Evaluating Human and AI Translation Strategies: A Process-Oriented Comparative Case Study Across Genres

Abstract

As AI has been increasingly applied in translation, Large Language Models (LLMs) are becoming important tools in translation practice. Both human translators and AI models employ strategies in the translation process, yet comparative studies remain limited. Using a self-developed Translation Strategy Evaluation Scale (TSES), this study drew on scientific, political-economic, and literary texts to compare human and AI translation strategies. Think-Aloud Protocols (TAPs) and AI reasoning outputs were used to examine differences in strategy types and decision-making between two human translators and two LLMs (GPT-5.3 and DeepSeek-V3.2); the Many-Facet Rasch Model (MFRM) was then applied to evaluate strategy use across four dimensions: necessity, compatibility, effectiveness, and consistency. Results show that while both shared 7 basic categories, they diverged in 4 respects. AI used abstraction and nominalization for stylistic formality; human translators performed grammatical monitoring, redundancy reduction, and discourse reorganization. In decision-making, AI followed systematic planning and rule-driven execution, while human translators relied on experiential judgment and reader awareness with ongoing self-monitoring. ChatGPT demonstrated the highest quality of strategy use across all text types. The study sheds light on the process-oriented nature of translation strategies and offers empirical evidence for integrating technological tools into translation pedagogy.