You can use a rating algorithm to learn the difficulty for each tsumego. If the user solved it, you count it like a win for the user, if she failed to solve it, count it like a win for the tsumego.
I’m pretty sure this would work ok. Training data could be the initial board state plus variants and the difficulty for example as measured by goproblems.com. Great project for a machine learning student.
A nice feature might be an AI assisted tsumego solver where users could enter their own tsumego problem or select a group to analyze at a specific move in one of their games. AI might go for a bigger move elsewhere, but you still might want to know if it was even possible to save that little invading group.
It looks like a program called Xuan Xuan Go has something like that, but you have to register for the full version (buy for 69 Euro) to get the tsumego solver; I have not tried it. I’m sure it can’t solve everything. DDK players would benefit from even a very limited tsumego solver. I’ve toyed with the idea of trying to write a simple brute-force tsumego solver, but as you get into larger problems, you would probably need a Neural Net to trim down the decision tree. I’m sure that would be quite a large undertaking (understatement). I’m no AI expert either; Just an electrical engineer who does a bit of coding on the side.