Teaching Programming in the Age of Generative AI: Insights from Literature, Pedagogical Proposals, and Student Perspectives

TL;DR

This paper reviews the impact of generative AI on programming education, proposing visual tools and simulations to enhance understanding and adapt teaching methods in the era of large language models.

Contribution

It offers a literature review on AI's influence on programming education and introduces pedagogical proposals emphasizing visual code comprehension and student perspectives.

Findings

Visual representations aid in understanding code

Simulations improve learning and assessment

Students support visual tools in programming education

Abstract

Computer programming is undergoing a true transformation driven by powerful new tools for automatic source code generation based on large language models. This transformation is also manifesting in introductory programming courses at universities around the world, generating an in-depth debate about how programming content should be taught, learned, and assessed in the context of generative artificial intelligence. This article aims, on the one hand, to review the most relevant studies on this issue, highlighting the advantages and disadvantages identified in the specialized literature. On the other hand, it proposes enriching teaching and learning methodologies by focusing on code comprehension and execution rather than on mere coding or program functionality. In particular, it advocates for the use of visual representations of code and visual simulations of its execution as effective tools for teaching, learning, and assessing programming, thus fostering a deeper understanding among students. Finally, the opinions of students who took the object-oriented programming course are presented to provide preliminary context supporting the incorporation of visual simulations in Java (or other languages) as part of the training process.