Author: Denis Avetisyan
A new era of software creation is upon us, driven by artificial intelligence and demanding a rethink of how we teach and practice computer science.
This review analyzes current industry adoption of AI-assisted development-including practices like ‘vibe coding’ and agentic systems-and proposes curriculum adjustments for future software engineers.
While software development has long benefited from automation, the recent surge in large language models presents a paradigm shift challenging established practices and educational approaches. This paper, ‘Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education’, investigates how professionals are adopting AI-assisted coding techniques-including ‘vibe coding’ and agentic systems-and reveals both significant productivity gains and emerging concerns regarding code quality and skill erosion. Our analysis of industry reflections highlights a crucial need to recalibrate computer science curricula, prioritizing problem-solving, architectural thinking, and robust code review practices. How can educational institutions best prepare future engineers for a development landscape increasingly shaped by intelligent tools and evolving workflows?
The Shifting Landscape of Code Creation
For decades, the arduous task of manually crafting code represented the primary time constraint in software development. Programmers would spend countless hours meticulously writing, debugging, and refining lines of instruction to achieve desired functionality. This process, demanding both technical skill and significant time investment, often dictated project timelines and resource allocation. The sheer volume of code required for even moderately complex applications meant that the act of writing – the literal keystrokes and logical construction – was the most significant bottleneck. Consequently, a programmer’s speed and efficiency in coding were highly valued, and tools focused primarily on enhancing this specific skill, such as advanced text editors and integrated development environments. However, this paradigm is now undergoing a substantial transformation as automated code generation tools begin to shoulder much of the coding burden.
The landscape of software creation is undergoing a significant transformation as Large Language Models (LLMs) redefine the primary challenges in development. Historically, the bulk of effort resided in the manual writing of code; however, LLMs are now automating a substantial portion of this task. Consequently, the most time-consuming aspect is no longer generating code, but rather ensuring its quality and reliability through rigorous review and testing processes. This shift necessitates a greater focus on validation, security analysis, and identifying potential bugs within LLM-generated code, demanding more sophisticated tools and expertise in these areas. The implications extend beyond simple efficiency gains, prompting a re-evaluation of development workflows and skillsets to effectively leverage the power of AI-assisted coding.
The landscape of software creation is undergoing a significant transformation, evidenced by a clear shift in the primary development bottleneck. Historically, the most demanding aspect of software engineering was the meticulous process of writing code line-by-line; however, increasingly sophisticated Large Language Models are automating this task at an accelerating rate. Current data suggests this isn’t a future projection, but a present reality, with an estimated 25% of startups in Silicon Valley and between 20-30% of the code within Microsoft’s repositories now being generated with the assistance of these LLM-powered tools. This indicates a fundamental change in focus – from writing code to rigorously reviewing and testing it, demanding new skillsets and workflows for developers and signaling a broader evolution in how software is built, maintained, and ultimately, delivered.
New Tools, Evolving Concerns
Agentic coding utilizes Large Language Models (LLMs) to autonomously execute coding tasks, moving beyond simple code completion to encompass planning, debugging, and even project management functions. This is achieved by providing the LLM with high-level goals and allowing it to independently determine the necessary steps and code implementations. Complementing this is ‘Vibe Coding’, a more iterative approach where developers provide contextual information and stylistic preferences – the ‘vibe’ – to the LLM, influencing the generated code’s aesthetic and adherence to specific coding conventions. Both techniques represent a shift from traditional coding paradigms, automating tasks previously requiring significant manual effort and impacting the software development lifecycle through increased velocity and potential for code generation at scale.
Prompt engineering is the iterative process of crafting text-based instructions, known as prompts, to elicit specific and desired outputs from Large Language Models (LLMs) used in code generation. The efficacy of LLM-assisted coding is directly correlated with prompt quality; well-defined prompts detailing the required functionality, input/output specifications, and desired coding style yield more accurate and usable code. This involves not only clearly stating the task but also refining the prompt through experimentation and analysis of the LLM’s responses, often requiring developers to learn techniques such as few-shot learning, chain-of-thought prompting, and the use of specific keywords to optimize performance and minimize errors in the generated code.
The increasing integration of Large Language Models (LLMs) into coding workflows is generating concern regarding potential skill erosion among software developers. Current data indicates substantial adoption rates, with approximately 25% of Silicon Valley startups and 20-30% of code within Microsoft’s repositories now utilizing LLM-enhanced tools for code generation and completion. This reliance on automated assistance may reduce opportunities for developers to practice and refine fundamental coding skills, potentially impacting their ability to independently solve complex problems or adapt to novel technologies without LLM support. While increased productivity is a noted benefit, the long-term consequences of diminished core competencies remain a subject of ongoing assessment within the industry.
The Imperative of Quality and Security
AI-generated code, while increasing development velocity, introduces potential security vulnerabilities due to the possibility of flawed logic, injection of insecure patterns learned from training data, or the unintentional creation of backdoors. Automated code generation tools may not consistently adhere to secure coding practices, and can replicate vulnerabilities present in the datasets they were trained on. Consequently, thorough vetting, including static and dynamic analysis, penetration testing, and manual code review, is essential to identify and remediate these risks before deployment. The increasing prevalence of AI-assisted development – currently at 25% of Silicon Valley startups and 20-30% of Microsoft’s codebase – amplifies the importance of proactive security measures in the software development lifecycle.
