Multimodal språktesting for bruk i tolkeutdanning: Utvikling og implementering av språkprøven for punjabitalende tolker i gresk offentlig sektor
DOI:
https://doi.org/10.5324/ntot.v2i1.6669Keywords:
artificial intelligence, interpreter education, language assessment, low-resource languages, multimodal language testing, public service interpretingAbstract
This article documents the design and implementation of Greece’s first multimodal, language proficiency test for Punjabi public service interpreters, developed in response to the March 2024 launch of the Public Service Interpreter Register. The test, which was partially developed with the help of AI, addresses the complex sociolinguistic and logistical realities faced in the Greek asylum and migration context. The paper reviews current frameworks for interpreter competence and language proficiency—drawing on the Common European Framework of Reference (CEFR) standards—and critiques the limitations of traditional assessment models. The Punjabi test integrates audio-based tasks and visual cues, leveraging AI-generated prompts to accommodate dialectal and literacy variation, prioritizing fairness and scalability, thus offering a replicable model for rare and minority languages where written resources, local expertise, and institutional infrastructure are lacking. The paper concludes by evaluating initial implementation outcomes, highlights both the potential and limitations of multimodal and AI-assisted testing approaches in assessing language proficiency among public service interpreters.
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