The first generation of students is learning to program alongside GenAI (Generative Artificial Intelligence) tools, raising questions about how young learners critically engage with them and perceive ethical responsibilities. While prior research has focused on university students or developers, little is known about secondary school novices, who represent the next cohort of software engineers. To address this gap, we conducted an exploratory study with 84 German secondary school students aged 16-19 attending software development workshops. We examined their critical thinking practices in AI-assisted programming, perceptions of AI ethics and responsibility, and gender-related differences in their views. Our results reveal an AI paradox: students demonstrate strong ethical reasoning and awareness about AI, yet many report integrating AI-generated code without a thorough understanding of it. The majority of our cohort attributed significant responsibility for AI practices to politics and corporations, potentially reflecting Germany's cultural context, with its strict regulations and data privacy discourse. Boys reported more frequent and experimental use of AI-assisted programming, whereas girls expressed greater scepticism and emphasised peer collaboration over GenAI assistance. Our findings highlight the importance of culturally responsive software engineering education that strengthens critical AI literacy in AI-assisted programming by linking ethics to concrete code artefacts and preparing young learners for this AI-driven software landscape.

翻译：第一代学生正在伴随生成式人工智能工具学习编程，这引发了一个问题：年轻学习者如何批判性地接触这些工具，以及他们如何感知伦理责任。虽然先前的研究聚焦于大学生或开发者，但我们对代表了下一代软件工程师的中学生新手知之甚少。为填补这一空白，我们对84名参加软件开发工作坊的16-19岁德国中学生开展了一项探索性研究。我们考察了他们在人工智能辅助编程中的批判性思维实践、对人工智能伦理与责任的认知，以及相关观点中的性别差异。我们的结果揭示了一个"人工智能悖论"：学生展现出较强的伦理推理能力和对人工智能的认知意识，但许多人报告称在未充分理解的情况下便整合了人工智能生成的代码。我们研究中的大部分学生将人工智能实践的重大责任归于政治和企业，这可能反映了德国严格监管和数据隐私讨论的文化背景。男生报告更频繁且更具实验性地使用人工智能辅助编程，而女生则表现出更大的怀疑态度，并强调同伴协作而非生成式人工智能辅助。我们的发现凸显了文化响应式软件工程教育的重要性——通过将伦理与具体代码产物关联，强化人工智能辅助编程中的批判性人工智能素养，从而帮助年轻学习者为这一人工智能驱动的软件环境做好准备。