Jiang Zhaokun 姜兆坤

Education:
B.A., Xiamen University
MTI, Shanghai Jiao Tong University

Research Interests:
Interpreting and translation quality assessment; cognitive modeling; human-computer interaction; computational linguistics

Self-Introduction:
My research interests lie at the intersection of language and technology. Specifically, I leverage data and computational modeling to evaluate the quality of translation and interpreting, map the cognitive trajectories of translators and interpreters in technology-assisted environments, and uncover the interaction mechanisms behind human-computer collaboration.

Thesis Advisor: A/P Han Chao

Thesis Title: Leveraging GenAI in Chinese-English Interpreting Performance Assessment: A Human-AI-teaming Approach (tentative)

Thesis Research Area:
Interpreting assessment is essential for teaching practice and talent selection, yet traditional manual scoring is time-consuming, labor-intensive, and prone to subjectivity - particularly in Chinese-English interpreting, where significant linguistic differences increase the complexity of evaluation. Most recently, generative artificial intelligence (GenAI) has offered new opportunities for the automated evaluation of human interpreting, yet it is also associated with potential challenges such as accuracy, consistency, and interpretability. To address these issues, this study proposes a two-stage human-AI collaboration framework: first enhancing GenAI’s scoring capability, then integrating it into a collaborative assessment workflow. This approach aims to improve both the quality and efficiency of scoring, advancing interpreting assessment toward a model that balances technological intelligence with human-centered values.