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 re-search products of the research community. Therefore, there have been in-creasing 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.

翻译：尽管已有证据表明基于人工智能的自适应学习平台具有影响力，但它们在学校的规模化采纳进展缓慢。此外，学校所采用的人工智能工具未必总是研究界经过深思熟虑和研究的科研成果。因此，识别影响采纳的因素，并探究这些因素在多大程度上可用于预测教师与自适应学习平台的参与度，已成为日益关注的焦点。为解决这一问题，我们开发了一套可靠的测量工具，以更全面地衡量影响学校教师采纳自适应学习平台的因素。同时，我们报告了该工具在从大型国家级人群中抽样选取的学校教师（n=792）中的实施结果，并利用这些数据预测教师在学校中与自适应学习平台的实际参与度。研究结果表明，尽管教师的知识、信心和产品质量都是重要因素，但它们未必是唯一甚至未必是影响教师在学校中与人工智能平台参与度的最重要因素。不增加额外工作量、提升教师的自主权和信任感、建立支持求助机制，以及确保伦理问题最小化，同样是人工智能在学校中采纳的关键，并且可能更好地预测教师与平台的参与度。本文最后讨论了所识别因素的价值，这些因素通过增加预测模型的变异维度并减少实践中的实施变异，有望提升自适应学习平台的实际采纳效果与有效性。