Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving tens of thousands of parameters or more. Though strides towards interpretation of complex models are being made in core machine learning communities, it remains true in these cases of "black box" modeling that research teams may have little possibility to peer inside to try understand how, why, or even whether such models will work when applied beyond the data on which they were built. Rather than relying on AI expertise alone, we suggest that "learning engineering teams" bring interdisciplinary expertise to bear to develop "explanatory learner models" that provide interpretable and actionable insights in addition to accurate prediction. We describe examples that illustrate use of different kinds of data (eg, click stream and discourse data) in different course content (eg, math and writing) and toward different goals (eg, improving student models and generating actionable feedback). We recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning