International Business Machines Corporation

Cassius D'Helon

Vinay Kumar Reddy Kasireddy

Jae-Wook Ahn

Nirmal K Mukhi

Srijith N Prabhu

Fabrizio Jose Pisciotta Sanabria

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.

Automated decision making for selecting scaffolds after a partially correct answer in conversational intelligent tutor systems (ITS)

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.

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Cassius D'Helon