<p>Expressing information in different forms is an important skill that students should develop in school. This skill positively impacts academic reading and writing. However, it can also lead to negative consequences, such as plagiarism. Students may paraphrase original texts and present them as their own work. Therefore, the need to develop effective approaches to <i>detect plagiarism</i> and <i>identify paraphrase</i> has become increasingly important in academia, journalism, publishing, and other fields where innovation, novelty, and originality are highly valued, especially with the rising incidence of plagiarism in these areas because of the easy access to information on the internet and the capabilities of large language models. Most published detection methods analyse plagiarism at the sentence-level. We have developed approaches for generating and detecting paraphrased paragraphs by considering <i>inter-sentence</i> and <i>intra-sentence relations</i>, which enables the identification of paraphrased text at the <i>paragraph-level</i>. This includes joining, splitting, and/or shifting sentences within a paragraph, as students often plagiarise paragraphs. In the generating stage, we create the ALECS dataset, by developing three algorithms and applying a masking approach to tackle the paragraph’s syntactic and lexical layers while maintaining the paragraph’s semantics. ALECS can contribute to developing students’ abilities in paraphrasing, as there are more than 6 different forms for each source paragraph. In addition, as in this study, ALECS can be employed to train deep learning models for the purpose of generating or detecting plagiarised paragraphs. For the detection phase, our method shows robust results and outperforms existing work in detecting paragraph-level paraphrases, achieving a 90.1 F1 score with Longformer and reaching 96 when using a fine-tuned GPT-3.5.</p> Graphical Abstract <p></p>