Abstract:

The integration of artificial intelligence (AI)-powered materials into education signifies a dynamic shift in the way we approach learning. Although some of these technologies (e.g., ChatGPT) were not originally designed for educational purposes, they are progressively finding their way into educational settings, presenting a multitude of both promising prospects and complex challenges. A key argument here is that AI-powered materials should always be employed in educational contexts to meet identified human aims - i.e., what we want to be able to do, rather than what these technologies can best do. This paper explores the potential use of probabilistic programming languages (PPLs) within mathematics education, presenting an innovative and interdisciplinary model for developing tools and resource tailored to teaching and learning Bayesian statistics at both secondary school and undergraduate levels. This model aims to enable various stakeholders – including software developers, learners, practitioners, and researchers – to leverage the potential of PPLs in facilitating accessible and inclusive educational experience in Bayesian statistics.

Keywords:

AI-assisted Technologies in an Educational Context, AIED for Development, Bayesian Statistics, Learning Contexts and Informal Learning, Mathematics Education.