Profiling Chinese Student Interpreters’ Usage Intention of ChatGPT-assisted Translation by Q Methodology Based on Technology Acceptance Model

Abstract

This study investigates the Chinese student interpreters’ usage intention of ChatGPT-assisted translation with Q methodology. Guided by technology acceptance theory (TAM), the study aims to explore the usage intention profiles and consensus and differences between them. Q sort tasks were undertaken by 30 participants and their commentary data was elicited to complement Q sort analysis. From the factor analysis, four factors were obtained, namely deeper-purpose seekers, big picture reflectors, deep understanders and coordinators. The distinguishing statements include the subject, technology, information and society elements. The article concludes with a discussion of the pedagogical implications of learning interpreting of university students.

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