One of the main problems facing U.S. education is the growing educational achievement gap (Kirsch, Braun, Yamamoto, & Sum, 2007). Snow and Biancarosa (2003) have argued that this gap is largely due to differential language proficiencies. Although there is a strong interest in helping English language learners achieve their language-learning goals, these students are under great pressure to improve not only their language skills, but also competency in content areas (e.g., math). While there are English language learning (ELL) computer-based learning tools that help students improve their English skills, the tools usually focus on informal vocabulary and day-to-day situations. We have developed a game-inspired assessment and learning environment aimed at supporting vocabulary learning in academic subject areas (BELLA/EM-ABLE). BELLA/EM-ABLE focuses on Tier II vocabulary, the language students need to enable them to learn more content-specific material from teachers and textbooks. Tier II words lie between everyday words and technical words (Calderón et al., 2005). The current version of BELLA/EM-ABLE consists of 57 Tier II conversations and 20 math activities. It also includes oral and written feedback in both English and Spanish. In addition, an internal psychometric model (or learner engine) is used to select conversations, math activities, and feedback levels; to estimate power levels (math and vocabulary knowledge levels); and to generate progress reports for students, teachers, or parents. This paper describes BELLA/EM-ABLE and also reports on a usability study carried out in a public middle school setting in New York City.