Aprofundamento cognitivo vs. aprendizagem superficial: uma análise crítica do impacto da IA generativa no ensino de programação

Abstract

The rise of Generative Artificial Intelligence (AI) represents one of the most significant disruptions in the contemporary educational landscape, especially in programming education. This final undergraduate paper investigates the central dichotomy that emerges from this integration: does AI act as a potentiation tool, promoting cognitive deepening, or as a technological "crutch" that encourages superficial learning? Through a systematic review of recent academic literature, this study critically analyzes the two main schools of thought on the subject. The methodology adopted search criteria in academic databases, focusing on publications between 2023 and 2025, to map the arguments, debates, and research gaps. The analysis reveals that one perspective advocates for the benefits of AI in improving performance, developing higher-order skills, and strengthening student motivation. Conversely, another perspective warns of the risks of the "crutch effect," the erosion of fundamental skills such as code debugging, and challenges to academic integrity. As its main contribution, this work proposes a dialectical synthesis for this tension, arguing that the impact of AI is not inherent to the technology but is mediated by pedagogical practice. To this end, a propositional framework for AI integration at three levels: Safe Adoption, Critical Collaboration, and Assisted Innovation, is presented, calibrated according to the student's level of expertise. It is concluded that, with an intentional and critical pedagogical approach, it is possible to mitigate the risks and transform Generative AI into a powerful ally for cognitive deepening.