Academic writing remains a significant challenge for many university students, especially in engineering programs, where technical mastery often takes priority over communicative skills. Consequently, students frequently struggle to organize ideas, construct coherent arguments, and employ formal language appropriately. This study evaluates engineering students’ writing progress across two key dimensions: lexical richness and argumentative structure. Analyzing 69 documents from one semester, it applies Natural Language Processing (NLP) techniques to measure lexical variety, density, and sophistication, and uses a Conditional Random Fields (CRF)-based sequence-labeling model to identify premises and conclusions. Plagiarism checks and AI-generated text detection ensured authenticity. Results show notable improvements in vocabulary and argumentative clarity in some groups, with variations linked to pedagogical methods and the growing influence of generative AI. Overall, the study highlights the potential of NLP tools to provide a comprehensive assessment of academic writing in engineering education.