Generative machine learning methods for adaptive target learning systems

1 . A non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to:

generate and display, at an interactive graphical interface of a subject user computing device, a graph structure comprising a plurality of concurrently displayed quest lines emanating from a central node representing a subject user by:

generating and displaying a first quest line comprising a first acyclic sequence of a first set of memory nodes, each first memory node in the first set of memory nodes storing a first set of interactive elements that cause at least one first recommended operation to validate a first target learning feature for the subject user; and

generating and displaying a second quest line comprising a second acyclic sequence of a second set of memory nodes, each second memory node in the second set of memory nodes storing a second set of interactive elements that cause at least one second recommended operation to validate a second target learning feature of the subject user,

wherein the first quest line and the second quest line intersect only at the central node;

receive an execution signal indicating user execution of the at least one first recommended operation for a first memory node of the first set of memory nodes, the execution signal comprising one or more performance measures associated with usage of the first set of interactive elements for the at least one first recommended operation by the subject user;

determine that the one or more performance measures satisfy a tolerance threshold for validating the first target learning feature; and

responsive to determining that the one or more performance measures satisfy the tolerance threshold for validating the first target learning feature:

access stored historical records comprising one or more prior performance measures associated with prior user execution of recommended operations to validate at least one prior learning feature;

input the one or more performance measures of the first target learning feature and the one or more prior performance measures of the at least one prior learning feature into a generative machine learning model to generate an additional target learning feature for the subject user and at least one additional recommended operation to validate the additional target learning feature;

selectively determine an additional set of interactive elements that cause the at least one additional recommended operation to validate the additional target learning feature;

automatically adjust, at the interactive graphical interface, the first quest line of the graph structure to selectively reveal an additional memory node within the first set of memory nodes, the additional memory node storing the additional set of interactive elements; and

using at least one of a validated first target learning feature and additional target learning feature, generate and selectively reveal at the interactive graphical interface a corresponding learning feature category.

2 . The non-transitory, computer-readable storage medium of claim 1 , the instructions further cause the system to:

receive, via the interactive graphical interface, a user selection of at least one graphical indicator representing the first memory node in the first set of memory nodes; and

automatically generate and display, at the interactive graphical interface, a sequential arrangement of one or more graphical containers, each graphical container indicating a subset of interactive elements from the first set of interactive elements which, when used by the subject user, are configured to cause a portion of the at least one first recommended operation.

3 . The non-transitory, computer-readable storage medium of claim 2 ,

wherein a priority rating is assigned to the portion of the at least one first recommended operation associated with each displayed graphical container, and

wherein the sequential arrangement of the one or more graphical containers is ordered based, in part, on the priority rating assigned to the portion of the at least one first recommended operation of each displayed graphical container.

4 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

receive, via the interactive graphical interface, a user selection of at least one graphical indicator representing an intermediary memory node within the first set of memory nodes; and

automatically adjust, at the interactive graphical interface, the graph structure to display a limited quantity of memory nodes within a relational proximity of the intermediary memory node.

5 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

receive, via the interactive graphical interface, user activation of at least one interactive element from the first set of interactive elements stored by the first memory node; and

automatically generate and display, at the interactive graphical interface, a third set of interactive elements which, when used by the subject user, are configured to cause a portion of the at least one first recommended operation.

6 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

receive, via the interactive graphical interface, a seed dataset comprising:

(1) a first set of descriptive attributes indicating one or more latent learning capacities associated with the subject user, and

(2) a second set of descriptive attributes indicating at least one terminal learning feature for the subject user;

selectively determine, using the first set of descriptive attributes of the seed dataset, at least one core memory node storing a predefined set of interactive elements that cause recommended operations for at least one predefined learning feature;

generate, using the second set of descriptive attributes of the seed dataset, an ordered sequence of one or more target learning features incrementing from the at least one predefined learning feature to the at least one terminal learning feature;

for each target learning feature within the ordered sequence of one or more target learning features:

input the first set of descriptive attributes, the second set of descriptive attributes, and the target learning feature into the generative machine learning model to determine one or more recommended operations for validating the target learning feature; and

generate an intermediary memory node storing a set of interactive elements that cause the one or more recommended operations to validate the target learning feature; and

generate and display, at the interactive graphical interface, an acyclic sequence of memory nodes that sequentially links the intermediary memory nodes of the ordered sequence of one or more target learning features from the at least one core memory node.

7 . The non-transitory, computer-readable storage medium of claim 6 ,

wherein the generated acyclic sequence of memory nodes is the first acyclic sequence of the first set of memory nodes, and

wherein the first memory node and the additional memory node are intermediary memory nodes of the generated acyclic sequence of memory nodes.

8 . The non-transitory, computer-readable storage medium of claim 1 , wherein the one or more performance measures of the first target learning feature comprises recorded distance metrics between the first target learning feature and a terminal learning feature for the subject user.

9 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

access a user account associated with the subject user that stores allocable resources for a digital exchange platform; and

add a predefined quantity of the allocable resources to the user account when the one or more performance measures satisfy the tolerance threshold.

10 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to:

generate and display, at the interactive graphical interface, the second quest line by generating a branching sequence comprising (i) a first branch that includes a first subset of nodes in the second acyclic sequence and (ii) a second branch that includes a second subset of nodes in the second acyclic sequence.

11 . The non-transitory, computer-readable storage medium of claim 10 , wherein the instructions further cause the system to:

generate and display, at the interactive graphical interface, a convergence point that merges the first branch and the second branch.

