Designing Job Lingua AI: A Conceptual Framework for AI-Enhanced Interview English in Higher Education

Abstract

The transition from university to workplace demands not only technical knowledge but also advanced communication skills, particularly in job-seeking contexts. In Malaysia’s public and private sectors, proficiency in English, especially in resume writing, cover letter development, and job interviews, is an important determinant of graduate employability. However, existing English for Specific Purposes (ESP) courses such as LCC502 often lack the personalized and formative feedback necessary to prepare learners for these high-stakes real-world tasks. This paper presents JobLinguaAI, an AI-supported educational platform developed to address this gap by integrating AI-powered writing assistance, speech coaching, and interview simulations within a CEFR B2 High aligned framework. Grounded in a design-based research approach and informed by the course outcomes of LCC502, JobLinguaAI supports students in producing professional documents, improving spoken fluency, and acquiring domain specific vocabulary through iterative feedback loops. Preliminary feedback from educators affirms its relevance, clarity, and alignment with course learning outcomes while also suggesting enhancements such as localized vocabulary and lecturer dashboards. The platform’s modular design allows for scalability across ESP domains and integration with institutional LMS platforms. With its potential to transform job readiness pedagogy in Malaysian higher education, JobLinguaAI offers a novel model for AI enhanced, task-based language learning that promotes learner autonomy, performance accuracy, and employability.

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