Conversational dialog systems are well known to be an effective tool for learning. Modern approaches to natural language processing and machine learning have enabled various enhancements to conversational systems but they mostly rely on text- or speech-only interactions, which puts limits on how learners can express and explore their knowledge. We introduce a novel method that addresses such limitations by adopting a visualization that is coordinated with a text-based conversational interface. This allows learners to seamlessly perceive and express knowledge through language and visual representations.

We present a practical application of serverless technology to implement a key component of a personalized tutoring system. We employ OpenWhisk[2] to orchestrate a set of microservices tasked with different responsibilities in the larger system. The tutor, which is trained on a specific set of content, is embodied as a multimodal, stateful dialog system. This desired experience brings with it unique challenges, prime among them being real-time interaction and the inherently stateful nature. We discuss the drivers for selecting serverless technology, the specific design choices we made and the issues we have faced. This work is in progress, and we conclude with some notes on paths we plan to explore as we continue to evolve the system and exploit this exciting new computing paradigm to help us achieve our goals.

Recent advances in artificial intelligence and natural language processing greatly enhance the capabilities of intelligent tutoring systems. However, gathering a subject-appropriate corpus of training data remains challenging. In order to address this issue, we present a system based on a hybrid Wizard-of-Oz technique, which enables cognitive systems to work in tandem with a human operator (the "wizard"), to enhance collection of dialog variants.