Assessment of Coding Skills and Programming Knowledge in the Age of Generative AI: Best Practices and Effective Strategies for Computer Science Education

Abstract

The rapid emergence of artificial intelligence (AI) and generative AI tools poses a significant threat to the validity of traditional programming assessments. As the boundary between authored and AI-generated code becomes increasingly indiscernible, long-standing assessment models centered on output correctness and code submission are at risk of obsolescence. Despite its urgency, prior work has largely concentrated on detection, with limited emphasis on reimagining assessment design. This chapter addresses that gap by proposing strategies for assessing programming proficiencies in an AI-mediated context. Its objective is to help educators move beyond surveillance-based models and adopt approaches that emphasize uniquely human cognitive capacities. These pedagogical strategies advance the field by shifting the discourse from reactive prevention to proactive, pedagogically aligned assessment design. In doing so, the chapter affirms that the future of programming assessment is not about resisting AI but about designing systems that assess human thinking over code output. Copyright © 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.