EDIT: Showing this using Ito's lemma is easy, that's NOT what I want to do. I also realised that $2\mathbb{E}[S\xi]\neq 2\xi\mathbb{E}[S]$ since $\xi$ is also a random variable. Nontheless, if this is the case I have no idea how to calculate the expectation of $S\xi$ anyway.
Given a Brownian motion $W(t)$ I want to show that
\begin{align} \lim_{n\rightarrow\infty}\mathbb{E}\left[\left|\sum_{j=0}^{n-1}\frac{jT}{n}\left(W\left(\frac{(j+1)T}{n}\right)-W\left(\frac{jT}{n}\right)\right) - TW(T)+\int\limits_0^TW(t)dt\right|^2\right]=0 \tag1. \end{align}
For simplicity we denote the sum by $S$ and set $\xi=-TW(t)+\int_{0}^TW(t) \ dt$, now we have that \begin{align} \mathbb{E}[(S+\xi)^2]=\mathbb{E}[S^2]+2\xi\mathbb{E}[S]+\xi^2. \end{align}
However I have trouble calculating $\mathbb{E}[S^2]$. I know that $\mathbb{E}[S]=0$ since
\begin{equation} \mathbb{E}[S]=\sum_{j=0}^{n-1}\frac{jT}{n}\mathbb{E}[W_{j+1}-W_j]=0 \end{equation} since the increments are $\sim\mathcal{N}(0,T/n)$ so the sum above is just a sum of zeroes. So I'm kind of left to show that
\begin{equation} \lim_{n\rightarrow\infty}\mathbb{E}[S^2]=-\xi^2. \end{equation}
I tried the following: For Brownian motion I know that $\mathbb{E}[S^2]=\text{Var}[S]$ so
\begin{align} \mathbb{E}[S^2]&=\sum_{j=0}^{n-1}\frac{j^2T^2}{n^2}\text{Var}[W_{j+1}-W_j]=\sum_{j=0}^{n-1}\frac{j^2T^2}{n^2}\frac{T}{n}=\frac{T^3}{n^3}\sum_{j=0}^{n-1}j^2\\&={\frac {{T}^{3} \left( 2\,{n}^{2}-3\,n+1 \right) }{6{n}^{2}}} \end{align}
which gives $T^3/3$ when $n\rightarrow\infty$. But this is not the result I want.
So you might ask how did I end up in (1)? Well, I wanted to show, using the definition of the Ito integral that
$$\int_0^TW(t)dt+\int_0^TtdW(t) = TW(T).$$
I rewrote it as
$$\int_0^TtdW(t) = TW(T) - \int_0^TW(t)dt$$
and used the definition: If there exists a stochastic process $I(T)$ such that $||I_n(T)-I(T)||_2=\lim\limits_{n\rightarrow\infty}\mathbb{E}[|I_n(T)-I(T)|^2]=0$ then $I(T)$ is an Ito integral. Plugging in $I_n(T)$ and $I(T)$ I arrived to (1).