Agent Smith: Teaching Question Answering to Jill Watson
This addresses the problem of time-consuming AI agent training for educators and online course administrators, representing an incremental improvement.
The paper tackles the high cost of training AI question-answering agents like Jill Watson for new online classes by introducing Agent Smith, an interactive machine teaching agent that reduces training time by an order of magnitude.
Building AI agents can be costly. Consider a question answering agent such as Jill Watson that automatically answers students' questions on the discussion forums of online classes based on their syllabi and other course materials. Training a Jill on the syllabus of a new online class can take a hundred hours or more. Machine teaching - interactive teaching of an AI agent using synthetic data sets - can reduce the training time because it combines the advantages of knowledge-based AI, machine learning using large data sets, and interactive human-in-loop training. We describe Agent Smith, an interactive machine teaching agent that reduces the time taken to train a Jill for a new online class by an order of magnitude.