Coverage of Machine Translation Post-Editing Competences in Iran's MA English Translation Curriculum:
An EMT Competence Framework (2022) Perspective
Abstract
Machine translation post-editing (MTPE) has become a core competence in translation workflows, yet its integration into translator education remains understudied. This case study examines Iran's national MA curriculum in English Translation against the European Master's in Translation (EMT) Competence Framework (2022) and current MTPE competence models. Credit-bearing units were mapped to the five EMT competence areas, and learning outcomes were analyzed via directed qualitative content analysis; MTPE-specific coverage was assessed using a matrix drawn from the EMT Framework, ISO 18587:2017, and established MTPE models. Two independent coders achieved substantial interrater reliability (κ = 0.81, 95% CI [0.74, 0.88]), confirming coding consistency rather than generalizability. Translation and Language and Culture accounted for 75.0% of credits, while Technological competence accounted for only 6.25%; Personal/Interpersonal and Service Provision areas received no dedicated coverage. At the outcome level, 24 of 38 EMT descriptors (63.2%) were unaddressed, with gaps in MT literacy, error analysis, post-editing strategies, workflow integration, quality evaluation, and ethical use. Findings suggest the curriculum remains largely traditional and text-oriented, underrepresenting technological, interpersonal, and service-oriented competences. Three reform priorities emerge: expanded technology training, longitudinal MTPE integration, and alignment with international frameworks.
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Copyright (c) 2026 Saeed Ketabi, Rouhullah Nemati Parsa, Mohammadtaghi Shahnazari
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