Generative artificial intelligence and terminology work: structural associations of prompt strategy preference, cognitive load, and self-efficacy

Introduction The widespread application of generative artificial intelligence (AI) in knowledge-intensive domains requires continuous adjustment of human cognitive patterns. In terminology work, aligning strict semantic standards against the non-deterministic output of large language models (LLMs) is closely intertwined with workers' subjective cognitive load. This study examines the structural associations among prompt strategy preference, AI interaction self-efficacy, and subjective cognitive load, while testing the potential moderating role of professional experience. Methods Based on cross