Michał Kornacki, PhD

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Overview

An MA seminar examining the theory and practice of artificial intelligence as applied to translation. The course surveys the development of machine translation from rule-based systems through neural MT to large language models and generative AI, situating each paradigm in its technological and professional context.

Practical sessions address the integration of GenAI tools into translator workflows: prompt engineering, hybrid human–AI pipelines, post-editing of LLM output, and the ethical dimensions of AI-assisted translation. The course draws on current research, including the instructor's own published work on hybrid workflows.

Topics Covered

• History of machine translation: RBMT, SMT, NMT
• Large language models and generative AI in translation
• Prompt engineering for translation tasks
• Hybrid workflows: human–AI collaboration models
• Post-editing LLM output: strategies and evaluation
• Quality evaluation of MT and AI-generated translations
• Ethics, transparency, and disclosure in AI-assisted translation
• Implications for translator training and the profession

Assessment

Critical analysis of an AI translation tool or workflow (40%), a practical post-editing assignment with commentary (40%), and seminar participation (20%).