  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.
