  Interpretability of the underlying AI representations is a key raison
d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring
Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of
learners' cognition and emotions for the purpose of supporting human learning
and teaching. Over thirty years of research in ITS (also known as AI in
Education) produced important work, which informs about how AI can be used in
Education to best effects and, through the OLM research, what are the necessary
considerations to make it interpretable and explainable for the benefit of
learning. We argue that this work can provide a valuable starting point for a
framework of interpretable AI, and as such is of relevance to the application
of both knowledge-based and machine learning systems in other high-stakes
contexts, beyond education.
