Science university students show moderate willingness to improve English proficiency yet hesitate to speak due to performance anxiety. However, instructors often rely on subjective evaluations, limiting data-driven instruction amid tight budgets and time shortages. Surveys reveal demand for field-aligned texts matching proficiency levels, while instructors prioritize preferences or school requirements.Beginning in April, this study applies objective AI evaluations so students know where to improve. English Language Speech Assistant (ELSA) for Schools measures pronunciation, writing, and comprehension gains using science materials.Two second-year science classes complete progress monitoring from Week 1 to Week 16 across English skills.Weekly ELSA feedback supports ongoing language practice through custom tasks. These tasks, tailored to science, technology, engineering, and mathematics (STEM) content through content-based instruction (CBI), engage discipline-specific interest with speaking practice for presentations and optional comprehension activities. Week 1 includes a proficiency test and pre-survey on confidence, followed by weekly ELSA practice. Week 16 features a post-survey and ELSA assessment comparing baseline gains. Data include Common European Framework of Reference (CEFR) baselines, pre- and post-surveys, and scores in pronunciation, fluency, and grammar. This study documents CBI–ELSA integration for STEM majors, quantifying improvements through ELSA’s visualization, targeted feedback, and dashboards showing results with greater accuracy.