# Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education arXiv: https://arxiv.org/abs/2512.23982 ยท HTML: https://mlsanity.com/paper/2512.23982/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 27 of its 83 references that are in the map. - First posted on arXiv 30 Dec 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 27 of 27) - [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023) - [Does Prompt Formatting Have Any Impact on LLM Performance?](https://mlsanity.com/paper/2411.10541/) (2024) - [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023) - [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023) - [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024) - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022) - [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023) - [A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning](https://mlsanity.com/paper/2406.05804/) (2024) - [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023) - ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023) - [How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment](https://mlsanity.com/paper/2309.14049/) (2023) - [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023) - [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024) - [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017) - [AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities](https://mlsanity.com/paper/2508.11126/) (2025) - [Generative AI in Computing Education: Perspectives of Students and Instructors](https://mlsanity.com/paper/2308.04309/) (2023) - [Desirable Characteristics for AI Teaching Assistants in Programming Education](https://mlsanity.com/paper/2405.14178/) (2024) - [CS1-LLM: Integrating LLMs into CS1 Instruction](https://mlsanity.com/paper/2406.15379/) (2024) - [Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning](https://mlsanity.com/paper/2410.08922/) (2024) - [Student-AI Interaction: A Case Study of CS1 students](https://mlsanity.com/paper/2407.00305/) (2024) - [MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models](https://mlsanity.com/paper/2501.15000/) (2025) - [Substance Beats Style: Why Beginning Students Fail to Code with LLMs](https://mlsanity.com/paper/2410.19792/) (2024) - [A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts](https://mlsanity.com/paper/2410.11877/) (2024) - [Novices' Learning Barriers When Using Code Examples in Open-Ended Programming](https://mlsanity.com/paper/2104.11806/) (2021) - [Rapid AIdeation: Generating Ideas With the Self and in Collaboration With Large Language Models](https://mlsanity.com/paper/2403.12928/) (2024) - [Refinement-Based Specification: Requirements and Architecture](https://mlsanity.com/paper/1404.7260/) (2014) - [Exploring the Role of Creativity in Software Engineering](https://mlsanity.com/paper/2101.00837/) (2021) ## Built on by (most cited first, 2 of 2) - [Context Before Code: An Experience Report on Vibe Coding in Practice](https://mlsanity.com/paper/2603.11073/) (2026) - [Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development](https://mlsanity.com/paper/2607.06101/) (2026) ## Abstract (arXiv) Recent advances in large language models (LLMs) have introduced new paradigms in software development, including vibe coding, AI-assisted coding, and agentic coding, fundamentally reshaping how software is designed, implemented, and maintained. Prior research has primarily examined AI-based coding at the individual level or in educational settings, leaving industrial practitioners' perspectives underexplored. This paper addresses this gap by investigating how LLM coding tools are used in professional practice, the associated concerns and risks, and the resulting transformations in development workflows, with particular attention to implications for computing education. We conducted a qualitative analysis of 57 curated YouTube videos published between late 2024 and 2025, capturing reflections and experiences shared by practitioners. Following a filtering and quality assessment process, the selected sources were analyzed to compare LLM-based and traditional programming, identify emerging risks, and characterize evolving workflows. Our findings reveal definitions of AI-based coding practices, notable productivity gains, and lowered barriers to entry. Practitioners also report a shift in development bottlenecks toward code review and concerns regarding code quality, maintainability, security vulnerabilities, ethical issues, erosion of foundational problem-solving skills, and insufficient preparation of entry-level engineers. Building on these insights, we discuss implications for computer science and software engineering education and argue for curricular shifts toward problem-solving, architectural thinking, code review, and early project-based learning that integrates LLM tools. This study offers an industry-grounded perspective on AI-based coding and provides guidance for aligning educational practices with rapidly evolving professional realities.