Embedded training for commercial aviation
A learning management system may be configured to retrieve roster data from a roster database and determine from the roster data whether a pilot has a scheduled downtime during a flight or a layover time before the flight. The system may further retrieve flight data associated with the flight from the learning management system and determine a training concept associated with the flight. The system may also select a training exercise from multiple training exercises, where the training exercise is associated with the training concept. A notification may be sent to an electronic device associated with the pilot, where the notification includes an offer to perform the training exercise.
1. A method comprising:
retrieving, by a first electronic device, roster data from a roster database over a network, wherein the roster data maps flight crew members to scheduled events related to one or more flight;
determining, at the first electronic device, from the roster data whether a flight crew member has a scheduled period of downtime including a scheduled available time during a flight, a layover time before the flight, or another time that the flight crew member is available for training;
retrieving, by the first electronic device, flight data associated with the flight from a flight database over the network, wherein the flight data maps the flight to a timeline that includes aviation events related to the flight;
determining, at the first electronic device, a training concept associated with the flight based on the aviation events;
retrieving, by the first electronic device, regulatory data over the network, wherein the regulatory data maps the flight crew member to a set of training exercises;
selecting, at the first electronic device, a training exercise based on a determination by an artificial intelligence learning model that the training exercise satisfies at least one of the set of training exercises and provides knowledge and skills that relate to at least one of the aviation events associated with the training concept associated with the flight; and
sending a notification, from the first electronic device to a second electronic device associated with the flight crew member, wherein, in response to the notification, the second electronic device is configured to generate a query to the flight crew member and receive electronic input from the flight crew member to determine whether the flight crew member is available for the training exercise;
in response to an indication that the flight crew member is available for the training exercise, uploading the training exercise, from the first electronic device to the second electronic device
after performance of the training exercise, receiving, at the first electronic device, feedback from the flight crew member providing an indicator of relevance of the training exercise to the knowledge and skills that relate to the at least one of the aviation events; and
modifying, at the first electronic device, the artificial intelligence learning model based on the feedback.
2. The method of claim 1 , wherein the scheduled period of downtime includes a period during a long-haul flight when training is permitted by flight regulations.
3. The method of claim 1 , wherein the artificial intelligence learning model is a neural network.
4. The method of claim 1 , wherein the regulatory data corresponds to requirements by a regulatory authority or an airline.
5. The method of claim 1 , wherein selecting the training exercise further comprises:
retrieving sample data associated with multiple flight crew members, wherein the training exercise is selected based at least in part on the sample data.
6. The method of claim 5 , wherein the training exercise is selected based at least in part on a training history associated with the multiple flight crew members.
7. The method of claim 1 , wherein the selected training exercise includes a mid-fidelity simulation.
8. The method of claim 1 , wherein the flight crew member is a pilot, an aircraft engineer, a ground operation crew member, or a cabin crew member.
9. A learning management system comprising:
a network module configured to:
retrieve roster data from a roster database over a network, wherein the roster data maps flight crew members to scheduled events related to one or more flight;
retrieve flight data associated with the flight from a flight database over the network, wherein the flight data maps the flight to a timeline that includes aviation events related to the flight; and
retrieve regulatory data over the network, wherein the regulatory data maps a flight crew member to a set of training exercises;
memory storing multiple training exercises; and
a processor configured to:
determine from the roster data whether a flight crew member has a scheduled period of downtime including a scheduled available time during a flight, a layover time before the flight, or another time that the flight crew member is available for training;
determining a training concept associated with the flight based on the aviation events;
select a training exercise from the multiple training exercises based on a determination by an artificial intelligence learning model that the training exercise satisfies at least one of the set of training exercises and provides knowledge and skills that relate to at least one of the aviation events associated with the training concept associated with the flight; and
send a notification to an electronic device associate with the flight crew member, wherein the notification includes an offer to perform the training exercise;
in response to an indication that the flight crew member is available for the training exercise, upload the training exercise to the electronic device;
after performance of the training exercise, receive feedback from the flight crew member providing an indicator of relevance of the training exercise to the knowledge and skills that relate to the at least one of the aviation events; and
modify the artificial intelligence learning model based on the feedback.
10. The system of claim 9 , wherein the scheduled period of downtime includes a period during a long-haul flight when training is permitted by flight regulations.
11. The system of claim 9 , wherein the artificial intelligence learning model is a neural network.
12. The system of claim 9 , wherein the regulatory data corresponds to requirements by a regulatory authority or an airline.
13. The system of claim 9 , wherein the processor is further configured to retrieve sample data associated with multiple flight crew members, wherein the training exercise is selected based at least in part on the sample data.
14. The system of claim 13 , wherein the training exercise is selected based at least in part on a training history associated with the multiple flight crew members.
15. The system of claim 9 , wherein the selected training exercise includes a mid-fidelity simulation.
16. The system of claim 9 , wherein the flight crew member is a pilot, an aircraft engineer, a ground operations crew member, or a cabin crew member.
17. A system comprising a processor and memory, wherein the memory stores instruction that, when executed by the processor, cause the processor to:
retrieve, at the system, roster data from a roster database over a network, wherein the roster data maps flight crew members to scheduled events related to one or more flight;
determine, at the system, from the roster data whether a flight crew member has a scheduled downtime including a scheduled available time during a flight, a layover time before the flight, or another time that the flight crew member is available for training;
retrieve, at the system, flight data associated with the flight from a flight database over the network, wherein the flight data maps the flight to a timeline that includes aviation events related to the flight;
determine, at the system, a training concept associated with the flight based on the aviation events;
retrieving, by the system, regulatory data over the network, wherein the regulatory data maps the flight crew member to a set of training exercises;
select, at the system, a training exercise based on a determination by an artificial intelligence learning model that the training exercise satisfies at least one of the set of training exercises and provides knowledge and skills that relate to at least one of the aviation events associated with the training concept associate with the flight; and
send a notification to an electronic device associated with the flight crew member, wherein the notification includes an offer to perform the training exercise; and
in response to an indication that the flight crew member is available for the training exercise, upload the training exercise, from the system to the electronic device
after performance of the training exercise, receive, at the system, feedback from the flight crew member providing an indicator of relevance of the training exercise to the knowledge and skills that relate to the at least one of the aviation events; and
modify, at the electronic device, the artificial intelligence learning model based on the feedback.
18. The system of claim 17 , wherein the scheduled downtime includes a period during a long-haul flight when training is permitted by flight regulations.
19. The system of claim 17 , wherein the artificial intelligence learning model is a neural network.
20. The system of claim 17 , wherein the flight crew member is a pilot, an aircraft engineer, a ground operations crew member, or a cabin crew member.