Large language models (LLMs) are becoming central to natural language processing education, yet materials showing their mechanics are sparse. We present AnimatedLLM, an interactive web application that provides step-by-step visualizations of a Transformer language model. AnimatedLLM runs entirely in the browser, using pre-computed traces of open LLMs applied on manually curated inputs. The application is available at https://animatedllm.github.io, both as a teaching aid and for self-educational purposes.

Argument maps are used extensively in Natural Language Processing (NLP), for training Large Language Models (LLMs) to analyze and generate arguments coherently. This paper discusses the pedagogic applications of the concept of argument mapping to enhance critical thinking in learning within educational contexts. The approach was found to be useful for shaping the thinking process during thesis writing and project courses and can be applied in higher education. In the age of rapid Gen AI advancement, it is important to embed critical thinking into education and such approaches can address challenges like AI overuse and potential loss of key skills and competences in learners. Argument mapping necessitates learners to visualize their thinking and while doing so, they not only achieve clarity of thought, but also make distinct connections between concepts in the form of arguments. Such clarity is at a much higher level compared to that achieved through concept or mind mapping as learners need to think in terms of well-formed claims and connections between them. In addition, collaborative argument mapping tasks could give learners opportunities for peer learning, and to concretize the abstract ideas through visualization and discussion.

The rapid advancement of Large Language Models (LLMs) presents both challenges and opportunities for Natural Language Processing (NLP) education. This paper introduces “Vibe Coding,” a pedagogical approach that leverages LLMs as coding assistants while maintaining focus on conceptual understanding and critical thinking. We describe the implementation of this approach in a senior-level undergraduate NLP course, where students completed seven labs using LLMs for code generation while being assessed primarily on conceptual understanding through critical reflection questions. Analysis of end-of-course feedback from 19 students reveals high satisfaction (mean scores 4.4-4.6/5.0) across engagement, conceptual learning, and assessment fairness. Students particularly valued the reduced cognitive load from debugging, enabling deeper focus on NLP concepts. However, challenges emerged around time constraints, LLM output verification, and the need for clearer task specifications. Our findings suggest that when properly structured with mandatory prompt logging and reflection-based assessment, LLM-assisted learning can shift focus from syntactic fluency to conceptual mastery, preparing students for an AI-augmented professional landscape.

LLM-based methods supersede many approaches in NLP at high velocity, making it necessary to adapt curricula. We argue that this effort also presents a chance to integrate LLM chatbots as learning support. We demonstrate (a) how we re-conceptualized an existing class segment on digital assistance systems to discuss LLM-based chatbots, (b) how we created a specialized instructional chatbot as a demonstrator that students could directly use for learning and revision and (c) how students’ initial perception of LLM-based AI changed due to instruction.

This short paper provides an overview of language technology related modules and courses developed at three leading universities of Latvia - University of Latvia (UL), Riga Technical University (RTU) and Riga Stradiņš University (RSU).

From being a niche technology with practical applications in translation and speech recognition, NLP is now underpinning the AI era through LLMs, promising a universal economic impact in the future. Although transitioning to the AI era is hyped by BigTech companies, practical adoption of the LLM capabilities for economically impactful tasks and processes goes via education of specialists capable to apply it properly. Human-in-the-loop, accuracy measurement, fine-tuning, on-premises processing of sensitive data have become essential skills for applying NLP. This short paper introduces two language technology modules developed and piloted at the Faculty of Science and Technology of the University of Latvia.

With the advent of Large Language Models (LLMs) researchers outside the Natural Language Processing (NLP) field are interested in learning how to process textual data for their own domain research goals. They are particularly motivated to start experimenting directly with LLMs, implicitly neglecting the large amount of accumulated knowledge that NLP has to offer them. In this text, we briefly share our new lesson materials that aim to show aspiring practitioners the strong connection between NLP fundamentals and LLMs, in the form of a two-day workshop. Our training material is mainly aimed at graduate students outside the NLP sphere who have basic technical knowledge and wish to start working with text, is fully open source and available online.

Modern LLM education is increasingly centered on system building: grounding generation with retrieval, enabling tool use, and deploying models under latency and cost constraints.We present an updated release of our open course on Transformer-based LLMs and multimodal models (Nikishina et al, 2024).The update introduces topics which became importance since the first edition, namely session on Retrieval Augmented Generation (RAG), a hands-on session on tool-using agents, an API-based track for applied work with LLM, and practical local inference with vLLM.We also add a dedicated session on multimodal dialog models with a focus on dialog grounding. We enriched the course with a discussion on long-context transformers, focusing on KV-cache efficiency along with the related models and benchmarks.All materials are released online.

The field of NLP has undergone vast, continuous transformations over the past few years, sparking debates going beyond discipline boundaries. This begs important questions in education: how do we design courses that bridge sub-disciplines in this shifting landscape? This paper explores this question from the angle of discourse processing, an area with rich linguistic insights and computational models for the intentional, attentional, and coherence structure of language. Discourse is highly relevant for open-ended or long-form text generation, yet this connection is under-explored in existing undergraduate curricula.We present a new course, "Computational Discourse and Natural Language Generation". The course is collaboratively designed by a team with complementary expertise and was offered for the first time in Fall 2025 as an upper-level undergraduate course, cross-listed between Linguistics and Computer Science. Our philosophy is to deeply integrate the theoretical and empirical aspects, and create an exploratory mindset inside the classroom and in the assignments. This paper describes the course in detail and concludes with takeaways from an independent survey as well as our vision for future directions.