The increasing adoption of AI-assisted development tools necessitates a strong focus on code quality and maintainability. While these tools can accelerate development cycles, they may inadvertently introduce code that prioritizes rapid output over adherence to established best practices, such as comprehensive error handling, clear documentation, and efficient resource management. This can lead to technical debt, increased debugging time, and reduced long-term stability of the software. Consequently, organizations must implement rigorous quality control measures, including static analysis, unit testing, and manual code review, to ensure that AI-generated code meets necessary standards for reliability and future modification.
Given the increasing prevalence of AI-assisted code generation – currently impacting 25% of Silicon Valley startups and 20-30% of Microsoft’s codebase – traditional code review practices are more critical than ever. While AI tools accelerate development, they do not inherently guarantee code quality or security. Manual review by experienced developers remains essential to identify potential vulnerabilities, ensure adherence to coding standards, and verify the logical correctness of AI-generated code. This process helps mitigate risks associated with automated generation and maintains the overall reliability and maintainability of the software.
Cultivating Future Developers: Skills for an AI-Driven World
The increasing prevalence of AI-driven coding tools, while boosting productivity, presents a potential challenge to the development of core ‘Computational Thinking’ skills. These fundamental abilities – decomposition, pattern recognition, abstraction, and algorithm design – are crucial for problem-solving, not just in coding, but across numerous disciplines. A dependence on AI to automatically generate code risks diminishing a developer’s capacity to logically analyze problems and devise effective solutions independently. Without actively engaging in the process of breaking down complex tasks, identifying underlying structures, and formulating step-by-step instructions, individuals may become proficient at using code, but less capable at truly understanding and innovating with it. This shift could ultimately limit their ability to adapt to novel challenges and contribute meaningfully to the evolution of software development, particularly as AI takes on an ever-greater role in the coding process.
To counteract the potential erosion of core programming skills in an age of AI-assisted coding, educators are increasingly turning to immersive pedagogical strategies. Project-Based Learning encourages developers to tackle realistic challenges, demanding they conceptualize, design, and implement solutions from inception to completion – skills that AI tools cannot fully replicate. Complementing this, Specification-Driven Development emphasizes the creation of detailed requirements before coding begins, forcing a deep understanding of the problem domain and fostering logical thinking. These approaches don’t reject AI tools; rather, they position them as supplementary assets, used to enhance rather than replace fundamental computational thinking abilities. By prioritizing these skills alongside AI integration, educational programs can better prepare future developers to innovate and problem-solve effectively in a rapidly evolving technological landscape.
The increasing prevalence of artificial intelligence in software development necessitates a carefully considered educational strategy. Current estimates suggest AI now composes a substantial portion of codebases – around 25% of Silicon Valley startups and 20-30% of Microsoft’s repositories – indicating a rapid shift in how software is built. While these tools dramatically increase productivity, a sole reliance on AI-generated code risks eroding developers’ foundational problem-solving abilities. Therefore, cultivating a balanced skillset-one that embraces AI assistance yet prioritizes core computational thinking and a deep understanding of underlying principles-is crucial. This approach ensures future developers can not only utilize AI effectively but also critically evaluate, debug, and innovate beyond the capabilities of current AI systems, maintaining a vital role in the evolving technological landscape.
The exploration of agentic coding, as detailed within the paper, necessitates a shift in how computational thinking is approached. It is not merely about instructing a machine, but fostering a collaborative dynamic. This mirrors Marvin Minsky’s assertion: “The more we understand about how things work, the more we realize how little we know.” The paper suggests a move beyond solely focusing on algorithmic precision to embracing the nuanced, iterative process of working with AI agents. Such an approach acknowledges the inherent uncertainty in complex systems and prioritizes adaptability – a principle central to both effective software development and a truly comprehensive computer science education. The core idea of the article emphasizes that future software engineers must learn to guide and refine these agents, rather than simply dictating solutions.
The Road Ahead
The current enthusiasm for ‘vibe coding’ and ‘agentic coding’ feels…familiar. Each generation rediscovers the allure of automating the tedious, of shifting responsibility to a system-any system-that promises to deliver code with minimal human intervention. The question, predictably, isn’t whether the tools will become more capable-they invariably will-but whether the underlying problems of software development are actually being addressed, or merely obscured by a more sophisticated layer of abstraction. They called it a framework to hide the panic, and that impulse appears timeless.
The paper rightly identifies a need to recalibrate computer science education. However, focusing solely on ‘prompt engineering’ risks treating the symptom, not the disease. A truly robust curriculum should emphasize, paradoxically, the fundamentals. A deeper understanding of algorithms, data structures, and, crucially, the limits of computation, will prove more valuable than mastery of any particular LLM. After all, tools change; principles endure.
Future work should investigate the long-term cognitive effects of relying heavily on AI-assisted development. Are developers becoming more, or less, capable problem-solvers? Is the ability to debug-to trace the logic of a system-atrophying? These aren’t merely technical questions; they speak to the very nature of expertise, and the subtle art of building things that, occasionally, work as intended.
Original article: https://arxiv.org/pdf/2512.23982.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-01-01 08:48