[论文解读] MAILS -- Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies

Astrid Carolus, Martin Koch+3 more|arXiv (Cornell University)|Feb 18, 2023

Ethics and Social Impacts of AI被引用 12

一句话总结

本论文开发并验证 MAILS 问卷以评估 AI 素养，结合标准 AI 素养维度与心理变化与元能力，在 300 名讲德语成年人中通过验证性因子分析。

ABSTRACT

The goal of the present paper is to develop and validate a questionnaire to assess AI literacy. In particular, the questionnaire should be deeply grounded in the existing literature on AI literacy, should be modular (i.e., including different facets that can be used independently of each other) to be flexibly applicable in professional life depending on the goals and use cases, and should meet psychological requirements and thus includes further psychological competencies in addition to the typical facets of AIL. We derived 60 items to represent different facets of AI Literacy according to Ng and colleagues conceptualisation of AI literacy and additional 12 items to represent psychological competencies such as problem solving, learning, and emotion regulation in regard to AI. For this purpose, data were collected online from 300 German-speaking adults. The items were tested for factorial structure in confirmatory factor analyses. The result is a measurement instrument that measures AI literacy with the facets Use & apply AI, Understand AI, Detect AI, and AI Ethics and the ability to Create AI as a separate construct, and AI Self-efficacy in learning and problem solving and AI Self-management. This study contributes to the research on AI literacy by providing a measurement instrument relying on profound competency models. In addition, higher-order psychological competencies are included that are particularly important in the context of pervasive change through AI systems.

研究动机与目标

• 将 AI 素养问卷建立在既定能力模型与心理因素之上。
• 创建一个可在专业场景中独立使用的模块化量表。
• 在标准素养维度之外，加入心理变化与元能力以扩展 AI 素养测量。
• 推导并验证一个 60 项 AI 素养题项集以及 12 项心理能力题。
• 为在现实世界情境中评估 AI 素养及相关心理能力提供可用的测量工具。

提出的方法

• 基于 Ng 等人的 AI 素养框架及额外的心理能力进行题项开发。
• 在线数据收集，300 名讲德语成年人。
• 使用验证性因子分析来检验题项集的因子结构。
• 进行模型测试以建立具有多维度与构念的测量工具。
• 纳入更广泛的构念，如在学习和解决问题中的 AI 自我效能感以及 AI 自我管理。

实验结果

研究问题

• RQ1 MAILS 是否能按既定框架（使用与应用 AI、理解 AI、检测 AI、AI 伦理、创造 AI）捕捉到 AI 素养的不同维度？
• RQ2 所提出的心理能力（例如与 AI 相关的解决问题、学习、情绪调节）是否可被衡量且可靠？
• RQ3 验证性因子分析是否支持 MAILS 的假设因子结构和模块化？
• RQ4 由于其模块化设计，MAILS 是否能在专业环境中灵活使用，同时保持有效的测量属性？

主要发现

• MAILS 的因子结构包括 使用与应用 AI、理解 AI、检测 AI、AI 伦理、创造 AI 作为独立构念。
• 与 AI 相关的心理能力（如解决问题、学习、情绪调节）可以与 AI 素养维度一起被测量。
• 在 300 名讲德语成年人 的样本中，该工具显示出经过验证的测量模型。
• 该问卷具有模块化特性，能够在专业情境中针对不同目标和使用场景独立使用不同维度。