Student demand for NLP training now spans linguistics, computer science, data science, and applied fields, producing cohorts with uneven preparation. We report on a four-course curriculum used in an M.S. Computational Linguistics program: an undergraduate on-ramp, a two-course graduate core (classical methods and neural/LLM methods), and a rotating special-topics seminar. We describe the role of each course, the bridging strategy that keeps the core sequence focused, and assessment patterns that emphasize error analysis, experimental reasoning, and reproducible practice. The goal is a set of reusable curricular design patterns for mixed-background programs facing rapid topic turnover in NLP.

NLP researchers regularly invoke abstract concepts like "interpretability," "bias," "reasoning," and "stereotypes," without defining them.Each subfield has a shared understanding or conceptualization of what these terms mean and how we should treat them, and this shared understanding is the basis on which operational decisions are made:Datasets are built to evaluate these concepts, metrics are proposed to quantify them, and claims are made about systems. But what do they mean, what _should_ they mean, and how should we measure them?I outline a seminar I created for students to explore these questions of conceptualization and operationalization, with an interdisciplinary reading list and an emphasis on discussion and critique.

We present an open, bachelor-level Natural Language Processing (NLP) course developed at Ukrainian Catholic University and delivered in Ukrainian. The course addresses several challenges in NLP education: adapting predominantly English-centric materials to a different linguistic and cultural context, supporting students with heterogeneous technical backgrounds, and balancing foundational theory with industry-relevant skills. All course materials, including lecture slides, notebooks, video recordings, and assignments, are publicly available. We describe our pedagogical design choices, focusing on culturally adapted tasks, integrated ethics, project-based assessment, and continuous student feedback. Our experience demonstrates that it is feasible to build a comprehensive and modern NLP curriculum from scratch in a non-English context, even when instructors come primarily from industry backgrounds.

This paper describes a Natural Language Processing (NLP) course taught at Kyiv School of Economics. The course consists of 16 lectures, 5 practical assignments and focuses on modern large language models (LLMs) while preserving an introduction to classical NLP. Practical assignments are organized using Kaggle, where GPU support plays an important role in enabling students to work with complex models. A key feature of the course is the focus on Ukrainian in the practical assignments, contributing to the development of Ukrainian NLP expertise and community. The course is taught primarily in-person, but due to the ongoing war in Ukraine, also includes a full online participation option and additional weekly QnA sessions.

As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent challenges in curriculum development: the field’s rapid evolution from both academia and industry, and the need to foster critical thinking beyond traditional technical training. We introduce our course on Ethical Aspects in NLP and our pedagogical approach, grounded in active learning through interactive sessions, hands-on activities, and “learning by teaching” methods. Over four years, the course has been refined and adapted across different institutions, educational levels, and interdisciplinary backgrounds; it has also yielded many reusable products, both in the form of teaching materials and in the form of actual educational products aimed at diverse audiences, made by the students themselves. By sharing our approach and experience, we hope to provide inspiration for educators seeking to incorporate social impact considerations into their curricula.

The exponential growth of AI education has brought millions of learners to online platforms, yet this massive scale has simultaneously exposed critical pedagogical shortcomings. Traditional video-based instruction, while cost-effective and scalable, demonstrates systematic failures in both sustaining learner engagement and facilitating the deep conceptual mastery essential for AI literacy. We present a pilot study evaluating a novel hybrid learning platform that integrates real-time conversational AI tutors with traditional video lectures. Our controlled experiment (N = 58,\ mean age M = 21.4,\ SD = 2.8) compared traditional video-based instruction with our AI-augmented video platform. This study employed a sequential within-subjects design where all participants first completed the traditional video condition followed by the AI-augmented condition, providing direct comparisons of learning outcomes. We measured learning effectiveness through immediate post-tests and delayed retention assessments (2-week delay). Results suggest improvements in learning performance: immediate post-test performance showed a large effect size (d = 1.505) with participants scoring 8.3 points higher after AI-augmented instruction (91.8\ vs.\ 83.5\ out of\ 100,\ p < .001). Behavioral analytics revealed increased engagement duration (71.1% improvement with AI tutoring) in the experimental group. This pilot study provides preliminary evidence that conversational AI tutors may enhance traditional educational delivery, suggesting a potential avenue for developing scalable, adaptive learning systems.

We developed Litteraturmaskinen, a graphical annotation and exploration interface that enables students to collaborate on labeling sentiment in literary passages, comparing their decisions with model predictions, and justifying their interpretations. We deployed the system in two educational settings: A university module on computational literary studies and regular teaching by two first-language high school teachers. Based on observations, collected teaching plans, and interviews, we find that tensions between epistemic and academic traditions are both a barrier for integration and a productive entry point for literary reflection and argumentation. We conclude with recommendations for integrating NLP into literature and first-language curricula.

The ubiquitous adoption of large language models by students prompts teachers to redesign courses and evaluation methods, especially in computer science and natural language processing (NLP) where the impact is more tangible.Our contribution is two-fold. First, we attempt to define invariants for the role of education itself given the over-abundance of information that appears to be more accessible than ever before. Then, we present our approach and materials used for an introductory course in NLP for undergraduate students, drawing inspiration from software engineering best practices. Our vision regarding large language models is torely on local models to cultivate a sense of ownership and sovereignty in an age where every bit of independence and privacy get eroded.