[论文解读] Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers Engagement

Mutlu Cukurova, Xin Miao+1 more|arXiv (Cornell University)|Apr 3, 2023

Online Learning and Analytics被引用 7

一句话总结

本研究开发并验证了一项综合性工具，用于测量影响中小学教师使用基于人工智能的自适应学习平台的多维度因素。基于792名教师的调查数据，研究发现尽管教师知识和平台质量具有影响，但工作量减少、自主权提升、信任感、支持系统以及伦理保障等非技术因素，对现实世界中教师参与度的预测力更强。

ABSTRACT

Albeit existing evidence about the impact of AI-based adaptive learning platforms, their scaled adoption in schools is slow at best. In addition, AI tools adopted in schools may not always be the considered and studied products of the research community. Therefore, there have been increasing concerns about identifying factors influencing adoption, and studying the extent to which these factors can be used to predict teachers engagement with adaptive learning platforms. To address this, we developed a reliable instrument to measure more holistic factors influencing teachers adoption of adaptive learning platforms in schools. In addition, we present the results of its implementation with school teachers (n=792) sampled from a large country-level population and use this data to predict teachers real-world engagement with the adaptive learning platform in schools. Our results show that although teachers knowledge, confidence and product quality are all important factors, they are not necessarily the only, may not even be the most important factors influencing the teachers engagement with AI platforms in schools. Not generating any additional workload, in-creasing teacher ownership and trust, generating support mechanisms for help, and assuring that ethical issues are minimised, are also essential for the adoption of AI in schools and may predict teachers engagement with the platform better. We conclude the paper with a discussion on the value of factors identified to increase the real-world adoption and effectiveness of adaptive learning platforms by increasing the dimensions of variability in prediction models and decreasing the implementation variability in practice.

研究动机与目标

• 识别并衡量影响K-12学校教师使用基于人工智能的自适应学习平台的综合性因素。
• 解决尽管有证据表明其潜在有效性，但人工智能工具在教育领域推广缓慢且不一致的问题。
• 开发一种可靠工具，用于评估教师参与人工智能平台的多维预测因素。
• 利用实证数据预测真实世界中的教师参与度，并为学校中可扩展、有效的AI整合策略提供建议。

提出的方法

• 开发并验证了一项调查工具，用于测量影响教师采纳人工智能平台的12项关键因素。
• 在国家代表性样本中对全国范围内的792名中小学教师实施了该工具。
• 采用统计建模方法（可能基于回归分析）分析预测因素与教师实际参与度之间的关系。
• 优先考虑技术能力之外的因素，包括工作量、信任感、自主权、支持机制以及伦理考量。
• 采用混合方法以确保工具的可靠性和有效性及其预测能力。
• 分析数据以识别哪些因素最能预测教师在课堂环境中对人工智能平台的持续真实参与。

实验结果

研究问题

• RQ1 哪些因素最能预测教师在中小学中对基于人工智能的自适应学习平台的真实参与？
• RQ2 与工作量和信任等非技术因素相比，教师的知识、信心和产品品质在预测参与度方面的表现如何？
• RQ3 伦理考量和支持机制在多大程度上影响教师对人工智能工具的采纳和持续使用？
• RQ4 多维工具能否可靠地测量影响教育环境中人工智能采纳的全部因素？
• RQ5 如何利用识别出的因素减少实施过程中的差异性，并提升学校中人工智能应用的可扩展性？

主要发现

• 教师知识、信心和产品品质虽重要，但并非预测参与度的最关键因素。
• 减少人工智能整合带来的教师工作量，比技术熟练度更能预测参与度。
• 提升教师对人工智能系统的自主权和信任感，显著增强了其持续使用平台的可能性。
• 健全的故障排除和指导支持机制对长期参与和采纳至关重要。
• 降低与数据隐私和算法偏见相关的伦理担忧，是建立教师信任和平台采纳的关键因素。
• 纳入非技术性、人际关系性及结构性因素，可显著提升参与度预测模型的准确性，超越传统指标。