<![CDATA[This mixed-methods sequential explanatory study evaluates the effectiveness of an AI-powered language learning platform in enhancing English for Specific Purposes (ESP) outcomes among pharmacy undergraduates. Sixty students at a private university in Jambi were assigned to either experimental or control groups; the experimental group utilized an AI system with adaptive algorithms, personalized feedback, and scenario-based interactive tasks for six weeks. Data were collected through vocabulary retention tests, context-based task evaluations, structured surveys, and focus-group interviews. Quantitative analyses indicated significant gains in vocabulary retention (mean increase from 56.3 to 78.5, p < .001) and contextual task performance (mean increase from 60.4 to 83.6, p < .001) relative to controls. Qualitative data derived from surveys and focus group interviews indicated heightened engagement, intrinsic motivation, and learner autonomy. Thematic analysis identified three key mechanisms underpinning these outcomes: adaptive feedback promoting persistence and metacognitive reflection, personalized learning enabling self-regulation, and authentic simulation fostering contextual awareness and professional readiness. The study demonstrates pedagogical advantages of integrating adaptive AI tools within ESP curricula for pharmacy education. It extends existing literature by providing empirical validation of AI-enhanced language learning and offers actionable guidance for instructional design and technology integration practices. Recommendations include aligning pedagogy with adaptive feedback cycles, securing robust institutional support, investing in faculty professional development, and fostering sustained interdisciplinary collaboration to maximize educational benefits. Future research should expand institutional representation, employ longitudinal designs, and implement comprehensive assessments encompassing both receptive and productive skills. Incorporating instructor perspectives and classroom observations would further elucidate implementation dynamics and sustainability, thereby strengthening the evidence base for AI-supported ESP frameworks in health professions education.]]>
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