  MineObserver 2.0 is an AI framework that uses Computer Vision and Natural
Language Processing for assessing the accuracy of learner-generated
descriptions of Minecraft images that include some scientifically relevant
content. The system automatically assesses the accuracy of participant
observations, written in natural language, made during science learning
activities that take place in Minecraft. We demonstrate our system working in
real-time and describe a teacher support dashboard to showcase observations,
both of which advance our previous work. We present the results of a study
showing that MineObserver 2.0 improves over its predecessor both in perceived
accuracy of the system's generated descriptions as well as in usefulness of the
system's feedback. In future work we intend improve system-generated
descriptions, give teachers more control and upgrade the system to perform
continuous learning to more effectively and rapidly respond to novel
observations made by learners.
