Technology-Empowered Curriculum Reform and Practice in English Translation Courses

The deep application of artificial intelligence technologies, particularly neural machine translation and generative AI, is fundamentally reshaping the global translation industry and compelling a paradigm shift in translator education. Applied higher education institutions in Guangdong Province, despite achieving preliminary outcomes in translation program reform, continue to face structural contradictions including curricular misalignment with industry standards, insufficient practical training components, and limited technological infrastructure. This study, grounded in Outcome-Based Education philosophy, systematically investigates the technology-empowered reform of English translation courses through literature analysis and case comparison. It proposes a comprehensive reform framework encompassing four interconnected dimensions: curriculum objectives, instructional content, practical teaching, and evaluation systems. The framework operationalizes graduate competencies into three observable and measurable indicators—technological application competence, project execution competence, and ethical judgment competence—and reverse-designs a “three-tiered progressive, modular curriculum system” that integrates AI-related courses, project-based and case-based pedagogies, deepened university–industry collaboration, and diversified outcome-oriented assessment mechanisms. A “ladder-type, three-stage” progressive practice system supported by functionally integrated intelligent translation laboratories is also formulated to enable a closed loop from skill training to competency validation. The study contributes theoretically by incorporating philosophy of technology perspectives into translation education discourse and practically by offering a replicable reform paradigm for peer institutions with comparable resource conditions. Limitations include constrained generalizability due to the regional scope of data and the need for longitudinal validation of reform effectiveness. Future reforms should strengthen technological ethics and critical thinking education, advance interdisciplinary knowledge integration, and establish regional teaching collaboration mechanisms to cultivate high-quality applied translation talents capable of effective human–machine synergy in the AI era.