Inverse DFT: Is there a valid / intuitive interpretation of results for non-integral timestamps?
Andy Walls provided the answer with the direct interpretation of an N-point inverse DFT with fractional time stamps. However, there is a slighty different (or totally should I've said) interpretation of a non-integer IDFT argument, which reduces to a good interpolation formula from the inverse DFT.
Consider the notation with $N,M,k,n$ being integers.
$$x_i[n] = \frac{1}{N} \sum_{k} X[k] \cdot e^{ j \frac{2\pi}{N} k (n) } \tag{0} $$
Lets say Eq.0 is a valid inverse DFT for some N-point $X[k]$, and results in a sequence $x_i[n]$, we ask what happens if we evaluate the sum at $n = n/M$; i.e., can we also plug $n/M$ into RHS $x_i[n]$ as given by Eq.1 below ?
$$x_i[n/M] = \frac{1}{N} \sum_{k} X[k] \cdot e^{ j \frac{2\pi}{N} k (n/M) } \tag{1} $$
Where $X[k]$ is the $N$-point DFT of some sequence $x[n]$, and $x_i[n]$ is the time sequence that result from the inverse transform evaluated at non-integer time indices. And the range of summation is deliberately left out.
With the index notation of $n/M$ what's implied is not very clear. Plugging $n/M$ into an inverse DFT is formally not allowed as the argument $n/M$ is meaningless for the time-domain sequence. Yet it's possible to interpret the notation as an indicator of an oversampling of the implied interpolation applied on the $x[n]$ sequence.
In that sense, I would re-write the inverse DFT as
$$x_i[n] = \frac{M}{L} \sum_{k} X[k] \cdot e^{ j \frac{2\pi}{N*M} k n } \tag{2}$$
Now $x_i[n]$ looks like an $L = M \times N$ point inverse DFT of the N-point forward DFT $X[k]$ of $x[n]$. But there're two alternative interpretations; the first one is explained by Andy Walls, in which the inverse DFT is applied to the zero appended DFT $X[k]$ which looks like:
$$x_i[n] = \frac{M}{L} \sum_{k=0}^{L-1} X[k] \cdot e^{ j \frac{2\pi}{L} k n } = \frac{M}{L} \sum_{k=0}^{N-1} X[k] \cdot e^{ j \frac{2\pi}{L} k n } \tag{3}$$
Using Matlab you can implement Eq.3 as:
xi = M*ifft( fft(x,N), L );

This is a terrible interpolation, as explained in the other answer.
But I would call it terrible because of the miss-placed zeros in the inverse DFT formula. It performs zero padding by appending zeros to the tail of $X[N-1]$ until the end $k=L-1$
Now, the other alternative is to add the zeros into the middle of $X[k]$, with the following associated inverse DFT, for $N$ even :
$$x_i[n] = \frac{M}{L} \big( \sum_{k=0}^{N/2} X[k] \cdot e^{ j \frac{2\pi}{L} k n } +\sum_{k=N/2+1}^{L-N/2} 0 \cdot e^{ j \frac{2\pi}{L} k n } +  \sum_{k=L+1-N/2}^{L-1} X[k] \cdot e^{ j \frac{2\pi}{L} k n } \big) \tag{4}$$
In Eq.4, the orignal $N$-point DFT $X[k]$ is repeated $M$ times, (indicator of expanding $x[n]$ by $M$), and windowed by an $N$ point rectangular window with a symmetric placement (representing the frequency domain effect of an ideal lowpass brickwall filter applied on the expanded time-domain sequence), and then an L-point inverse DFT is performed to get the $L$-point interpolated sequence $x_i[n]$. This can be achieved in MATLAB with the following line :
xif = M*real(ifft([X(1:N/2+1), zeros(1,L-N),X(N/2+2:N)],L));

Now this is a quite good interpolator of $x[n]$ at a rate of $M$ times oversampling it...