Artificial intelligence in computer programming education: A systematic literature review

Abstract

The demand for skilled programmers and the increasing complexity of coding skills have led to a rise in the adoption of artificial intelligence (AI) and machine learning (ML) technologies in computer programming education. Previous research has explored the potential of AI in aspects such as grading assignments, generating feedback, detecting plagiarism, and identifying at-risk students, but there is a lack of systematic reviews focused on AI-powered teaching processes in computer programming classes. To provide a more comprehensive understanding of AI and ML's role in computer programming education, this systematic review examines a wider range of applications across the entire pedagogical process. Analyzing 119 relevant research papers published between 2012 and 2024, this review offers an overview of AI and ML tools and techniques used in various educational contexts. Aligned with instructional design models, the reviewed literature is categorized into four key areas: course design, classroom implementation, assessment and feedback, and performance monitoring. This systematic review not only highlights the practical tools available to instructors but also identifies research trends and potential areas for future exploration in the field of computer programming education. • This SLR comprehensively analyzes how AI and ML enhance the pedagogical process in higher education computer programming courses. • After 2020, research has shifted from AI-powered performance monitoring to classroom implementation and assessment. • Recent literature, leveraging advanced computational capabilities, has begun exploring course design applications and processes. • While most publications use traditional ML, deep learning enables researchers to investigate more complex data sources. • Instructors can efficiently focus on creative tasks, while learners benefit from timely and personalized assistance.

Topics & Concepts