AI-Lab: A Framework for Introducing Generative Artificial Intelligence Tools in Computer Programming Courses

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. With the proliferation of these tools, educators must evaluate their role in academia, as 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. Our research seeks to answer the question: “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. Specifically, AI-Lab presents opportunities to use GenAI for tailored support, such as topic introductions, detailed examples, corner case identification, rephrased explanations, and debugging assistance. Through identifying and rectifying GenAI’s errors, students enrich their learning process. Additionally, AI-Lab offers strategies for formulating prompts to elicit high-quality GenAI responses and provides mechanisms for educators to explore students’ perceptions of GenAI’s role in their learning experience.

Preliminary anonymous surveys show that at least 54.5% of our students use GenAI for homework. Notably, the framework highlights the risks of GenAI over-dependence as well as introducing its context-specific benefits, aiming to motivate students intrinsically towards balanced usage.

The AI-Lab framework underscores the importance of guiding students’ interactions with GenAI to maintain core skill development in Computer Science. By fostering an environment where GenAI is used as a pedagogical tool, educators can mitigate the risks associated with over-dependence on GenAI. 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, thus providing a balanced pathway for integrating GenAI into academia.