Abstract: Representative embodiments disclose mechanisms for dynamically adjusting the user interface and/or behavior of an application to accommodate continuous and unobtrusive learning. As a user gains proficiency in an application, the learning cues and other changes to the application can be reduced. As a user loses proficiency, the learning cues and other changes can be increased. User emotional state and openness to learning can also be used to increase and/or decrease learning cues and changes in real time. The system creates multiple learning models that account for user characteristics such as learning style, type of user, and so forth and uses collected data to find the best match. The selected learning model can be further customized to a single user. The model can also be tuned based on user interaction and other data. Collected data can also be used to adjust the base learning models.

Abstract: Representative embodiments disclose mechanisms for dynamically adjusting the user interface and/or behavior of an application to accommodate continuous and unobtrusive learning. As a user gains proficiency in an application, the learning cues and other changes to the application can be reduced. As a user loses proficiency, the learning cues and other changes can be increased. User emotional state and openness to learning can also be used to increase and/or decrease learning cues and changes in real time. The system creates multiple learning models that account for user characteristics such as learning style, type of user, and so forth and uses collected data to find the best match. The selected learning model can be further customized to a single user. The model can also be tuned based on user interaction and other data. Collected data can also be used to adjust the base learning models.