你能感受到氛围吗？：对新手程序员参与氛围编码的探索
"Can you feel the vibes?": An exploration of novice programmer engagement with vibe coding
AI总结：
本文通过黑客松活动探讨新手程序员在氛围编码中的参与情况，发现其在快速原型设计和跨学科协作中的潜力，但也存在过早收敛和代码质量不均的问题。
AI中文摘要：
随着生成式AI和更广泛的AI辅助编程趋势的出现，'氛围编码'一词指的是通过自然语言提示来创建软件，而不是直接的代码编写。这种方法承诺民主化软件开发，但其教育影响仍处于探索阶段。本文报告了一项为期一天的教育黑客松活动，探讨新手程序员和混合经验团队如何参与氛围编码。我们组织了一个包容性的活动，邀请巴西一所公立大学的31名本科生参与，来自计算和非计算学科。通过观察、退出调查和半结构化访谈，我们研究了创意过程、工具使用模式、协作动态和学习成果。发现表明，氛围编码能够实现快速原型设计和跨学科协作，参与者在时间限制内发展了提示工程技能并交付了功能演示。然而，我们观察到在构思阶段过早收敛，代码质量不均需重新工作，以及对核心软件工程实践参与有限。团队采用了复杂的流程，结合多种AI工具在流水线配置中，人类判断对于关键精修仍至关重要。短格式（9小时）在帮助新手建立信心的同时，也能容纳时间有限的参与者。我们得出结论，当结合明确的发散思维、AI输出的批判性评估和对生产质量的现实期望时，氛围编码黑客松可以成为有价值的低风险学习环境。
英文摘要：
Emerging alongside generative AI and the broader trend of AI-assisted coding, the term "vibe coding" refers to creating software via natural language prompts rather than direct code authorship. This approach promises to democratize software development, but its educational implications remain underexplored. This paper reports on a one-day educational hackathon investigating how novice programmers and mixed-experience teams engage with vibe coding. We organized an inclusive event at a Brazilian public university with 31 undergraduate participants from computing and non-computing disciplines, divided into nine teams. Through observations, an exit survey, and semi-structured interviews, we examined creative processes, tool usage patterns, collaboration dynamics, and learning outcomes. Findings reveal that vibe coding enabled rapid prototyping and cross-disciplinary collaboration, with participants developing prompt engineering skills and delivering functional demonstrations within time constraints. However, we observed premature convergence in ideation, uneven code quality requiring rework, and limited engagement with core software engineering practices. Teams adopted sophisticated workflows combining multiple AI tools in pipeline configurations, with human judgment remaining essential for critical refinement. The short format (9 hours) proved effective for confidence-building among newcomers while accommodating participants with limited availability. We conclude that vibe coding hackathons can serve as valuable low-stakes learning environments when coupled with explicit scaffolds for divergent thinking, critical evaluation of AI outputs, and realistic expectations about production quality.
