Hector Research Institute of Education Sciences and Psychology

Dr. Xiaobin Chen

Xiaobin Chen is a Junior Research Group Leader at the Hector Research Institute of Education Sciences and Psychology. His research interests include Artificial Intelligence in Education, Intelligent Computer Assisted Language Learning (ICALL), and Second Language Acquisition.

His current research projects include "Aisla - an intelligent agent for second language English learning in real-life contexts" and "AI in education: Pedagogically oriented language knowledge extraction and readability-controllable natural language generation", both funded by the German Ministry of Education and Research (BMBF).



  • Bear, E. & Chen, X. (2023). Evaluating a conversational agent for second language learning aligned with the school curriculum. In Proceedings of the 24th International Conference of Artificial Intelligence in Education (AIED 2023, Doctoral Consortium).
  • Beukman, M. & Chen, X. (2023). Learner Perception of Pedagogical Agents. In Proceedings of 24th International Conference on Artificial Intelligence in Education (AIED 2023, Late-breaking Track).
  • Ludewig, U., Alscher, P., Chen, X.B., and McElvany, N. (in press). What Makes Domain Knowledge Difficult? Word Usage Frequency From SUBTLEX and dlexDB Explains Knowledge Item Difficulty. Behavior Research Methods. DOI: 10.3758/s13428-022-01918-0.
  • Chen, X.B., Meurers, D., and Rebuschat, P. (2022). Towards individually-adaptive input: Effects of complex input on the development of L2 writing complexity. Language Learning & Technology, 26(1): 1-21.
    DOI: 10125/73496
  • Chen, X.B., Bear, E., Hui, B., Santhi-Ponnusamy, H., Meurers, D. (2022). Education Theories and AI Affordances: Design and Implementation of an Intelligent Computer Assisted Language Learning System. In Proceedings of AIED2022.
  • Cui, Y, Zhu, J, Yang, L., Fang, X., Chen, X.B., Wang, Y., & Yang, E. (2022). CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform. In Proceedings of 13th Language Resources and Evaluation Conference (LREC).
  • Weiss, Z., Chen, X.B., and Meurers, D. (2021). Using Broad Linguistic Complexity Modeling for Cross-Lingual Readability Assessment. In proceedings of NLP4CALL Workshop.
  • Chen, X.B., Alexopoulou, T., & Tsimpli, I. (2020). Automatic extraction of subordinate clauses and its application in second language acquisition research. Behavior Research Methods. Advanced Online Access.  https://doi.org/10.3758/s13428-020-01456-7
  • Ruiz, S., Chen, X.B., Rebuschat P., & Meurers D. Meurers, D. (2019). Measuring individual differences in cognitive abilities in the lab and on the web. PLOS ONE14 (12)https://doi.org/10.1371/journal.pone.0226217
  • Chen, X.B., & Meurers, D. (2019). Linking text readability and learner proficiency using linguistic complexity feature vector distance. Computer Assisted Language Learning, 32 (4), 418-447. https://doi.org/10.1080/09588221.2018.1527358
  • Chen, X.B., & Meurers, D. (2018). Word frequency and readability: Predicting the text-level readability with a lexical-level attribute. Journal of Research in Reading, 41(3), 486-510.
  • Chen, X.B., & Meurers, D. (2017). Challenging learners in their individual zone of proximal development using pedagogic developmental benchmarks of syntactic complexity. In E. Volodina, I. Pilán, L, Borin, G, Grigonyte, & K, Björkenstam (Eds.), Proceedings of the Joint 6th Workshop on NLP for Computer Assisted Language Learning and 2nd Workshop on NLP for Research on Language Acquisition at NoDaLiDa 2017, Gothenburg, Sweden, 22 May (pp. 8-17). Linköpings: Linköping University Electronic Press.
  • Chen, X.B., & Meurers, D. (2016). CTAP: A Web-based tool supporting automatic complexity analysis. In D. Brunato, F. Dell'Orletta, G. Venturi, T. François, & P. Blache (Eds.), Proceedings of the Computational Linguistics for Linguistic Complexity Workshop at the 26th International Conference on Computational Linguistics (COLING 2016), Osaka, Japan, 11 December (pp. 113-119). The International Committee on Computational Linguisitcs.
  • Chen, X. B., & Meurers, D. (2016). Characterizing text difficulty with word frequencies. In J. Tetreault, J. Burstein, C. Leacock, & H. Yannakoudakis (Eds.), Proceedings of The 11th Workshop on Innovative Use of NLP for Building Educational Applications (BEA) , San Diego, USA, 16 June (pp. 84-94). Association for Computational Linguistics.
  • Chen, X.B. (2013). Tablets for informal language learning: Student usage and attitudes. Language Learning & Technology, 17(1), 20-36.


German Ministry of Education and Research

Curriculum Vitae

Since 09/2019
Junior Research Group Leader

Hector Research Institute of Education Sciences and Psychology, University of Tübingen

Since 09/2019
Associate Member of the LEAD Graduate School & Research Network

University of Tübingen

10/2018 - 09/2019
Research Associate at the Faculty of Modern and Medieval Languages

University of Cambridge, UK

10/2014 – 09/2018
Researcher and PhD candidate, Computational Linguistics

LEAD Graduate School & Research Network, University of Tübingen

07/2008 – 09/2014
English Lecturer at the School of Foreign Languages

South China University of Technology, China

09/2005 – 07/2008
Master of Arts in Applied Linguistics

South China University of Technology, China

09/2001 – 07/2005
Bachelor of Arts in English

South China University of Technology, China