Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: A system and method are presented. User metadata defining a set of activities undertaken by a user are received by a processor. The processor determines, based on the set of activities, a skill history for the user. The skill history identifies a first plurality of skills associated with the user. The processor determines a preferred career associated with the user, determines, for the preferred career and by accessing a career skill repository, a second plurality of skills associated with the preferred career, determines a missing skill by comparing the first plurality of skills associated with the user to the second plurality of skills associated with the preferred career to identify the missing skill that is in the second plurality of skills and not in the first plurality of skills and outputs a user interface identifying the missing skill.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.