Active learning methods are used to improve the classification accuracy when little labeled data is available. Most traditional active learning methods pose a very specific query to the oracle, i.e. they ask for the label of an unlabeled example. This paper proposes a novel active learning method called RIQY (Rule Induced active learning QuerY). It can construct generic active learning queries based on rule induction from multiple unlabeled instances. These queries are shorter and more readable for the oracle and encompass many similar cases. Also the learning algorithm can achieve higher accuracy rates by asking fewer queries. We evaluate our algorithm on 12 different real datasets. Our results show that we can achieve higher accuracy rates using fewer queries compared to the traditional active learning methods.