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-sectional survey data from 1,760 practitioners using LLMs for terminology tasks, structural equation modeling (SEM) and latent profile analysis (LPA) were employed. SEM evaluated the linear structural associations among prompt strategy preference, self-efficacy, and cognitive load, while LPA identified behavioral user profiles. Multi-group equivalence testing was further conducted across three career stages (novices, intermediates, and experts). Results Prompt strategy preference is significantly and negatively associated with subjective cognitive load, with AI interaction self-efficacy serving as a significant mediator in this structural relationship. LPA identified three distinct user profiles (low, medium, and high strategy use), with the high strategy group exhibiting the lowest cognitive load and highest self-efficacy. Multi-group equivalence tests indicated no statistically significant moderation across experience cohorts, demonstrating structural invariance of the core model across career stages. Discussion From the perspective of cognitive ergonomics, upfront prompt structuring is structurally linked to downstream cognitive offloading and psychological buffering. These findings provide empirical evidence for individual cognitive resilience during human-AI collaboration in the generative AI era.