From Unknown to Known: An AI Coaching Problem in Open-World Environments

Large language models have been the state of the art for many tasks. Yet, whether their own competence can be beneficial to human learning of those tasks remains uncertain. We hypothesize the key is whether we can successfully infer the unknown-to-known reasoning process behind completing those tasks. We further ground the helping into two modules, router design and active helper. Tested on the popular open-world sandbox game Minecraft, our method consistently surpasses the performance of commonly used large language models.