I am trying to understand the central limit theorem established for integrals.
Specifically, let $\left( X_n \right)$ be a sequence of random variables. I understand that under a set of conditions, for the sample mean $\bar{X}_N := \frac{1}{N} \sum_{i=1}^N X_i$, we have $\sqrt{N} \left( \frac{\bar{X}_N - \mu}{\sigma} \right) \overset{d}\to N\left(0,1\right)$.
$\textbf{My question is:}$ if, instead of looking at the sample mean $\bar{X}_N$, we look at the integral $\int_0^1 X(t) dt$, where $X(t)$ is some stochastic process on the interval $\left[0,1\right]$, such that $\int_0^1 X(t) dt$ is well-defined, then will $\frac{\int_0^1 X(t) dt - \mathbb{E}\left[\int_0^1 X(t) dt\right]}{\sqrt{\text{Var}\left(\int_0^1 X(t) dt\right)}}$ (or something in this format) converge to standard normal?
Thank you!