There is a lot of statistical literature on optimal experimental design, and it's used all the time. Years ago at Intel, we spent a lot of time on optimal design of quality control measurements, and I have no doubt a lot of industrial scientists in other companies spend their time thinking about such things.

The problem is, information is a model dependent concept (derivatives of log-likelihood depend on the likelihood), so if your prior isn't fairly strong, there isn't a lot of improvement to be had. A lot of science is exploratory, trying to optimize the experimental design is premature.

Either way, this isn't stuff you need an AI for at all, it's stuff people talk about and think about now, today, using computer assisted human intellect.