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A technical curriculum on language-oriented artificial intelligence in translation and specialised communication

arXiv 2602.12251 ↗First posted 12 Feb 2026cs.CL cs.AI cs.HCcited by 0

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Abstract (arXiv)

This paper presents a technical curriculum on language-oriented artificial intelligence (AI) in the language and translation (L&T) industry. The curriculum aims to foster domain-specific technical AI literacy among stakeholders in the fields of translation and specialised communication by exposing them to the conceptual and technical/algorithmic foundations of modern language-oriented AI in an accessible way. The core curriculum focuses on 1) vector embeddings, 2) the technical foundations of neural networks, 3) tokenization and 4) transformer neural networks. It is intended to help users develop computational thinking as well as algorithmic awareness and algorithmic agency, ultimately contributing to their digital resilience in AI-driven work environments. The didactic suitability of the curriculum was tested in an AI-focused MA course at the Institute of Translation and Multilingual Communication at TH Koeln. Results suggest the didactic effectiveness of the curriculum, but participant feedback indicates that it should be embedded into higher-level didactic scaffolding - e.g., in the form of lecturer support - in order to enable optimal learning conditions.

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References · 6

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
  4. 2019A Multiscale Visualization of Attention in the Transformer Modelcited by 292
  5. 2024adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgroundscited by 14
  6. 2024AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculumcited by 3

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