The Nifty Assignment Generator: Integrating Cultural Relevance and Belonging into CS Fundamentals

Abstract

Computer programming assignments have long been the foundation of assessment in computer science education. However, the landscape has drastically shifted due to several factors: assignment reuse dating back for the past decades, widespread availability of solutions online, and the rise of Generative AI (GenAI) tools that can now produce highly optimized solutions - regardless of institutional policy. Compounding these issues are post-pandemic shifts in education, such as the prevalence of online instruction and increased pressure to align with workforce expectations and accreditation requirements. These challenges call for a fundamental rethinking of how programming assignments are designed and evaluated. This paper introduces a novel application that leverages GenAI to generate culturally relevant and engaging "nifty assignments"tailored to specific regions and student demographics. The goal is to enhance student motivation, sense of belonging, and learning outcomes while maintaining alignment with institutional memoranda of understanding (MOUs), accreditation standards, and the CS2023 curricular guidelines. The tool was piloted at a two-year community college, an educational setting known for its critical role in workforce development, educational access, and computing pathways. Early results suggest the approach can reinvigorate course content, improve student engagement, and support recruitment and retention in computing programs.