Yes, AI can be trained to detect open-ended questions. One way to do this is to use a machine learning model, such as a classifier, which is trained on a dataset of labeled questions (i.e., questions that have been labeled as open-ended or closed-ended). The classifier can then be used to predict whether a new question is open-ended or closed-ended based on the features of the question.
One example of a model that is suitable for this task is a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells. RNNs are well-suited for processing sequential data, such as text, and LSTM cells are a type of RNN that can handle long-term dependencies, making them useful for natural language processing tasks.
Another example of a model is the BERT (Bidirectional Encoder Representations from Transformers) which is pre-trained on large amounts of text data and fine-tuned for various NLP tasks including classification tasks.
It’s worth noting that the performance of these models will depend on the quality and quantity of the labeled data that is used to train them, and fine-tuning these models on a specific domain or task may improve their performance.