The generation and identification of paraphrases are essential challenges in natural language processing (NLP), involving considerable consequences for education, plagiarism detection, and content analysis. Despite significant advancements in sentence-level paraphrasing, paragraph-level paraphrasing is still inadequately investigated, especially across various fields. This research is the first to examine the effectiveness of SALAC algorithms and Transformer-based models in generating coherent and semantically accurate paraphrases across various domains, utilising the ALECS dataset, which includes text samples from five different educational fields, such as Economics and Anthropology. The methodology uses SALAC algorithms for sentence reordering, according to coherence scores obtained from the ALBERT model’s sentence Order Prediction (SOP). Real-world human evaluation is performed on the paraphrased paragraphs, measuring semantic similarity and coherence via a Likert scale of five pre-defined points. Evaluators involve academic students and researchers, ensuring the reliability of outcomes. Research findings show that SALAC algorithms successfully maintain semantic integrity and coherence across several domains, highlighting their generalisability. Additionally, the study examines how domain-specific readability impacts annotation reliability and paraphrasing performance. This research contributes to the development of robust, domain-independent paraphrasing techniques at the paragraph-level for educational applications and plagiarism detection, thereby advancing NLP solutions across multiple domains.