The best method for assessing performance when learning is as old as the world: assess the effort, not how well the result complies with some requirements.
If the level of effort made is high, but the outcome does not comply in some way, praise is due. If the outcome complies, but the level of effort is low, there is no reason for praise (what are you praising? mere compliance?) and you must have set a wrong bar.
Not doing this fosters people with mental issues such as rejection anxiety, perfectionism, narcissism, defeatism, etc. If you got good grades at school with little actual effort and the constant praise for that formed your identity, you may be in for a bad time in adulthood.
Teacher’s job is to determine the appropriate bar, estimate the level of effort, and to help shape the effort applied in a way that it improves the skill in question and the more general meta skill of learning.
The issue of judging by the outcome is prevalent in some (or all) school systems, so we can say LLMs are mostly orthogonal to that.
However, even if that issue was addressed, in a number of skills the mere availability of ML-based generative tools makes it impossible to estimate the level of actual effort and to set the appropriate bar, and I do not see how it can be worked around. It’s yet another negative consequence of making the sacred process of producing an amalgamation of other people’s work—something we all do all the time; passing it through the lens of our consciousness is perhaps one of the core activities that make us human—to become available as a service.