Persistent dissatisfaction with the outcomes of conventional English instruction in bilingual classroom settings has highlighted the need for more effective pedagogical models for non-linguistic majors in regional universities. These challenges are particularly pronounced in small, instructor-led groups taught primarily by non-native English-speaking lecturers under constrained institutional conditions. The study aimed to design, implement, and evaluate a bilingual teaching model enhanced by emerging natural language processing (NLP) tools in order to improve learner engagement, motivation, and language proficiency among non-linguistic majors. It also sought to examine how technological support could mitigate external and institutional factors that limit the effectiveness of traditional instruction. A longitudinal research design was employed, covering the period from 2008 to 2017. The study combined qualitative analysis of institutional and instructional conditions with the implementation of an integrated foreign-language environment that embedded NLP tools into classroom activities, feedback mechanisms, and assessment practices. Student progress was monitored through a hybrid system integrating conventional assessment instruments with NLP-supported data tracking. The findings demonstrate that the NLP-enhanced bilingual model significantly increased student motivation and produced measurable gains in English proficiency over time. The novelty of the study lies in its early and systematic integration of NLP applications into bilingual pedagogy within non-linguistic programs, offering empirically grounded insights into technology-supported language instruction and providing a transferable framework for institutions operating in comparable educational contexts.