Interpretability of the underlying AI representations is a key raison'\\ᵉtre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring (ITS) research. OLMs provide tools for 'opening' up the AI models of' cognition and emotions for the purpose of supporting human learning teaching. Over thirty years of research in ITS (also known as AI in) produced important work, which informs about how AI can be used in to best effects and, through the OLM research, what are the necessary to make it interpretable and explainable for the benefit of. We argue that this work can provide a valuable starting point for a of interpretable AI, and as such is of relevance to the application both knowledge-based and machine learning systems in other high-stakes, beyond education.
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Conati et al. (2018) studied this question.