Abstract: Systems and methods for automated content delivery and evaluation are disclosed herein. The system can include a memory. The memory can include a content library database including a plurality of problems and data for stepwise evaluation of each of the plurality of problems. The system can include at least one server. The at least one server can automatically decompose a content item into a plurality of potential steps and associate attributes with the potential steps. The at least one server can receive a response from a user for the content item, identify steps in the received response, and select a next action based the identified steps of the received response.

Abstract: Systems and methods for feature-based alert triggering are disclosed herein. The system can include memory including a model database containing a machine-learning algorithm. The system can include a user device that can receive inputs from a user; and at least one server. The at least one server can: receive electrical signals from the user device, the electrical signals corresponding to a plurality of user inputs provided to the user device; automatically generate input-based features from the received electrical signals; input the input-based features into the machine-learning algorithm; automatically and directly generate a risk prediction with the machine-learning algorithm from the input-based features; and generate and display an alert when the risk prediction exceeds a threshold value.

Abstract: Systems and methods for feature-based alert triggering are disclosed herein. The system can include memory including a model database containing a machine-learning algorithm. The system can include a user device that can receive inputs from a user; and at least one server. The at least one server can: receive electrical signals from the user device, the electrical signals corresponding to a plurality of user inputs provided to the user device; automatically generate input-based features from the received electrical signals; input the input-based features into the machine-learning algorithm; automatically and directly generate a risk prediction with the machine-learning algorithm from the input-based features; and generate and display an alert when the risk prediction exceeds a threshold value.

Abstract: A system and method for generating a synchronized mix of a plurality of audiovisual tracks is provided. A new audiovisual track may be recorded simultaneously while one or more other audiovisual tracks are being played to help simulate a live jam session as closely as possible. In a “DJ Mix” mode, a user may manipulate sequencing and/or characteristics related to one or more audiovisual tracks included in a project in real-time while the project (e.g., song) is being played. A synchronized mix of a plurality of audiovisual tracks may be generated based on various attributes associated with the audiovisual tracks. For example, audiovisual tracks that are recorded along with a metronome may be synchronized based on a metronome clock rate selected by a user.

Abstract: A system and method for generating a synchronized mix of a plurality of audiovisual tracks is provided. A new audiovisual track may be recorded simultaneously while one or more other audiovisual tracks are being played to help simulate a live jam session as closely as possible. In a “DJ Mix” mode, a user may manipulate sequencing and/or characteristics related to one or more audiovisual tracks included in a project in real-time while the project (e.g., song) is being played. A synchronized mix of a plurality of audiovisual tracks may be generated based on various attributes associated with the audiovisual tracks. For example, audiovisual tracks that are recorded along with a metronome may be synchronized based on a metronome clock rate selected by a user.