Impact of the use of artificial intelligence tools on the development of computational thinking in systems engineering students

Abstract

Artificial intelligence (AI) has become a key component in teaching and learning processes in higher education, particularly in programs such as Systems Engineering, where computational thinking is a core competence. Tools such as ChatGPT, Copilot, and Gemini are increasingly used in academic activities related to programming, problem solving, and algorithmic analysis, generating new pedagogical and cognitive dynamics. Computational thinking, understood as the ability to decompose problems, abstract relevant information, recognize patterns, and design algorithms, is essential for engineering education, as it supports structured reasoning and the understanding of complex systems. Previous research suggests that AI-based tools can facilitate the comprehension of abstract concepts, optimize coding processes, and provide personalized feedback, contributing to improved academic performance. Nevertheless, the literature also highlights potential risks associated with excessive or unguided use of AI, including technological dependence, reduced cognitive effort, and superficial learning. In the Latin American context, empirical evidence on this relationship remains scarce, especially in advanced university programs. Therefore, this study adopts a quantitative, non-experimental, and correlational approach to analyze the impact of academic AI tool usage on the development of computational thinking among Systems Engineering students. The expected findings aim to provide empirical evidence to support curricular and pedagogical decision-making, fostering an ethical, responsible, and educationally meaningful integration of artificial intelligence in higher education.