An Interactive Conversational Agent to Aid Human Learning
In this project, we introduce an intelligent agent that can help humans learn a new subject by intelligently scheduling the introduction of new concepts from that subject to the learner. We explore the application of this agent on the task of language learning wherein we aim to help a learner improve their command on a new language. The agent keeps track of the current knowledge and capability of the learner and intelligently selects exercises for the user based on this current knowledge state. Our work aims to provide a method to deduce the knowledge state of a user using the user’s task history, such as past mistakes and results from tests. Our method involves a knowledge tracing model combined with a reinforcement learning agent to select the best possible next exercise for the user. The tested model significantly outperforms a random policy, which was selected as our baseline comparison. These successfully results demonstrate the capabilities of reinforcement learning as a viable technique for online learning modules. We have made all code and pre-trained models from this research public to encourage further research and development in this domain.
The code is available at: https://github.com/dheerajmpai/coach
Demo video: https://www.youtube.com/watch?v=ym1XnJ2X-a0