Artificial Intelligence (AI) tools have transformed software development, making it crucial to equip computer science (CS) students with the skills to leverage these technologies. This talk presents an innovative curriculum approach, integrating AI tools into an advanced CS capstone course at a stage where students possess foundational skills in software engineering. This strategic timing ensures that students can critically engage with AI, recognizing biases and managing challenges like hallucinations in AI-generated outputs.

Before redesigning the curriculum, independent research was conducted to understand the strengths and limitations of various AI tools, such as Lucidchart, Eraser.io for design documentation, and GitHub Copilot, GPT-4, Codeium, Claude, and Gemini for implementation tasks like code generation, code completion, UI design, error handling, and API integration. This research guided the curriculum by shaping assignment design and delivering foundational lectures on prompt engineering to ease the learning curve for students.

Experiments during the capstone course included AI-enhanced assignments and project, where students applied these tools for software design and implementation. Quantitative data-prompt refinement counts, error rates, and code accuracy, and qualitative reflections revealed increased confidence in AI tools, enhanced productivity, and greater readiness for industry roles. Despite these benefits, students faced challenges with complex tasks that required iterative refinement and oversight, but they gained skills in managing biases and hallucinations in AI outputs.

The curriculum’s “right-left” approach enables a smooth transition to AI-assisted development, preparing students for the evolving tech landscape. This talk shares key findings, best practices, and insights into balancing manual skills with AI-enhanced learning.