[论文解读] The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment- A Preprint

Leon Furze, Mike Perkins+2 more|arXiv (Cornell University)|Mar 15, 2024

Artificial Intelligence in Healthcare and Education被引用 5

一句话总结

论文报告在越南英国大学进行的试点研究，实施AI Assessment Scale (AIAS)以指导GenAI整合评估，结果是降低不当行为、提升成就和模块通过率。

ABSTRACT

The rapid adoption of Generative Artificial Intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices, and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. This paper presents the findings of a pilot study conducted at British University Vietnam (BUV) exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from 'No AI' to 'Full AI', enabling educators to design assessments that focus on areas requiring human input and critical thinking. Following the implementation of the AIAS, the pilot study results indicate a significant reduction in academic misconduct cases related to GenAI, a 5.9% increase in student attainment across the university, and a 33.3% increase in module passing rates. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in HE, promoting academic integrity while leveraging the technology's potential to enhance learning experiences. Refer to published version for final text.

研究动机与目标

• 调查一个灵活的 AIAS 框架如何将 GenAI 纳入高等教育评估。
• 评估AIAS引导的评估是否能减少GenAI相关的学术不端行为。
• 评估在实现AIAS后对学生成就和课程模块通过率的影响。
• 当整合GenAI工具时，考察教学实践和学生作业模式的变化。

提出的方法

• 采用AIAS，设定从“无AI”到“全AI”的五个等级来设计评估任务。
• 在越南英国大学对高等教育进行试点实施。
• 收集并分析学术诚信、成就和模块通过率的指标。
• 观察教学实践和学生提交的变化，以包含GenAI支持的作业。

实验结果

研究问题

• RQ1 在高等教育评估中使用AIAS是否能减少GenAI相关的学术不端行为？
• RQ2 AIAS引导的GenAI整合对学生成就和模块通过率的影响是什么？
• RQ3 在应用AIAS时，教师的教 学实践和评估设计如何演变？

主要发现

• 在GenAI相关的学术不端行为方面显著减少。
• 全校范围内学生成就提升了5.9%。
• 模块通过率提升了33.3%。
• 教师在模块中引入GenAI工具，学生提交多模态作品。
• AIAS在保持学术诚信与促进学习提升的同时，支持GenAI的整合。