Method and system for automated content selection and delivery

Abstract

Systems and methods of artificial intelligence based recommendation are disclosed herein. The system can include: a user device including: a network interface; and an I/O subsystem. The system can include an artificial intelligence engine that can provide a remediation dialogue. The system can include a content management server that can: receive a user identification identifying a user from the user device; retrieve user information from a memory; identify and deliver a question to the user device based on the retrieved user information; receive a response to the delivered question from the user device; determine that the received response is incorrect; trigger the launch of the artificial intelligence engine; receive an indication of completion of the dialogue; and provide a second question after receipt of the indication of completion of the dialogue.

What is claimed is: 
     
         1 . A recommendation system comprising:
 memory comprising:
 a user profile database comprising information relating to a plurality of users, wherein the information relating to the plurality of users comprises unique user history data associated with each of the users in the plurality of users; and 
 a content library database comprising a plurality of nodes linked in a plurality of sequential relationships in a content network, wherein some of the plurality of nodes are associated with guard conditions, wherein each guard condition identifies at least one of: an entrance condition controlling entrance into the node associated with the guard condition; and an exit condition controlling exit from the node associated with the guard condition; 
   a 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 
   one or more servers configured to:
 receive user identification information, wherein the user identification information identifies a user; 
 determine a location of the user identified by the received user identification information in the content network; 
 select a next node based on the location of the user in the content network and on at least some of the guard conditions; 
 deliver next node content to the user device via a communication network, wherein the next node content is adaptively selected from a plurality of selectable content options when the next node is a placeholder node, and wherein the next content is directly linked to the next node when the next node is not a placeholder node. 
   
     
     
         2 . The system of  claim 1 , wherein at least some of the guard conditions regulate entry into a learning sequence comprising some of the plurality of nodes. 
     
     
         3 . The system of  claim 1 , wherein at least some of the guard conditions regulate exit from a learning sequence comprising some of the plurality of nodes. 
     
     
         4 . The system of  claim 1 , further comprising an artificial intelligence engine configured to receive inputs from at least the user device, and wherein at least one of the selectable content options of the placeholder node comprises a dialogue with the artificial intelligence engine. 
     
     
         5 . The system of  claim 4 , wherein the artificial intelligence engine is configured to:
 launch the dialogue;   deliver interrogation and content based on the received user responses; and   determine termination of the dialogue based on a user performance metric indicative of a user skill level, wherein the dialogue is determined for termination when the user performance metric identifies a sufficient user skill level.   
     
     
         6 . The system of  claim 5 , wherein the remediation dialogue comprises multiple levels of interrogation and response. 
     
     
         7 . The system of  claim 6 , wherein the artificial intelligence engine is configured to reevaluate the user skill level subsequent to each level of interrogation and response. 
     
     
         8 . The system of  claim 7 , wherein the reevaluation of the user skill level comprises application of a natural language processing algorithm to the received response, wherein the natural language processing algorithm comprises a speech recognition algorithm and natural language understanding algorithm. 
     
     
         9 . The system of  claim 1 , wherein the one or several servers are further configured to determine a user skill level based on user responses received relating to the delivered next node content. 
     
     
         10 . The system of  claim 1 , further comprising a supervisor device in communicating connection with the one or several servers via the communication network, wherein the one or several servers are further configured to send an alert to the supervisor device when the determined user skill level drops below a predetermined threshold value, wherein the alert comprises computer code configured to direct the supervisor device to display the alert upon receipt. 
     
     
         11 . A method of content selection and delivery, the method comprising:
 receiving user identification information at one or several servers from a user device via a communication network, wherein the user identification information identifies a user;   determining with the one or several servers a location of the user identified by the received user identification information in the content network;   selecting with the one or several servers a next node based on the location of the user in the content network and on at least some of the guard conditions; and   delivering with the one or several servers next node content to the user device via the communication network, wherein the next node content is adaptively selected from a plurality of selectable content options when the next node is a placeholder node, and wherein the next content is directly linked to the next node when the next node is not a placeholder node.   
     
     
         12 . The method of  claim 11 , wherein at least some of the guard conditions regulate entry into a learning sequence comprising some of the plurality of nodes. 
     
     
         13 . The method of  claim 11 , wherein at least some of the guard conditions regulate exit from a learning sequence comprising some of the plurality of nodes. 
     
     
         14 . The method of  claim 11 , wherein the next content comprises an intervention. 
     
     
         15 . The method of  claim 14 , wherein the intervention comprises at least one of: targeted remediation content; a scaffolding hint; question generation; answer specific feedback; a text; a text summary; and a follow-up question. 
     
     
         16 . The method of  claim 11 , wherein the next content directly linked to the next node comprises a dialogue with an artificial intelligence engine, wherein the artificial intelligence engine is configured to receive inputs from at least the user device. 
     
     
         17 . The method of  claim 11 , wherein at least one of the selectable content options of the placeholder node comprises a dialogue with an artificial intelligence engine, wherein the artificial intelligence engine is configured to receive inputs from at least the user device. 
     
     
         18 . The method of  claim 17 , further comprising:
 launching the dialogue with the artificial intelligence engine;   delivering interrogation and content based on the received user responses from the artificial intelligence engine to the user device; and   terminating the dialogue with the artificial intelligence engine based on at least one halting criterion.   
     
     
         19 . The method of  claim 18 , wherein the halting criteria comprises a termination threshold, and wherein terminating the dialogue comprises: generating a user performance metric indicative of at least one of: a skill level; a correct response; and an incorrect response; and comparing the user performance metric to the termination threshold. 
     
     
         20 . The method of  claim 19 , wherein the dialogue is terminated when the user performance metric identifies a sufficient user skill level. 
     
     
         21 . The method of  claim 18 , wherein the remediation dialogue comprises multiple levels of interrogation and response. 
     
     
         22 . The method of  claim 21 , further comprising reevaluating the user skill level with the artificial intelligence engine subsequent to each level of interrogation and response. 
     
     
         23 . The method of  claim 22 , wherein the reevaluation of the user skill level comprises application by the one or several servers of a natural language processing algorithm to the received response, wherein the natural language processing algorithm comprises a speech recognition algorithm and natural language understanding algorithm. 
     
     
         24 . The method of  claim 11 , further comprising determining a user skill level with the one or several servers based on user responses received relating to the delivered next node content. 
     
     
         25 . The method of  claim 11 , further comprising sending an alert from the one or several servers to a supervisor device when the determined user skill level drops below a predetermined threshold value, wherein the alert comprises computer code directing the supervisor device to display the alert upon receipt.