12 . The non-transitory, computer-readable storage medium of claim 11 , wherein the instructions further cause the system to:

apply at least two of an animation, a border highlight, or a call to action to the displayed convergence point.

13 . The non-transitory, computer-readable storage medium of claim 1 , wherein the instructions further cause the system to apply probability data to determine and cache a predicted additional memory node.

14 . A system comprising:

at least one hardware processor; and

at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:

generate and display, at an interactive graphical interface of a subject user computing device, a graph structure comprising a plurality of concurrently displayed quest lines emanating from a central node representing a subject user by:

generating and displaying a first quest line comprising a first acyclic sequence of a first set of memory nodes, each first memory node in the first set of memory nodes storing a first set of interactive elements that cause at least one first recommended operation to validate a first target learning feature for the subject user; and

generating and displaying a second quest line comprising a second acyclic sequence of a second set of memory nodes, each second memory node in the second set of memory nodes storing a second set of interactive elements that cause at least one second recommended operation to validate a second target learning feature of the subject user,

wherein the first quest line and the second quest line intersect only at the central node;

receive an execution signal indicating user execution of the at least one first recommended operation for a first memory node of the first set of memory nodes, the execution signal comprising one or more performance measures associated with usage of the first set of interactive elements for the at least one first recommended operation by the subject user;

determine that the one or more performance measures satisfy a tolerance threshold for validating the first target learning feature; and

responsive to determining that the one or more performance measures satisfy the tolerance threshold for validating the first target learning feature:

access stored historical records comprising one or more prior performance measures associated with prior user execution of recommended operations to validate at least one prior learning feature;

input the one or more performance measures of the first target learning feature and the one or more prior performance measures of the at least one prior learning feature into a generative machine learning model to generate an additional target learning feature for the subject user and at least one additional recommended operation to validate the additional target learning feature;

selectively determine an additional set of interactive elements that cause the at least one additional recommended operation to validate the additional target learning feature; and

automatically adjust, at the interactive graphical interface, the first quest line of the graph structure to selectively reveal an additional memory node within the first set of memory nodes, the additional memory node storing the additional set of interactive elements.

15 . The system of claim 14 further caused to:

receive, via the interactive graphical interface, a user selection of at least one graphical indicator representing the first memory node in the first set of memory nodes; and

automatically generate and display, at the interactive graphical interface, a sequential arrangement of one or more graphical containers, each graphical container indicating a subset of interactive elements from the first set of interactive elements which, when used by the subject user, are configured to cause a portion of the at least one first recommended operation.

16 . The system of claim 15 ,

wherein a priority rating is assigned to the portion of the at least one first recommended operation associated with each displayed graphical container, and

wherein the sequential arrangement of the one or more graphical containers is ordered based, in part, on the priority rating assigned to the portion of the at least one first recommended operation of each displayed graphical container.

17 . The system of claim 14 further caused to:

receive, via the interactive graphical interface, a user selection of at least one graphical indicator representing an intermediary memory node within the first set of memory nodes; and

automatically adjust, at the interactive graphical interface, the graph structure to display a limited quantity of memory nodes within a relational proximity of the intermediary memory node.

18 . The system of claim 14 further caused to:

receive, via the interactive graphical interface, user activation of at least one interactive element from the first set of interactive elements stored by the first memory node; and

automatically generate and display, at the interactive graphical interface, a third set of interactive elements which, when used by the subject user, cause a portion of the at least one first recommended operation.

19 . The system of claim 14 further caused to:

access a user account associated with the subject user that stores allocable resources for a digital exchange platform; and

add a predefined quantity of the allocable resources to the user account when the one or more performance measures satisfy the tolerance threshold.

20 . A computer-implemented method comprising:

generating and displaying, at an interactive graphical interface of a subject user computing device, a graph structure comprising a plurality of concurrently displayed quest lines emanating from a central node representing a subject user by:

generating and displaying a first quest line comprising a first acyclic sequence of a first set of memory nodes, each first memory node in the first set of memory nodes storing a first set of interactive elements that cause at least one first recommended operation to validate a first target learning feature for the subject user; and

generating and displaying a second quest line comprising a second acyclic sequence of a second set of memory nodes, each second memory node in the second set of memory nodes storing a second set of interactive elements that cause at least one second recommended operation to validate a second target learning feature of the subject user,

wherein the first quest line and the second quest line intersect only at the central node;

receiving an execution signal indicating user execution of the at least one first recommended operation for a first memory node of the first set of memory nodes, the execution signal comprising one or more performance measures associated with usage of the first set of interactive elements for the at least one first recommended operation by the subject user;

determining that the one or more performance measures satisfy a tolerance threshold for validating the first target learning feature; and

responsive to determining that the one or more performance measures satisfy the tolerance threshold for validating the first target learning feature, performing operations comprising:

accessing stored historical records comprising one or more prior performance measures associated with prior user execution of recommended operations to validate at least one prior learning feature;

inputting the one or more performance measures of the first target learning feature and the one or more prior performance measures of the at least one prior learning feature into a generative machine learning model to generate an additional target learning feature for the subject user and at least one additional recommended operation to validate the additional target learning feature;

selectively determining an additional set of interactive elements that cause the at least one additional recommended operation to validate the additional target learning feature; and

automatically adjusting, at the interactive graphical interface, the first quest line of the graph structure to selectively reveal an additional memory node within the first set of memory nodes, the additional memory node storing the additional set of interactive elements.