How do I go from number data to string data (NN)?
There are various ways to handle string inputs to neural networks, but since you are trying to predict spelling difficulty, I suggests representing your words as a sequence of characters. This will preserve information about the particular spelling of the words including the order of the letters.

To represent a sequence of characters you can one-hot encode each character, so each word will be represented as a sequence of one-hot length 26 vectors.

To handle this kind of input I suggest either a 1D Convolutional Neural Network or some flavor of a Recurrent Neural Network.

If you choose a 1D CNN you will have to feed it fixed sized inputs. To do this choose a max word length, k, and either cut off or pad with zeros each input word to fit into a k*26 input matrix.

Here is an example of a CNN architecture you might use in Keras:

model = Sequential()
model.add(Convolution1D(nb_filter=32, filter_length=5, activation='relu', input_shape=(k, 26))
model.add(MaxPooling1D())
model.add(Convolution1D(nb_filter=32, filter_length=5, activation='relu')
model.add(MaxPooling1D())
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])