[论文解读] The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

Mike Perkins, Leon Furze+2 more|arXiv (Cornell University)|Dec 12, 2023

Artificial Intelligence in Healthcare and Education参考文献 57被引用 7

一句话总结

这篇论文提出了 AI Assessment Scale (AIAS)，一个在教育评估中以伦理方式整合 GenAI 的实用框架，平衡教学目标、透明度、公平性与灵活性。

ABSTRACT

Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct.

研究动机与目标

• 在教育评估中应对 GenAI 的机会与挑战时，推动需要一个伦理、实用工具的需求。
• 提供一个简单、灵活的框架，教育者可以快速实施以使 GenAI 的使用与学习成果对齐。
• 在 GenAI 支持的评估政策中，为学生和机构提高清晰度与透明度。
• 鼓励将话语转向利用 GenAI 来支持教学与学习，而不仅仅将其视为不当行为的风险。

提出的方法

• 将 AIAS 提出为一个结构化工具，用于基于学习成果选择合适的 GenAI 使用水平。
• 概述教育者和机构如何快速采用该框架来制定政策。
• 结合现有文献，将教育中的 GenAI 重新定位为教学法的辅助，而不仅仅是一个不当行为的担忧。

实验结果

研究问题

• RQ1 在 AIAS 的使用下，不同学习成果的 GenAI 使用水平在教育上是否适宜？
• RQ2 AIAS 如何提升学生与机构在透明度、公平性与政策清晰度方面的表现？
• RQ3 AIAS 如何帮助将教育中的 GenAI 转变为支持教学与学习的工具？
• RQ4 机构如何快速实施 AIAS 以应对对 GenAI 在教育中的不确定性和焦虑？

主要发现

• AIAS 为将 GenAI 融入评估提供了一个以学习成果为重点的实用起点。
• 该框架优先考虑学生与教育者的透明度与公平性。
• AIAS 鼓励灵活、快速的实施，能够适应多样化的教育情境。
• 作者主张将关于 GenAI 的话语重新定位为对教学法的提升，而不仅仅聚焦于不当行为。