We invite submissions in two categories: short papers (2 pages) on teaching materials and full papers (8 pages) on original, unpublished research (both regular research papers and position papers).
All submissions must use the official ACL LaTeX style template and follow the standard ACL submission requirements. References and appendices do not count against the page limit (for both submission types). Limitations and ethical considerations are optional and do not count against these limits either. Papers that do not conform to these requirements will be desk-rejected without review.
For submission and the review process, we will use OpenReview. If you do not have an OpenReview account yet, make sure to create it well in advance of the deadline. This is especially important, as in some cases, the approval of the account may take some time. The reviewing process will be single-blind.
Submission Link: https://openreview.net/group?id=eacl.org/EACL/2026/Workshop/TeachNLP
Submission Type 1: Short Papers on Teaching Materials
We invite submissions of short papers of 1-2 pages that describe teaching materials such as curricula, course GitHub repositories, Jupyter notebooks, slides, homework, programming assignments, or projects. These short papers need not be anonymized, but will be peer-reviewed and published as part of the workshop proceedings, and presented as posters and/or demos. The associated teaching materials, while not being part of the proceedings, should be submitted in addition to the short paper. We will create a Teaching NLP repository where authors may opt in to make their materials available for reuse after the workshop.
Submission Type 2: Full Papers
We invite papers of up to 8 pages discussing pedagogical aspects of NLP, focusing on (but not limited to) any of the following general topics:
Tools and methodologies (e.g., teaching with code, active learning, flipped classroom)
Scaling curricula to fit large class sizes
Adapting existing curricula to incorporate new NLP advancements
Teaching online NLP courses or adjusting courses to become remote
Challenges of designing the first NLP course or related degree program at a college, university, or on a MOOC platform
Teaching heterogeneous groups of students (e.g., with respect to prior experience in computer science and linguistics, with respect to their social and cultural background, etc.)
Teaching underrepresented students
Bridging the gap between academic training and industry needs
Incorporating ethics, reproducibility, and responsible practices in NLP courses
Teaching multilingual NLP