Balancing innovation and integrity in using generative AI for a new computer programming assignments approach

Abstract

This study presents a new structured approach to AI-assisted learning designed to support students in actively engaging with programming tasks, progressively developing independent problem-solving skills, and effectively utilizing Generative Artificial Intelligence (GenAI) in programming education. The framework, based on constructivist and experiential learning theories, aims to guide students in interacting with GenAI tools to enhance algorithmic, critical, and analytical reasoning rather than replace cognitive effort. The research employs a mixed-methods design, incorporating quantitative data from laboratory performance assignments and paper-based assessments of programming skills, as well as qualitative data from semi-structured expert interviews. Three experts from well-known Omani universities verified and confirmed the model's consistency with established learning theories and instructional design principles. Thirty-two undergraduate students at Sultan Qaboos University were divided into experimental and control groups. Quantitative analysis indicated that the experimental group, which adopted the new approach, significantly outperformed the control group, which used GenAI without restrictions, on assignments assessing code analysis, debugging, optimization, and problem-solving skills (p = 0.009). These findings suggest that the proposed model effectively balances creativity and academic integrity.