Research on the Paradigm Reconstruction of Interpreting Pedagogy Driven by Generative AI

This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology. With the breakthroughs of AI technologies such as ChatGPT in natural language processing, traditional interpreting education faces dual challenges of technological substitution and pedagogical transformation. Based on Kuhn’s paradigm theory, the study analyzes the limitations of three traditional interpreting teaching paradigms, language-centric, knowledge-based, and skill-acquisition-oriented, and proposes a novel “teacher-AI-learner” triadic collaborative paradigm. Through reconstructing teaching subjects, environments, and curriculum systems, the integration of real-time translation tools and intelligent terminology databases facilitates the transition from static skill training to dynamic human-machine collaboration. The research simultaneously highlights challenges in technological ethics and curriculum design transformation pressures, emphasizing the necessity to balance technological empowerment with humanistic education.