learning. Recent advancements in Generative Artificial Intelligence (AI), particularly with large language models (LLMs), present new opportunities to deliver scalable, repeatable, and instant feedback, effectively making abundant a resource that has historically been scarce and costly. From a technical perspective, this approach is now feasible due to breakthroughs in AI and Natural Language Processing (NLP). While the potential educational benefits are compelling, implementing these technologies also introduces a host of ethical considerations that must be thoughtfully addressed. One of the core advantages of AI systems is their ability to automate routine and mundane tasks, potentially freeing up human educators for more nuanced work. However, the ease of automation risks a ``tyranny of the majority'', where the diverse needs of minority or unique learners are overlooked, as they may be harder to systematize and less straightforward to accommodate. Ensuring inclusivity and equity in AI-generated feedback, therefore, becomes a critical aspect of responsible AI implementation in education. The process of developing machine learning models that produce valuable, personalized, and authentic feedback also requires significant input from human domain experts. Decisions around whose expertise is incorporated, how it is captured, and when it is applied have profound implications for the relevance and quality of the resulting feedback. Additionally, the maintenance and continuous refinement of these models are necessary to adapt feedback to evolving contextual, theoretical, and student-related factors. Without ongoing adaptation, feedback risks becoming obsolete or mismatched with the current needs of diverse student populations [...]

研究动机与目标

• 解决在高等教育中部署生成式AI进行自动化学生反馈所面临的伦理挑战。
• 克服自动化反馈系统中‘多数人暴政’的问题，避免边缘化少数或独特学习者。
• 确保反馈保持个性化、相关且能适应学生需求和教育情境的动态变化。
• 将人类学科专业知识有意义地整合到AI反馈开发中，以维持质量与真实性。
• 建立可持续、透明且可审计的AI反馈流程，以建立信任并确保长期有效性。

提出的方法

• 采用RESPACT框架作为AI反馈开发中伦理决策的结构化视角。
• 将人类专家知识整合到模型训练和反馈生成过程中，以确保教学相关性与真实性。
• 实施持续的模型优化与适应，以应对不断变化的学生群体、课程内容和理论框架。
• 通过严格的、面向多样化用户群体的测试以及定期系统审计，检测并缓解偏见与公平性问题。
• 通过全面的文档记录和面向用户的指导，确保透明度与可解释性。
• 为教育工作者嵌入AI素养培训，以支持AI反馈工具负责任且高效的应用。

实验结果

研究问题

• RQ1 如何伦理地开发生成式AI，以实现可扩展、个性化的反馈，同时不加剧现有的教育不平等？
• RQ2 人类学科专家在AI生成反馈系统的设计与优化中应发挥何种作用？
• RQ3 如何长期维护和调整AI反馈系统，以确保其在多样化学生群体中的相关性与有效性？
• RQ4 通过何种机制可确保AI驱动反馈的透明度、可解释性与可信度，同时不取代人类教师？
• RQ5 如何提升教育工作者的AI素养，以支持AI反馈工具在教学与评估中的负责任整合？

主要发现

• 生成式AI能够以前所未有的方式实现可扩展、即时且个性化的反馈。
• 当前的自动化反馈系统主要服务于‘中位数’学生，高成就者与学习困难者因开发成本过高而被忽视。
• 人类专业知识的整合对于确保AI生成输出的反馈质量、真实感与教学相关性至关重要。
• 持续的模型维护与适应对于防止反馈过时或与当前学生需求脱节至关重要。
• 严格的测试、多样化用户评估以及定期审计是检测并缓解AI反馈系统中偏见、确保公平性的关键。
• 教育工作者的AI素养是负责任且有效使用AI反馈工具的前提，但目前许多教育工作者仍缺乏这一能力。