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This paper introduces a technical curriculum designed to enhance AI literacy within the language and translation (L&T) industry, covering vector embeddings, neural networks, tokenization, and transformer networks. The curriculum aims to cultivate computational thinking, algorithmic awareness, and agency among L&T professionals to improve their digital resilience. Evaluation in an MA course at TH Koeln suggests the curriculum's effectiveness, while also highlighting the need for additional lecturer support to maximize learning outcomes.
Equipping language and translation professionals with AI fundamentals can boost their digital resilience in increasingly AI-driven work environments.
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.