I see at least one good topic for this: Monte-Carlo algorithm to compute area or volume. It has a strong geometric side, is definitely probabilistic, only involves simple concepts, and is even relevant to real-life (of mathematicians).
Imagine we want to compute the area of a region in the plane, say a oddly-shaped pool in a fancy California hotel. Problem is, we are lazy and currently staying at the 20th floor's balcony of our room.. Good news is, we have a very large number of beers available in the minibar, we know the area of the rectangular garden where the pools lies and the pool is currently closed so we do not risk harming people in what follows.
Let simply drop randomly beer cans in the garden and count the amount of them that fall in the pool. If we divide this by the number of cans which did not fell out of the garden altogether, assuming we dropped them somewhat uniformly, we should get a good estimate of the ratio between the areas of the pool and the garden. Then we can deduce the former by multiplying the later by the above ratio.
I phrased it in a quite unscientific context, but the point is that this kind of method turns out to be indeed useful in applied maths to compute areas, or more generally higher-dimensional volumes.
Note that this method becomes unusable in high dimensions because irregular shape tend to occupy an exponentially small fraction of the least cube they are contained in. There are other methods available then, also using probabilities (randomly bouncing billiards, hit and run) which are implemented in computer to obtain volume estimates. In general, it is a hard problem to estimate the volume of a convex set in $\mathbb{R}^n$ given by linear constraints or as convex hulls of a given set of points, but I guess we are getting aside your current issue here.