Research in Intelligent Computer-Assisted Language Learning (ICALL) integrates Natural Language Processing into Computer-Assisted Language Learning. For such research to be innovative and sustainable, we believe that it needs to combine Second Language Acquisition (SLA) research, precise linguistic modeling, and sound computational linguistic methods (Meurers 2012; Meurers & Dickinson 2017). On this foundational research, we ground the development of digital tools addressing real-life education needs, in school and lifelong learning.

  • Evidence for Learning

    We develop methods analyzing learner language to broaden the empirical evidence for development, both in terms of linguistic constructions and general linguistic complexity, including task effects and L1 transfer.

    Analyzing Learner Language
  • Intelligent Language Tutoring

    We create interactive systems that support foreign language learners in practicing language skills with incremental, scaffolding feedback — like a human tutor would, who unfortunately we can't always have around.

  • Enriching Input

    We design search engines and linguistic complexity measures needed to identify the input that best fosters learners in their language development. We also check whether educational materials are adapted to their audience.


About us

Detmar Meurers started the ICALL research group at the Ohio State University, where he was a faculty member at the Department of Linguistics from 2000 to 2008, before moving to the University of Tübingen.
Complementing the current members of our group, some former members of the group include Xiaofei Lu, Markus Dickinson, Luiz Amaral, Martí Quixal, Robert Reynolds and Sowmya Vajjala.
We pursue our interdisciplinary agenda as part of the LEAD Graduate School and Research Network in Empirical Educational Science, in close collaboration with the LEAD Distinguished International Professor Patrick Rebuschat. We are founding members of the Heritage Language Network, the INDUS DFG Network, and collaborate with our linguistics colleagues in the SFB 833.

Analyzing Learner Language

In the SFB 833-A4 project, we are developing automatic meaning assessment methods for short-answer reading comprehension. To collect a rich task-based corpus in a real-life teaching context, we created the WELCOME app (Ott et al., 2012) and obtained the CREG corpus (36k answers to 1.5k questions). Our research showcases the importance of interpreting data in context (Ziai & Meurers, 2014; De Kuthy et al., 2015, 2016a, b; Ziai et al., 2016). The CoaLLA project explores the integration of top-down and bottom-up information. With Katrin Wisniewski we explored linguistic correlates of the CEFR as part of the MERLIN project.
As EFCamDat consultants, we collaborate with Dora Alexopoulou (Cambridge) and Marije Michel (Utrecht) to jointly analyze this very large English learner corpus (1.18 million writing tasks by 175k learners, CEFR A1–C2). We characterize language development both for specific constructions, e.g., relative clauses (Alexopoulou, Geertzen, Korhonen & Meurers, 2015) and in terms of linguistic complexity, emphasizing the need to account for task effects (Alexopoulou, Michel, Murakami & Meurers, 2017).
We also analyze L1 transfer effects using machine learning for Native Language Identification as an experimental testbed integrating shallow and deeper linguistic characteristics of learner data (Bykh & Meurers 2012, 2014, 2016; Meurers, Krivanek & Bykh 2014; Bykh, Vajjala, Krivanek & Meurers 2013).

Interactivity: Intelligent Tutoring Systems

Feedback is known to be very effective in fostering learning — yet human tutors are not always around, and the different amount of support students get at home is a major cause for inequality in education. While tutoring systems are increasingly taken hold in formal domains such as mathematics and the natural sciences, foreign language learning poses additional modeling challenges. We are combining NLP methods with SLA insights in designing foreign language tutoring systems that provide individual, scaffolding feedback to students while they work on homework. Students are stepwise led to successfully complete an exercise so that teachers in class can work with a more homogeneous student group in class. Following the Portuguese Intelligent Tutoring System (ITS) TAGARELA (Amaral & Meurers 2011) designed to complement university instruction, in collaboration with a German school book publisher we created the FeedBook, an interactive workbook for English 7th grade in a DFG-funded transfer project. In the first randomized controlled field study with an ITS fully embedded in a regular German school context, we established the effectiveness of the specific scaffolding feedback (Meurers et al. 2019). The approach is extended to adaptively sequence activities in the DigBinDiff project. In Interact4School, we extend the ITS with motivational feedback and explore the interface between individual learning using an ITS and the teacher orchestrated learning in the classroom. In IL2 we work on improving that interface, both on the technical side (teacher dashboards) and in terms of developing teacher training components linking SLA concepts and research results with the use of digital tools based on this foundation.
In the new BMBF project AISLA, we develop an intelligent dialog system supporting the acquisition of English in authentic, spoken language contexts.
We also collaborated with the Tübinger Institut für Lerntherapie in developing Prosodiya, a mobile serious game for German dyslexic primary-school children currently being evaluated in a large field study with a waiting control group design.


We are developing linguistic complexity analyzers integrating a wide range of linguistic, psycholinguistic, and SLA complexity features for English (Vajjala & Meurers 12, 13, 14a, b, c, Chen & Meurers 2016a, b) and German (Hancke, Vajjala, Meurers 12; Hancke & Meurers 2013) — and tools such as CTAP making it easy to use these measures.
Applying these methods to education, we investigate the (in)appropriateness of textbooks for students of different grades and school types (Bryant et al. 2017, Berendes et al., in press).
To support teachers and learners in identifying texts that are both interesting and richly represent the language constructs to be acquired, we created the linguistically-aware search engine FLAIR (Chinkina & Meurers 16). On this basis, we collaborate in the BMBF-funded KANSAS project with the German Institute for Adult Education (DIE) and the Mercator Institute for Literacy and Language Education to build a tool supporting teachers of functional literacy courses.
Connecting foundational and applied issues, we are spelling out Krashen's i+1 input fostering learning in terms of linguistic complexity using SyB (Chen & Meurers 17), a syntactic benchmarking tool, and we investigate the impact of challenging learners with such input.