Chat card engine(s) for conversational chat cards

A method comprising:

• receiving, from a client device, an indication to start a chat card exercise;
• determining, by a chat card engine, a source document based on the chat card exercise;
• determine, by the chat card engine, a plurality of subtopics within the source document;
• generating, by the chat card engine, a plurality of chat cards based on the source document, wherein each of the plurality of chat cards correspond to a respective subtopic of the plurality of subtopics present in the source document;
• generating, by a virtual assistant integrated with the chat card engine and configured as an artificial intelligence (AI) tutor, a first prompt based on a first subtopic;
• providing, by the chat card engine via the virtual assistant, a first chat card comprising the first prompt to the client device, wherein the plurality of chat cards comprises the first chat card;
• receiving, by the chat card engine via the virtual assistant, a first query from the client device responsive to the first prompt;
• generating, by a Large Language Model (LLM) operably coupled with the chat card engine, an understanding score based on an interaction history within the first chat card, wherein the interaction history comprises at least the first query and the first prompt;
• generating, by the chat card engine via the virtual assistant, a second prompt based on the understanding score;
• and
• providing, by the chat card engine via the virtual assistant, the second prompt to the client device on the first chat card.

A computer readable storage media comprising processor-executable instructions configured to cause one or more processors to:

• receive, from a client device, an indication to start a chat card exercise;
• determine, by a chat card engine, a source document based on the chat card exercise;
• determine, by the chat card engine, a plurality of subtopics within the source document;
• generate, by the chat card engine, a plurality of chat cards based on the source document, wherein each of the plurality of chat cards corresponds to a respective subtopic of the plurality of subtopics present in the source document;
• generate, by a virtual assistant integrated with the chat card engine and configured as an artificial intelligence (AI) tutor, a first prompt based on a first subtopic;
• generate, by the chat card engine via the virtual assistant, a first chat card comprising the first prompt;
• receive, by the chat card engine via the virtual assistant, a first query from the client device responsive to the first prompt;
• generate, by a Large Language Model (LLM) operably coupled with the chat card engine, an understanding score based on an interaction history within the first chat card, wherein the interaction history comprises at least the first query and the first prompt;
• generate, by the chat card engine via the virtual assistant, a second prompt based on the understanding score;
• and
• provide, by the chat card engine via the virtual assistant, the second prompt to the client device on the first chat card;
• generate, by the chat card engine via the virtual assistant, a second prompt based on the understanding score;
• and
• provide, by the chat card engine via the virtual assistant, the second prompt to the client device on the first chat card.