From Code-Centric to Concept-Centric: Teaching NLP with LLM-Assisted "Vibe Coding"

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.

💡 Research Summary

The paper introduces “Vibe Coding,” a pedagogical framework that integrates Large Language Models (LLMs) as coding assistants while deliberately shifting assessment focus from syntactic fluency to conceptual mastery in an undergraduate Natural Language Processing (NLP) course. Recognizing that traditional lab‑based instruction often forces students to spend excessive time debugging low‑level code errors, the authors propose three core components: (1) sanctioned use of LLMs for code generation during labs, (2) mandatory logging of prompts, responses, and iterations to make AI interaction visible and reflective, and (3) an assessment scheme weighted 20 % on functional code output, 30 % on the quality and depth of prompt logs, and 50 % on critical reflection questions that probe design decisions, theoretical understanding, and result analysis.

The framework was deployed in the Fall 2025 semester at King Saud University (Riyadh) in a senior‑level NLP course with 19 information‑technology majors. The curriculum combined 12 weeks of lectures, seven two‑hour labs, and a 12‑week team project that localized the Physical Interaction Question Answering (PIQA) dataset into Modern Standard Arabic. Lab topics covered tokenization, POS/NER, text classification, n‑gram language models, word embeddings, transformer fine‑tuning, and in‑context learning. Each lab followed a consistent structure: brief lecture, LLM‑assisted coding, prompt‑log documentation, and a set of high‑order reflection questions.

Quantitative survey results show high overall satisfaction (course relevance M = 4.68/5, theory‑practice balance M = 4.58/5, confidence in NLP concepts M = 4.00/5). Students rated LLM use as engaging (M = 4.42) and believed the labs taught concepts effectively despite the automation (M = 4.42). The prompt‑log component received a moderate mean (M = 3.79) with larger variance, indicating differing perceived benefits. Reflection‑based assessment was viewed as fair (M = 4.21) and the shift in grading weights appropriate (M = 4.26). However, time allocation was a concern (M = 3.53), and many students felt the final project scope was ambitious relative to available time (M = 3.84).

Thematic analysis of open‑ended responses identified five salient themes. First, offloading implementation to LLMs reduced cognitive load, allowing students to focus on conceptual reasoning and rapid experimentation. Second, prompt engineering emerged as a transferable skill; students reported improvement in crafting precise, iterative prompts. Third, verification of LLM outputs proved challenging, especially for Arabic NLP tasks where model performance was less reliable, creating a boot‑strapping problem that required solid foundational knowledge. Fourth, despite reduced debugging effort, time pressure persisted, shifting from syntax correction to understanding, verification, and documentation. Fifth, in the capstone project, students strategically employed LLMs for debugging, complex algorithm implementation, environment setup, and iterative guidance, treating the tools as supplements rather than replacements for their own expertise.

The study demonstrates that, when coupled with structured prompt logging and reflection‑oriented assessment, LLM‑assisted coding can successfully redirect student effort from low‑level coding mechanics toward higher‑order learning outcomes. It also highlights practical challenges—verification workload, time management, and the need for explicit instruction on prompt engineering—that must be addressed to scale the approach. Future work is suggested to include objective measures of cognitive load, development of automated LLM‑output validation tools, and exploration of the framework across diverse disciplines, course levels, and cultural contexts.