  We propose an adaptive environment (CABINET) to support caselaw analysis
(identifying key argument elements) based on a novel cognitive computing
framework that carefully matches various machine learning (ML) capabilities to
the proficiency of a user. CABINET supports law students in their learning as
well as professionals in their work. The results of our experiments focused on
the feasibility of the proposed framework are promising. We show that the
system is capable of identifying a potential error in the analysis with very
low false positives rate (2.0-3.5%), as well as of predicting the key argument
element type (e.g., an issue or a holding) with a reasonably high F1-score
(0.74).
