Team

Xiaolei Lu is an associate professor in the College of Foreign Languages and Cultures at Xiamen University, China. Her articles have appeared in peer-reviewed journals such as Computer Assisted Language Learning, Interpreting, Target, and Natural Language Engineering. She is the co-author of Applied Corpus Processing with Python, a volume dedicated to corpus data processing and analysis.

Corpus processing, translation technology, and automated translation/interpreting assessment.

Shirong Chen is an Assistant Professor at the School of International Studies, University of International Business and Economics, China. She received her Master’s degree from Renmin University of China and her PhD from Xiamen University. Her research focuses on translation and interpreting (T&I) assessment, with particular interests in rater cognition, automated T&I assessment, rating quality, and cognitive T&I studies. She seeks to understand how raters process, evaluate, and score T&I performances, and how these processes relate to assessment validity, by combining process-tracing techniques—including eye-tracking, pen-tracking, think-aloud protocols, and retrospective interviews—with psychometric and statistical modelling. Her work has been published in The Asia-Pacific Education Researcher, PLOS ONE, Target, Interpreting, and The Interpreter and Translator Trainer.

Quality assessment in translation and interpreting, cognitive translation and interpreting studies

Mengting Jiang is a PhD researcher in Interpreting Studies at the College of Foreign Languages and Cultures, Xiamen University, China. She is jointly supervised by Professor Chao Han at the National University of Singapore and Professor Jing Chen at Xiamen University. Her current work focuses on rater-mediated language assessment, particularly on how human raters comprehend, evaluate, and integrate information in cross-lingual and multimodal interpreting assessment. Methodologically, she employs a range of empirical approaches, including think-aloud protocols, psychometric scales, and mouse-tracking techniques.

Interpreting assessment, rater cognition, scale development, and process-tracing methods (e.g., mouse tracking).

Measuring rater cognition in interpreting assessment: Development and validation of the Scoring Process Scale

Yuhan Kang is currently a PhD researcher at the College of Foreign Languages and Cultures, Xiamen University. Her research interests include translation and interpreting education, test preparation, self-regulated learning, and AI-assisted feedback training. Her doctoral project investigates how a large-scale, high-stakes translation and interpreting test shapes candidates' self-regulated preparation strategies. She is also interested in the use of generative AI and multiagent tools to support interpreting learning, peer practice, and feedback literacy. Her current work mainly draws on qualitative inquiry, questionnaire-based research, and educational technology to understand how interpreter trainees prepare, collaborate, and evaluate performance in multilingual and digital learning contexts. More broadly, her work aims to connect assessment, learning strategy research, and responsible AI-supported pedagogy in translator and interpreter education.

Translation and interpreting education; test preparation; self-regulated learning; AI-assisted feedback training.

From washback to test preparation: Developing and validating a self-directed preparation strategy scale for the CATTI interpreting test

Qionglu Chen is a PhD researcher in the Department of English Language and Literature at Xiamen University, China. Her research interests are in GenAI-assisted interpreting assessment and feedback. She has published in SSCI-indexed journals including Language Testing and Innovations in Education and Teaching International.

GenAI-assisted interpreting assessment and feedback.

Exploring feedback seeking and uptake from GenAI in consecutive interpreting

Zhaokun Jiang is a PhD researcher in the Faculty of Arts and Social Sciences at the National University of Singapore. She currently serves as the student coordinator for the Translation and Interpreting Research Group. Her work has been published in established journals such as the International Journal of Applied Linguistics, Research Methods in Applied Linguistics, Acta Psychologica, Digital Scholarship in the Humanities, and Translation Studies. She has also co-authored a book chapter focusing on large language models and translation quality assessment.

Her research primarily spans three areas: (1) technology-mediated language teaching and learning; (2) process- and product-oriented language assessment; and (3) translation and interpreting studies. Methodologically, she explores these topics by integrating computational, experimental, and mixed-methods designs. She is currently investigating a project centered on generative AI-augmented peer assessment for interpreting students.

Xiaoxi Sun is a PhD researcher in the Department of Chinese Studies at the National University of Singapore and a member of the M³T&I + X Lab. Her research focuses on optimizing the human evaluation of machine translation.

Optimizing the human evaluation of machine translation.