[论文解读] Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education

Alexander J. Karran, Philippe Charland+5 more|arXiv (Cornell University)|Feb 22, 2024

Ethics and Social Impacts of AI被引用 7

一句话总结

本文调查教育领域的各种AI应用对学生、教师和家长的可接受性，使用情境剧本和中介分析揭示代理性、透明度、可解释性和隐私如何影响接受度与信任。

ABSTRACT

This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions.

研究动机与目标

• 从多方利益相关者的角度检验教育中的AI可接受性（学生、教师、家长）。
• 识别AI特征（代理性、透明度、可解释性、隐私）如何影响接受度与信任。
• 探索如感知全球效用、公正感和信心等中介因素在各利益相关者之间的作用。

提出的方法

• 使用情境剧本方法，包含四个AI使用场景，其中代理性、透明度、可解释性和隐私被操控。
• 在每个情景后，收集关于全球效用、个人有用性、公正、信心、风险以及使用该AI情景的意向的问卷数据。
• 进行中介分析，考察AI特征如何通过效用感、公正感和信心等中介变量影响接受度。
• 汇聚来自1,198名参与者的数据，这些参与者通过合作机构和社交媒体招募，代表多种利益相关者群体。

实验结果

研究问题

• RQ1 利益相关者群体（学生、教师、家长）如何影响教育AI的接受度与信任？
• RQ2 AI代理性、透明度和可解释性的变化如何影响感知效用、公正感和信心？
• RQ3 有哪些中介变量可以解释AI特征与教育AI使用意向之间的关系？
• RQ4 全球效用和公正感的知觉在不同AI使用场景中是否对接受度具有关键影响？

主要发现

• 不同利益相关者群体在AI的接受度和信任方面存在显著差异。
• 感知的全球效用、公正感和信心在AI特征（代理性、透明度、可解释性）与使用AI的意向之间起中介作用。
• 教育领域的AI接受度是细致的，取决于具体的AI应用及利益相关者的感知。