Abstract: The present disclosure provides computer-implemented methods, systems, and devices for generating multistep explanations for pedagogical exercises. A computing device receives a query from a user. The computing device determines that the query includes query data describing a pedagogical exercise to be solved. The computing device provides the query data as input to an explanatory machine-learned model. The computing device receives, as output from the explanatory machine-learned model, a pedagogical response, the pedagogical response including a multi-step explanation of a solution to the pedagogical exercise. The computing device provides the pedagogical response for display to a user.

Abstract: A method of ranking criticality of upcoming customer meetings may comprise receiving, via a network interface device, for a plurality of customers, a meeting deadline date, a meeting frequency value, and a previous sales factor rate indicating revenue or a percentage of previous meetings that resulted in revenue to a user, determining, via a processor, for the plurality of customers, a past due percentage value by dividing the number of days elapsed since the meeting deadline date by a number of days within the meeting frequency value, and determining, for each of the plurality of customers, a criticality ranking by weighting the past due percentage value with the previous sales factor rate. Displaying, via a graphical user interface (GUI), the plurality of customers, listed in order of the criticality ranking, from highest to lowest, for scheduling of upcoming meetings in order of criticality.