BRIDGING THE GAP: PREPARING VOCATIONAL LANGUAGE STUDENTS FOR AI-INTEGRATED TRANSLATION WORKFLOWS

Abstract

The rapid adoption of AI in localization workflows has created a gap between industry demands and the skills taught in vocational English programs in Indonesia. This study investigates how well Indonesian vocational English programs prepare graduates for AI-integrated translation work. This study conducts a curriculum gap analysis of seven Applied Bachelor in “English for Business and Professional Communication” programs by triangulating publicly available syllabi with industry signals synthesized from AI-and-translation webinars and seminars observed between January and August 2023. Industry signals point to accelerating adoption of AI and machine translation across localization workflows, expanding expected competencies to include MT post-editing, MT quality evaluation, AI-enabled CAT tools, and localization operations literacy.

Across accessible curricula, we observe strong foundations in general/professional communication and legacy translation/interpreting offerings—for example, “Introduction to Translating and Interpreting,” “Business Document Translation,” “Translation for Media Communication,” and interpreting courses—yet little explicit coverage of AI-mediated practices. In several cases, limited publication of detailed curricula itself emerges as a barrier to alignment and transparency.

We propose a minimal, skills-first enhancement that programs can embed without overhauling degree structures: (1) MT post-editing labs tied to domain texts; (2) measurable MT evaluation using industry standard error taxonomies; (3) CAT tool practicums leveraging AI features; (4) prompt-driven human-in-the-loop workflows for terminology, QA, and style; (5) localization project simulations covering data ethics and client constraints; and (6) industry-linked practicum or internship modules explicitly aligned to AI workflows. Collectively, these steps help translate classroom learning into demonstrable workplace competencies for AI-shaped translation markets.