# CS1-LLM: Integrating LLMs into CS1 Instruction

arXiv: https://arxiv.org/abs/2406.15379 · HTML: https://mlsanity.com/paper/2406.15379/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2024.

## Built on by (most cited first, 16 of 16)

- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios](https://mlsanity.com/paper/2505.16160/) (2025)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [How Do Programming Students Use Generative AI?](https://mlsanity.com/paper/2501.10091/) (2025)
- [Code Roulette: How Prompt Variability Affects LLM Code Generation](https://mlsanity.com/paper/2506.10204/) (2025)
- [Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails](https://mlsanity.com/paper/2504.11146/) (2025)
- [The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own](https://mlsanity.com/paper/2503.05760/) (2025)
- [Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education](https://mlsanity.com/paper/2512.23982/) (2025)
- ["Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbots](https://mlsanity.com/paper/2603.00057/) (2026)
- [Demystify, Use, Reflect: Preparing students to be informed LLM-users](https://mlsanity.com/paper/2511.11764/) (2025)
- [A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation](https://mlsanity.com/paper/2505.00100/) (2025)
- [Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting](https://mlsanity.com/paper/2605.05144/) (2026)
- [To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks](https://mlsanity.com/paper/2606.30549/) (2026)
- [Is Solving Better Than Evaluating GenAI Solutions?](https://mlsanity.com/paper/2607.27586/) (2026)
- [VibeJam: A User Study Platform for Web Development with Agents](https://mlsanity.com/paper/2608.29889/) (2026)

## Abstract (arXiv)

The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1. In addition, professional software engineers are often using these tools, raising the question of whether we should be training our students in their use as well. This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of LLMs from the beginning of the course. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Instead, the course now emphasizes skills needed to successfully produce software with an LLM. This includes explaining code, testing code, and decomposing large problems into small functions that are solvable by an LLM. In addition to frequent, formative assessments of these skills, students were given three large, open-ended projects in three separate domains (data science, image processing, and game design) that allowed them to showcase their creativity in topics of their choosing. In an end-of-term survey, students reported that they appreciated learning with the assistance of the LLM and that they interacted with the LLM in a variety of ways when writing code. We provide lessons learned for instructors who may wish to incorporate LLMs into their course.