Over the last year, the ascent of Generative AI (GenAI) has raised concerns about its impact on core skill development, such as problem-solving and algorithmic thinking, in Computer Science students. Preliminary anonymous surveys show that at least 48.5% of our students use GenAI for homework. With the proliferation of these tools, the academic community must contemplate the appropriate role of these tools in education. Neglecting this might culminate in a phenomenon we term the "Junior-Year Wall," where students struggle in advanced courses due to prior over-dependence on GenAI. Instead of discouraging GenAI use, which may unintentionally foster covert usage, our research seeks to answer: "How can educators guide students' interactions with GenAI to preserve core skill development during their foundational academic years?" We introduce "AI-Lab," a pedagogical framework for guiding students in effectively leveraging GenAI within core collegiate programming courses. This framework accentuates GenAI's benefits and potential as a pedagogical instrument. By identifying and rectifying GenAI's errors, students enrich their learning process. Moreover, AI-Lab presents opportunities to use GenAI for tailored support such as topic introductions, detailed examples, corner case identification, rephrased explanations, and debugging assistance. Importantly, the framework highlights the risks of GenAI over-dependence, aiming to intrinsically motivate students towards balanced usage. This approach is premised on the idea that mere warnings of GenAI's potential failures may be misconstrued as instructional shortcomings rather than genuine tool limitations. Additionally, AI-Lab offers strategies for formulating prompts to elicit high-quality GenAI responses. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.

翻译：过去一年间，生成式人工智能的崛起引发了对其在计算机科学学生核心技能培养（如问题解决能力与算法思维）方面影响的担忧。初步匿名调查显示，至少48.5%的学生使用生成式人工智能完成作业。随着此类工具的普及，学术界必须审慎思考其在教育中的合理定位。若忽视此问题，可能催生我们称之为"大二壁垒"的现象——即学生因早期过度依赖生成式人工智能而在高阶课程中遭遇学习困难。与其采取可能助长隐性使用的禁止策略，我们的研究致力于回答："教育者应如何引导学生在基础学术阶段与生成式人工智能互动，以保障核心技能的发展？"我们提出"AI-Lab"教学框架，旨在指导学生在核心大学编程课程中有效运用生成式人工智能。该框架强调生成式人工智能作为教学工具的优势与潜力。通过识别并修正生成式人工智能的错误，学生得以强化学习过程。此外，AI-Lab提供了利用生成式人工智能实现个性化支持的多种途径，包括主题导入、详细示例、边界案例识别、解释性重述及调试辅助。尤为重要的是，该框架凸显过度依赖生成式人工智能的风险，旨在从内在动机层面引导学生实现平衡使用。这一方法基于如下理念：单纯警示生成式人工智能可能出现的失误，易被误解为教学缺陷而非工具局限。同时，AI-Lab提供了设计提示词以获取高质量生成式人工智能响应的策略。对教育者而言，AI-Lab还为其提供了探究学生如何看待生成式人工智能在学习体验中作用的机制。