We are happy to announce the 15th edition of the NLP4CALL workshop on Natural Language Processing for Computer-Assisted Language Learning. This year, NLP4CALL will be a two-day hybrid event that will take place on November 19-20, 2026. Onsite participation will be possible at the Humanisten building of the University of Gothenburg (Renströmsgatan 6, Göteborg, Sweden) and a link for remote participation will be shared closer to the workshop dates. Both onsite and online participation are free of charge.
The workshop series on Natural Language Processing (NLP) for Computer-Assisted Language Learning (NLP4CALL) is a meeting place for researchers working on integrating Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising from this connection. The latter includes, among others, the integration of insights from Second Language Acquisition (SLA) research, and the promotion of “Computational SLA” through setting up Second Language research infrastructures.
The intersection of Natural Language Processing (or Language Technology / Computational Linguistics) and Speech Technology with Computer-Assisted Language Learning (CALL) brings “understanding” of language to CALL tools, thus making CALL intelligent. This fact has given the name for this area of research — Intelligent CALL, or short, ICALL. As the definition suggests, apart from having excellent knowledge of Natural Language Processing and/or Speech Technology, ICALL researchers need good insights into SLA theories and practices, as well as knowledge of second language pedagogy and didactics. This workshop therefore invites a wide range of ICALL-relevant research, including studies where NLP-enriched tools are used for testing SLA and pedagogical theories, and vice versa, where SLA theories, pedagogical practices or empirical data are modeled in ICALL tools. The NLP4CALL workshop series is aimed at bringing together competences from these areas for sharing experiences and brainstorming around the future of the field.
We welcome papers:
This year, the workshop has a special focus on process-oriented approaches to educational NLP, including but not limited to work related to the collection and analysis of keystroke data from langauge learners.
Submissions should describe original unpublished complete or in-progress work and follow the ACL Guidelines for Generative Assistance in Authorship.
Each submitted paper will be peer-reviewed by at least two members of the program committee in a double-blind fashion. All accepted papers will be collected into a proceedings volume to be published both in the NEALT Proceeding Series and through the ACL anthology.
We accept short, long and demo papers, all of which have to adhere to the following page limits:
Also note that:
Papers should be submitted as PDFs through EasyChair. LaTeX and Word templates are available here.
All deadlines are AoE.
Registrations are open! You can find the registration form here.
Bio: Andrea Horbach has been professor for Educational NLP at Kiel University and the Leibniz Institute for Science and Mathematics Education (IPN) since 2024. Her research interests include free-text scoring, argument mining, automated feedback, and the use of process data for assessment. She is particularly interested in developing explainable and fair methods that support human agency in writing and assessment processes.
Abstract: Automated essay scoring has traditionally focused on the text as the final written product. More recently, corpora that include keylogging data have made it possible to shift the focus toward writing as a process: to investigate how students write and revise their texts, and to explore whether scoring models can provide useful feedback before the final essay is completed. Moreover, AI-based writing support is becoming increasingly available, but we still know comparatively little about how students actually use such systems while writing. In this talk, I will give an overview of ongoing work based on data from the German PISA 2025 foreign language assessment. The data combine final essays and their scores with intermediate text states, keylogging information, and interaction logs from an LLM-based writing support chatbot. I will present first results on how intermediate texts and process information can be used as early evidence for automated scoring, and how students interact with AI support during writing. I will conclude by discussing what these data can tell us about timely feedback, the interpretation of process evidence, and the role of AI support in computer-assisted language learning.
Bio: Rianne Conijn is an assistant professor in learning analytics in the Human-Technology Interaction group at Eindhoven University of Technology, the Netherlands. Her research focuses on developing methods to capture and model learning behavior (e.g., self-regulated learning) and writing behavior (e.g., revision processes), using fine-grained behavioral data, such as keystroke and clickstream data. In addition, she uses these methods to examine how educational technologies, such as generative AI, affect the learning process. Rianne recently received a NWO Veni grant on “Human-Centered AI in education” where she aims to improve human-AI collaboration in writing in higher education.
Abstract: This abstract has been rewritten by me and polished with AI several times, yet you only read this final version. The final product of writing reveals little of the cognitive and behavioural activities involved in producing it. Keystroke logging, e.g. using Inputlog, can provide detailed insights into how writing unfolds. However, its value depends on how we interpret and analyse these data. In this talk, I show how keystroke data can inform our understanding of students’ writing at three levels, each making different aspects of the process visible and each requiring its own analytical choices. The micro level covers individual key events and short sequences, for instance showing that pauses cannot simply be equated with planning. The meso level characterizes how a writing session unfolds over time, revealing patterns that can inform writing instruction that whole-session averages obscure. The macro level extends the analysis across a project, tracing activity across sessions, drafts, and changing task demands. Finally, I discuss how these perspectives can help us investigate writing with generative AI, drawing on ongoing research into students’ master thesis writing. Together, these perspectives show how analysing writing at different levels can deepen our understanding of how students produce texts, both with and without generative AI.
For more information about the workshop, you are welcome to reach out to nlp4call2026@easychair.org.
This workshop is jointly supported by:
2026