System and method for customizing learning interactions based on a user model

A learning system comprising:

• a non-transitory memory storing one or more user parameters associated with a user of the learning system;
• and
• one or more hardware processors coupled to the non-transitory memory and configured to execute instructions to cause the learning system to perform operations including:
• accessing a first learning interaction provided to a user of the learning system and a user-provided response to the first learning interaction;
• retrieving the one or more user parameters from the non-transitory memory;
• predicting, based on the one or more user parameters, a predicted response to the first learning interaction;
• comparing the predicted response to the user-provided response;
• in response to determining that the predicted response does not match the user-provided response, generating one or more updated user parameters;
• generating one or more learning assets based on the one or more updated user parameters, the one or more learning assets providing content for a second learning interaction;
• generating the second learning interaction based on the one or more learning assets;
• predicting, based on the one or more updated user parameters, whether or not each of a future knowledge of the user and an active knowledge of the user related to a particular task performed in a certain profession is below a specified threshold;
• storing the one or more updated user parameters in the non-transitory memory;
• and
• transmitting the one or more updated user parameters to an entity other than the user to provide feedback associated with the user;
• wherein
• the generating the one or more learning assets includes:
• accessing one or more knowledge sources;
• generating a text document based on a digital file included in the one or more knowledge sources accessed;
• selecting a plurality of knowledge items from the text document to extract the plurality of knowledge items from the one or more knowledge sources;
• filtering the plurality of knowledge items based on the one or more updated user parameters to form one or more filtered knowledge items;
• and
• building the one or more learning assets based on the one or more filtered knowledge items;
• the digital file included in the one or more knowledge sources accessed includes one or more of text, audio, and multimedia in an unstructured digital format;
• the generating the text document includes:
• generating a plurality of intermediate text documents from the digital file using a plurality of conversion techniques;
• selecting the text document from the plurality of intermediate text documents;
• and
• repairing the text document in response to the selecting of the text document from the plurality of intermediate text documents;
• and
• the predicted response is further predicted based on an interaction history associated with the user, the interaction history includes previous user-provided responses to previous learning interactions.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a learning system to perform operations including:

• accessing a first learning interaction provided to a user of the learning system, a user-provided response to the first learning interaction, and one or more user parameters associated with the user;
• predicting, based on the one or more user parameters, a predicted response to the first learning interaction;
• determining whether the predicted response matches the user-provided response;
• in response to determining that the predicted response does not match the user-provided response, updating the one or more user parameters;
• and
• generating one or more learning assets based on the one or more updated user parameters, the one or more learning assets providing content for a second learning interaction;
• generating the second learning interaction based on the one or more learning assets;
• predicting, based on the one or more updated user parameters, whether or not each of a future knowledge of the user and an active knowledge of the user related to a particular task performed in a certain profession is below a specified threshold;
• and
• transmitting the one or more updated user parameters to an entity other than the user to provide feedback associated with the user;
• wherein
• the generating the one or more learning assets includes:
• accessing one or more knowledge sources;
• generating a text document based on a digital file included in the one or more knowledge sources accessed;
• selecting a plurality of knowledge items from the text document to extract the plurality of knowledge items from the one or more knowledge sources;
• filtering the plurality of knowledge items based on the one or more updated user parameters to form one or more filtered knowledge items;
• and
• building the one or more learning assets based on the one or more filtered knowledge items;
• the digital file included in the one or more knowledge sources accessed includes one or more of text, audio, and multimedia in an unstructured digital format;
• the generating the text document includes:
• generating a plurality of intermediate text documents from the digital file using a plurality of conversion techniques;
• selecting the text document from the plurality of intermediate text documents;
• and
• repairing the text document in response to the selecting of the text document from the plurality of intermediate text documents;
• and
• the predicted response is further predicted based on an interaction history associated with the user, the interaction history includes previous user-provided responses to previous learning interactions.