Systems and methods for automated evaluation system routing are described herein. The system can include a memory, which can include a model database and a correlation database. The system can include a first user device and a second user device. The system can include at least one server. The at least one server can: receive a response communication from the user device; generate an initial evaluation value according to an AI model; determine a correlation between the initial evaluation value and evaluation range data; accept the initial evaluation value when the correlation exceeds a threshold value; and route the response communication to the second user device for generation of an elevated evaluation value when the correlation does not exceed the threshold value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for training of an AI model, the system comprising: memory comprising: a model database comprising a model for generating an evaluation value based on at least one computer-generated parameter of a response; a first user device comprising: a network interface configured to exchange data via the communication network; and an I/O subsystem configured to convert electrical signals to user interpretable outputs via a user interface; and at least one server configured to: receive an evaluated response communication comprising a response and an evaluation from the first user device; train the AI model in real-time with the received evaluated response communication; determine completion of the training of the AI model; and update a model status identifier when the AI model is trained. 2. The system of claim 1, wherein the at least one server is configured to: identify the AI model associated with the received evaluated response communication; and determine a training status of the identified AI model. 3. The system of claim 2, wherein the training status comprises one of: trained; or untrained. 4. The system of claim 2, wherein training the AI model in real-time comprises identifying features within the received response and a correlation between those identified features and the evaluation of the received response. 5. The system of claim 4, wherein training the AI model in real-time comprises providing the evaluated response communication for training irrespective of the size of a set of training data. 6. The system of claim 5, wherein the at least one server is further configured to add the received evaluated response communication to training data. 7. The system of claim 6, wherein determining completion of the training of the AI model comprises allocating some of the training data to a test set and applying the AI model to the test set. 8. The system of claim 7, wherein determining completion of the training of the AI model further comprises: comparing a result of the applying of the AI model to the test set to a performance parameter; and identifying the AI model as trained when the performance parameter is met. 9. The system of claim 8, wherein the at least one server is further configured to receive a response communication and generate an evaluation with the AI model when the AI model is trained. 10. A method for training an AI model, the method comprising: receiving an evaluated response communication comprising a response and an evaluation at at least one server from a first user device; training with the at least one server the AI model in real-time with the received evaluated response communication; determining with the at least one server completion of the training of the AI model; and updating with the at least one server a model status identifier when the AI model is trained. 11. The method of claim 10, further comprising: identifying with the at least one server the AI model associated with the received evaluated response communication; and determining with the at least one server a training status of the identified AI model. 12. The method of claim 11, wherein the training status comprises one of: trained; or untrained. 13. The method of claim 11, wherein training the AI model in real-time comprises identifying features within the received response and a correlation between those identified features and the evaluation of the received response. 14. The method of claim 13, wherein training the AI model in real-time comprises providing the evaluated response communication for training irrespective of the size of a set of training data. 15. The method of claim 14, further comprising adding the received evaluated response communication to training data. 16. The method of claim 15, wherein determining completion of the training of the AI model comprises allocating some of the training data to a test set and applying the AI model to the test set. 17. The method of claim 16, wherein determining completion of the training of the AI model further comprises: comparing a result of the applying of the AI model to the test set to a performance parameter; and identifying the AI model as trained when the performance parameter is met. 18. The method of claim 17, further comprising receiving a response communication and generating an evaluation with the AI model when the AI model is trained.