A technical curriculum on language-oriented artificial intelligence in translation and specialised communication
Ralph Krüger
TL;DR
Problem: AI-first automation in language and translation increases dependence on AI systems, risking opaque processes for stakeholders. Approach: A four-notebook curriculum on vector embeddings, neural networks, tokenization, and transformer architectures delivered via Colab/GitHub CC BY-SA 4.0, with didactic scaffolding and multimodal content. Contributions: Empirical evidence from MA courses showing meaningful self-reported knowledge gains and positive judgments on notebook-based learning, plus insights on needed lecturer support and potential LLM-assisted enhancements. Significance: Provides a modular, open educational resource to build technical AI literacy among L&T stakeholders, with implications for professional agency, digital resilience, and HCXAI-oriented pedagogy.
Abstract
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.