<![CDATA[This study explores the integration of the Gemini AI model as a "morphological engine" to enhance academic vocabulary acquisition among university students learning English as a foreign language. By employing a qualitative case study of 25 students, the research investigates the efficacy of "Lexical Deconstruction"—systematically breaking down complex Greco-Latinate terms into their constituent roots to bridge the gap between basic communication and scholarly proficiency. Results demonstrate that this AI-mediated workflow increases root retention by 65% and significantly reduces cognitive overload, particularly in STEM fields where jargon functions as a compound language. Crucially, the process of deconstructing and reintegrating vocabulary fostered a sense of "lexical ownership," leading to a 40% reduction in plagiarism compared to traditional translation methods. For teachers, this study offers a scalable, student-led framework to transform AI from a text generator into a cognitive scaffold that promotes linguistic autonomy. For researchers, it provides a methodological template for analyzing human-AI interaction through the lens of etymological genealogy. Ultimately, the findings suggest that mastering the recurring building blocks of English enables students to move from passive decoding to active participation in global academic discourse, ensuring intentionality and integrity in their scholarly voice.]]>
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