welcome to the community
I guess there's a confusion in your understanding of the kernel we use in case of rgb data. We normally use a kernel of equal number of channels as the input coming in (in this case as you mentioned it's RGB, so my number of channels for convolution operation would be 3). So instead of using a 3 X 3 Kernel, we use a 3 X 3 X 3 kernel. Weight matrix multiplication of kernel and image pixels happen channel-wise.
However, having said this, you can use a kernel of size 3 X 3 when input image is rgb, by specifying the stride as 1 in the third dimension. What this will do is convolute the kernel not only horizontally and vertically but also through the depth or specifically through the channels as well. I don't exactly know why one would like to do it.
Apart from this I guess the course or the video you are referring to might have specified '2D convolution on a 3D image'. That doesn't mean using a 2D kernel. And a 2D convolution on a 3D image uses a 3D kernel and after weight matrix multiplication you get a 2D image hence justifying the 2D convolution name.