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.
From Sentiment Classification to Actionable and Responsible Feedback: A Scoping Review and Evidence Map of NLP in Student Evaluation of Teaching, 2015-2026
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
University of the Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Hong Kong（香港大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；SenseTime Research（商汤科技研究院）
Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students
University of Trento（特伦托大学）；Aston University（阿斯顿大学）；University of Bologna（博洛尼亚大学）；University of Pisa（比萨大学）；University of Trieste（的里雅斯特大学）；University of Groningen（格罗宁根大学）
UKP-SQuARE: An Interactive Tool for Teaching Question Answering
Ubiquitous Knowledge Processing Lab (UKP Lab)（普适知识处理实验室（UKP实验室））；Department of Computer Science（计算机科学系）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））；Technical University of Darmstadt（达姆施塔特工业大学）
Beyond Helpfulness: A Teaching-over-Solving Diagnostic for Measuring Educational Impact in LLM Tutors
Washington University in St. Louis（圣路易斯华盛顿大学）；Department of Operations Research and Engineering Management, Southern Methodist University（南卫理公会大学运筹学与工程管理系）
Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment
Vrije University of Amsterdam（阿姆斯特丹自由大学）；University College London（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）；National Institute of Informatics（日本信息处理学会）；Shandong University（山东大学）；Guangzhou University（广州大学）
Fine-tuning foundational models to code diagnoses from veterinary health records
Colorado State University (CSU) Data Science Research Institute（科罗拉多州立大学（CSU）数据科学研究院）；CSU College of Veterinary Medicine and Biomedical Sciences（科罗拉多州立大学兽医医学与生物科学学院）；CSU Veterinary Teaching Hospital（科罗拉多州立大学兽医教学医院）；Southern California Clinical and Translational Science Institute（南加州临床与转化科学研究院）
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
East China Normal University（华东师范大学）；Institute of AI for Education, ECNU（华东师范大学教育人工智能研究院）；Fudan University（复旦大学）；ZhuQingTing Data Technology (Zhejiang) Co., Ltd.（竹蜻蜓数据科技（浙江）有限公司）
