  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.
