Science education, by its very nature, requires students to mentally construct complex scientific phenomena, internalize these structures, and ultimately express them within the framework of the unique rules of scientific language. This process encompasses not only the transfer of knowledge but also the development of higher-order cognitive skills. However, traditional assessment methods widely used in current education systems (e.g., multiple-choice tests) are insufficient for analyzing, on a large scale, the cognitive processes, reasoning chains, and persistent misconceptions hidden in students' written output (open-ended answers, detailed experiment reports, argumentation texts). This chapter examines the integration of Natural Language Processing (NLP) technologies into science education in depth, within the framework of constructivism, cognitive load theory, and scientific argumentation. The study argues that NLP is more than just a technical tool for processing data; it offers an analytical framework that makes students' "mental models" and complex "conceptual networks" visible through a pedagogical interface. Furthermore, through a conceptual "Misconception Detection" scenario based on the principle of semantic similarity, which does not require any complex model training, the integration of this technology into classroom teaching and formative assessment processes is demonstrated concretely.