One issue that traditional AI Agent reasoning built on Theory of Mind(ToM) (ToM: I know what you are thinking, so I can predict what you will do next.) was hard

One issue that traditional AI Agent reasoning built on Theory of Mind(ToM) (ToM: I know what you are thinking, so I can predict what you will do next.) was hard to get right was because the domain model and interface was quite tough to design. Agents did not fully understand and model the belief state of the person. But more importantly, there was possibly also no way to quickly _generate_ lots of plans and test them in a Domain independent manner (RHLF is horribly inefficient for this). Now there are AI agents are great at generating plans (examples of what could work) that can be tested with kids and creators and provide feedback to tune not just LLMs but also add to the Domain models. This can solve a lot of interesting problems that have plagued tutoring systems: 1. Improve sample efficiency of generated plans to get to the right goal while meeting all the subgoals for a plan. 2. Enrich engineered and correct neuro-symbolic models of reasoning with better knowledge representations. 3. Quantitative psychometric systems that use metrics like proficiency could only be as good as the underlying “factor” model and hypothesis, but one could test multiple more interesting hypothesis more quickly: "Are you mentally tiered?", "Here is a hint: ___. Does that help?".