Abstract: At least one hardware processor implementing a conversational intelligent tutor system obtains, from a student, a partially correct natural language answer to a question posed by the conversational intelligent tutor system. The at least one hardware processor implementing the conversational intelligent tutor system accesses contextual knowledge related to the student. Based on the partially correct answer and the contextual knowledge, the at least one hardware processor applies at least one of a rule set and a predictive model to select at least one scaffolding option for the student. the at least one scaffolding option is caused to be displayed to the student.

Abstract: A method, computer program product and computer system for recovering from a likely exit condition in an electronic conversation with a user and interactive agent is provided. A processor retrieves one or more messages from a conversation between a user and an interactive agent. A processor determines at least one label for the one or more messages between a user and an interactive agent. A processor compares the at least one label for the one or more messages to a sequence model. A processor identifies the one or more messages from the conversation indicate an exit condition for the user based on the comparison of the at least one label for the one or more messages to the sequence model. A processor sends a recovery message to the user to redirect the conversation.

Abstract: Improving performance of a dialogue-based tutor includes receiving feedback from a user associated with a natural language interaction of the user with a dialogue-based tutoring (DBT) system. The DBT system includes training data and domain-specific content. One or more feedback categories is presented to the user based upon the feedback. A selection of a feedback category from among the one or more feedback categories is received from the user. A feedback candidate is generated based upon the feedback and the selected feedback category. The feedback candidate is indicative of a potential change in one or more of the training data and the domain-specific content based upon the feedback.

Abstract: At least one hardware processor implementing a conversational intelligent tutor system obtains, from a student, a partially correct natural language answer to a question posed by the conversational intelligent tutor system. The at least one hardware processor implementing the conversational intelligent tutor system accesses contextual knowledge related to the student. Based on the partially correct answer and the contextual knowledge, the at least one hardware processor applies at least one of a rule set and a predictive model to select at least one scaffolding option for the student. the at least one scaffolding option is caused to be displayed to the student.

Abstract: A method, computer program product and computer system for recovering from a likely exit condition in an electronic conversation with a user and interactive agent is provided. A processor retrieves one or more messages from a conversation between a user and an interactive agent. A processor determines at least one label for the one or more messages between a user and an interactive agent. A processor compares the at least one label for the one or more messages to a sequence model. A processor identifies the one or more messages from the conversation indicate an exit condition for the user based on the comparison of the at least one label for the one or more messages to the sequence model. A processor sends a recovery message to the user to redirect the conversation.

Abstract: Improving performance of a dialogue-based tutor includes receiving feedback from a user associated with a natural language interaction of the user with a dialogue-based tutoring (DBT) system. The DBT system includes training data and domain-specific content. One or more feedback categories is presented to the user based upon the feedback. A selection of a feedback category from among the one or more feedback categories is received from the user. A feedback candidate is generated based upon the feedback and the selected feedback category. The feedback candidate is indicative of a potential change in one or more of the training data and the domain-specific content based upon the feedback.