[论文解读] The AI Assessment Scale Revisited: A Framework for Educational Assessment

Mike Perkins, Jasper Roe+1 more|arXiv (Cornell University)|Dec 12, 2024

Artificial Intelligence in Healthcare and Education被引用 5

一句话总结

本论文提出更新的人工智能评估量表（AIAS），用于引导教育者与学生就生成式人工智能的使用展开对话，并在 AI 能力不断发展的背景下帮助重新设计评估，配备新的视觉指南和更精炼的等级。

ABSTRACT

Recent developments in Generative Artificial Intelligence (GenAI) have created significant uncertainty in education, particularly in terms of assessment practices. Against this backdrop, we present an updated version of the AI Assessment Scale (AIAS), a framework with two fundamental purposes: to facilitate open dialogue between educators and students about appropriate GenAI use and to support educators in redesigning assessments in an era of expanding AI capabilities. Grounded in social constructivist principles and designed with assessment validity in mind, the AIAS provides a structured yet flexible approach that can be adapted across different educational contexts. Building on implementation feedback from global adoption across both the K-12 and higher education contexts, this revision represents a significant change from the original AIAS. Among these changes is a new visual guide that moves beyond the original traffic light system and utilises a neutral colour palette that avoids implied hierarchies between the levels. The scale maintains five distinct levels of GenAI integration in assessment, from "No AI" to "AI Exploration", but has been refined to better reflect rapidly advancing technological capabilities and emerging pedagogical needs. This paper presents the theoretical foundations of the revised framework, provides detailed implementation guidance through practical vignettes, and discusses its limitations and future directions. As GenAI capabilities continue to expand, particularly in multimodal content generation, the AIAS offers a starting point for reimagining assessment design in an era of disruptive technologies.

研究动机与目标

• 促使教育者和学生之间就教育中适当的 GenAI 使用开展开放对话。
• 提供一个经验证导向的修订框架，适用于 K-12 与高等教育情境，可广泛适应。
• 提供实用指南和情景案例以在多样化评估情境中实施 AIAS。
• 反映全球采用的实施反馈并应对日益发展的 GenAI 能力。

提出的方法

• 将框架建立在社会建构主义原理和评估有效性之上。
• 描述由从无AI到AI探索五个层级组成的修订 AIAS 结构。
• 引入新的中性颜色视觉指南，避免层级之间的暗示性等级排序。
• 通过情景案例提供实用的实施指南。
• 讨论局限性、未来方向以及对多模态 GenAI 发展之适用性。

实验结果

研究问题

• RQ1 如何更新 AIAS 以反映在教育中迅速发展的 GenAI 能力？
• RQ2 AIAS 在促进对话和跨多样化教育情境的评估重新设计方面可如何发挥作用？
• RQ3 在 K-12 与高等教育中实施修订后的 AIAS 的实际意义与局限性是什么？
• RQ4 新的视觉指南如何影响对评估中 AI 集成层级的认知？

主要发现

• AIAS 已经修订以反映日益发展的 GenAI 能力和教学需要。
• 更新包括一个新的中性视觉指南，避免对不同层级的排名暗示。
• 框架保持五个 GenAI 集成层级，从无 AI 到 AI 探索。
• 通过实用情景案例提供实施指南，帮助教育者重新设计评估。
• 此次修订基于全球采用反馈并强调评估有效性与建构主义原则。