Abstract
Developing the problem solving skills of children is a challenging problem that is crucial for the future of our society. Given that artificial intelligence (AI) has been used to solve problems across a wide variety of domains, AI offers unique opportunities to develop problem solving skills using a multitude of tasks that pique the curiosity of children. To make this a reality, it is necessary to address the uninterpretable “black-box” that AI often appears to be. Towards this goal, we design a collaborative artificial intelligence algorithm that uses a human-in-the-loop approach to allow students to discover their own personalized solutions to problems. This collaborative algorithm builds on state-of-the-art AI algorithms and leverages additional interpretable structures, namely knowledge graphs and decision trees, to create a fully interpretable process that is able to explain solutions in their entirety. We describe this algorithm when applied to solving the Rubik’s cube as well as our planned user-interface and assessment methods.
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