Modification of extended reality environments based on learning characteristics

1. A computer-implemented method comprising:

receiving a user profile associated with a user, wherein the user profile includes a set of learning characteristics related to the user;

displaying a simulation associated with a learning session on an extended reality (XR) display, wherein non-critical content is occluded from the simulation to create a clean background surrounding critical content related to the learning session based in part on the set of learning characteristics, and wherein the non-critical content comprises objects within a learning environment that are not related to the learning session;

monitoring focus data from one or more communicatively coupled Internet of Things (IoT) devices, wherein the focus data is generated while the user is viewing the simulation;

comparing the focus data with one or more focus thresholds related to the user, wherein the focus thresholds are based in part on correlating historical focus data of the user and the set of learning characteristics; and

inserting a virtual artifact within the simulation in response to the one or more focus thresholds being met.

2. The computer-implemented method of claim 1 , further comprising:

analyzing learning characteristics and focus data related to a plurality of users; and

grouping the plurality of users into one or more learning groups based in part on the plurality of users having similar learning characteristics and similar patterns in focus data,

wherein the displaying of the simulation is modified based in part on at least one of the set of learning characteristics of the user matching a learning characteristic within the one or more learning groups.

3. The computer-implemented method of claim 1 , wherein the set of learning characteristics are generated in part by analyzing a set of educational reports related to the user using natural language computing.

4. The computer-implemented method of claim 1 , wherein the set of learning characteristics are generated in part through gamification.

5. The computer-implemented method of claim 1 , wherein the focus data is collected through a feedback loop to capture interaction of the user when viewing the simulation.

6. The computer-implemented method of claim 1 , further comprising:

monitoring one or more performance factors of the user when the simulation is modified; and

adjusting the one or more focus thresholds based in part on the performance factors.

7. The computer-implemented method of claim 1 , wherein the virtual artifact is inserted into the clean background surrounding the critical content related to the learning session.

8. The computer-implemented method of claim 1 , wherein the virtual artifact is determined from visual content preferences in the user profile.

9. The computer-implemented method of claim 8 , wherein the visual content preferences are based on a focus level for virtual artifacts that the user finds appealing, and wherein the focus level is determined in part from the focus data.

10. The computer-implemented method of claim 9 , wherein the virtual artifacts that the user finds appealing include animated virtual artifacts.

11. The computer-implemented method of claim 8 , wherein the learning characteristics are selected from a group of learning characteristics consisting of: behavioral data, focus data, learning history, educational grades, and educational content.

12. The computer-implemented method of claim 1 , wherein occluding the non-critical content from the simulation comprises:

analyzing, using natural language computing, written and spoken content of the learning session associated to the simulation; and

determining, based on the analyzing, the critical content and the non-critical content related to the simulation.

13. The computer-implemented method of claim 1 , wherein the focus data includes heart rate data associated with the user.