Literature Review on the Integration of Generative AI in Programming Education
Jemimah Nathaniel, Solomon Sunday Oyelere, Jarkko Suhonen, Matti Tedre
Abstract
Abstract Integrating generative artificial intelligence tools like ChatGPT and GitHub Copilot into programming education presents notable opportunities to enhance learning but also raises critical challenges in balancing innovation with preserving foundational programming skills. Through a systematic review of 40 empirical studies guided by PRISMA 2020 and Kitchenham’s methodologies, this study evaluates how effectively GenAI was incorporated into programming education and its impact on preserving higher-order thinking skills and foundational programming logic. The Findings reveal that successful integration hinges on intentional teaching strategies, thoughtfully designed assessments, and structured integration processes. However, barriers such as GenAI tools’ limited accessibility features, insufficient bias mitigation, and a tendency to prioritize tool availability over curriculum alignment often disrupt seamless adoption. Additionally, the potential for students to become over-reliant on AI risks diminishing higher-order thinking skills and programming logic. To address these gaps, this paper proposes the GenAI-Ped framework, a structured approach combining self-regulated learning, universal design principles, and iterative feedback to harmonize GenAI-driven support with skill development. The findings emphasized the necessity of strategic implementation, educator training, and inclusive practices to maximize GenAI’s potential in programming education.