Following Ahmads answer, we need to show that $x_i = -\frac{1}{\sqrt{n-1}}$ for all $1 \leq i \leq n-1$ and $x_n = \sqrt{n-1}$ indeed produces the maximum value of $\sum_{i=1}^n x_i^m$, which is $\alpha_m^* = \dfrac{(n-1)^{m-1}+(-1)^m}{(n-1)^{(m/2)-1}}$. Actually, this paper (Some constrained optimization problems in elementary statistics, by Wolfgang Stadje (2002)) cites that M. LAKSHMANAMURTI in the mentioned 1950 article is using exactly these values to prove the claim.
Let us change the variables to $x_i = -\frac{1}{\sqrt{n-1}} + y_i$ for all $1 \leq i \leq n-1$ and $x_n = \sqrt{n-1} + y_n$. Then we have to observe $\sum_{i=1}^n y_i = 0$. Further, demanding $n = \sum_{i=1}^n x_i^2$ gives, when using $\sum_{i=1}^n y_i = 0$, the condition $y_n = - \frac{\sqrt{n-1}}{2 n } \sum_{i=1}^n y_i^2$. So it is only possible to use negative $y_n$.
Expanding $\sum_{i=1}^n x_i^m$ to first order in $y_i$, we obtain (again using $\sum_{i=1}^n y_i = 0$)
$$
\sum_{i=1}^n x_i^m = \alpha_m^* + m (n-1)^{-\frac{m-1}{2}} [(n-1)^{m-1} - (-1)^{m-1}] y_n
$$
Since $(n-1)^{m-1} - (-1)^{m-1} \ge 0$, and since $y_n <0$, we have that the value of $\sum_{i=1}^n x_i^m$ will actually not increase in the vicinity of the original values for $x_i$, no matter what the changes $y_i$ are. This shows that the original values for $x_i$ indeed locally produce the highest value of $\alpha_m$.