Over the past two decades, the quick growth of internet users has changed the way of access to information. However, the majority of this information is only available in English, which is very difficult for non-native English speakers to understand, particularly students. Since there are more than 6,500 languages spoken in the world, language barriers make it difficult for people to use web content efficiently. Although the quality assurance system for English is highly developed, it fails to cater to the needs of non-native English speakers. This paper introduces a novel approach for Cross-Lingual Question Answering which will be useful for all students whose mother tongue is not English. In our research, we have implemented different types of BERT models to analyze the CLQA dataset. We can examine and evaluate the performance of different BERT model implementations across a range of languages with the XQuAD dataset.