This sounds like a grounded use of LLMs. Presumably they're feeding indigenous-language text into the NN to build a model of how the language works. From this model one may then derive starting points for grammar, morphology, vocabulary, and so on. Like, how would you say "large language model" in Navajo? If fed data on Navajo neologisms, an LLM might come up with some long word that means "the large thing by means of which, one can teach metal to speak" or similar. And the tribal community can take, leave, or modify that suggestion but it's based on patterns that are manifest in the language which AI statistical methods can elicit.

Machine learning techniques are really, really good at finding statistical patterns in data they're trained on. What they're not good at is making inferences on facts they haven't been specifically trained to accommodate.