Xiaobin Chen

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.