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• Computational Linguistics
• Empirical Educational Science
• Intelligent Computer-Assisted Language Learning (ICALL)
• +10 more

Prof. Dr. Walt Detmar Meurers is a Research Professor at the Leibniz Institute for Knowledge Media (IWM) since April 2024, leading the Language and AI in Education research group. Previously, he served as a Full Professor of Computational Linguistics and head of the Theoretical Computational Linguistics group at the University of Tübingen (2008–2024). His career spans roles at The Ohio State University (Assistant to Associate Professor, 2001–2008), Universitet i Tromsø (Professor II, 2013–2016), and University of Tübingen (Lecturer, 1997–2000). His research bridges Computational Linguistics and Empirical Educational Science, focusing on Second Language Acquisition, Intelligent Computer-Assisted Language Learning (ICALL), learner corpora analysis, and linguistic complexity modeling for adaptive educational systems. Key projects include FeedBook (adaptive English learning), MERLIN (learner corpus analysis), and CoMiC (semantic comparison in education). He has contributed to formal syntax-information structure interfaces within the HPSG framework and advocated for integrating linguistic theory with educational technology. Scientific Leadership: Steering Board, LEAD Graduate School and Research Network Steering Committee, Heritage Language Consortium Scientific Advisory Board, CATALPA and SZHB Consultant, EFCAMDAT project Education: PhD in Computational Linguistics, University of Tübingen (2000) MA in Linguistics, Computer Science, Psychology, University of Tübingen (1994) ERASMUS exchange at IRIT, Université Paul Sabatier Impact: Co-authored Language and Computers (freely available as an open-access textbook) Organized workshops: AALL'09, IICALL Advisory roles for start-ups: cabuu, Prosodiya

Xiaobin ChenView profile

Assistant Professor

• Artificial Intelligence in Education
• Intelligent Computer Assisted Language Learning (ICALL)
• Second Language Acquisition
• +7 more

Xiaobin Chen is a Junior Professor at the University of Tübingen, affiliated with the Hector Institute for Empirical Educational Research within the Faculty of Humanities and the Department of Social Sciences. He leads research at the intersection of artificial intelligence and language education, with strong institutional support from the German Federal Ministry of Education and Research (BMBF). His work is deeply collaborative, involving partnerships with researchers across institutions and active participation in the LEAD Graduate School & Research Network. His research focuses on leveraging artificial intelligence and natural language processing to enhance second language learning. Key areas include Intelligent Computer-Assisted Language Learning (ICALL), automated language assessment, readability modeling, and the design of pedagogically sound AI tools such as conversational agents and interactive reading platforms. His work emphasizes aligning technological innovation with educational theory and classroom needs. The recent publications reflect a strong trajectory in AI-driven educational research, particularly in applying NLP to analyze learner language, develop intelligent tools, and assess language proficiency. Themes across the articles include individual differences in learning, human-AI collaboration, automated scoring, and the integration of AI into real-world educational settings, especially in EFL contexts. Xiaobin Chen leads two major BMBF-funded research projects: Aisla - Intelligent Language Assistant for Learning English in Everyday Situations (2021–2024) AI in Education: Pedagogically Oriented Language Knowledge Extraction and Readability-Controllable Natural Language Generation (2022–2025) These projects support his team’s development of innovative, theory-informed AI systems for language learning and assessment. He is associated with the KIBi Research Group , which focuses on integrating knowledge, interaction, and behavior in intelligent educational systems. The group develops and evaluates digital tools that adapt to learners’ needs, using data from interaction logs and linguistic analysis to drive personalization.