ARTIFICIAL INTELLIGENT COGNITION THRESHOLD

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 stateand 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 aslearning 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 onuser interaction and other data. Collected data can also be used to adjust the base learning models. 表示性实施例公开了用于动态调整应用的用户界面和/或行为来适应连续且无干扰的学习的机制。随着用户对应用的熟练度增加，可以减少学习线索和对应用的其他改变。当用户失去熟练度时，可以增加学习线索和其他改变。用户情绪状态和对学习的开放度也可以用于实时增加和/或减