The learning and teaching of assembly language, and other technical subjects, is characterized by its high level of complexity and the in-depth knowledge it demands of the internal hardware architecture. In contradistinction to high-level languages, assembly language demands that students directly engage with registers, memory, and arithmetic operations at a fundamental level, necessitating a substantial degree of detail and precision. The challenge of comprehending the processor's inner workings and the necessity to engage directly with hardware constitute substantial impediments for novices, leading to a cognitive burden that can prove demotivating and hinder learning efficacy.

In the current context, the proliferation of tools based on generative AI, designed to assist in coding, has changed the paradigm of programming learning. While these tools can provide rapid solutions, there is a concern that students may develop a reliance on them, adopting a passive approach to problem-solving, relying on generated solutions without comprehending the underlying rationale. This situation is especially worrying in the study of assembly language, where mastery of every detail is essential for training professionals capable of optimizing performance and energy consumption in embedded systems.

In this context, there is a necessity to develop innovative teaching methodologies that facilitate the assimilation of complex concepts, foster deep understanding, and avoid dependence on automated solutions. Gamification is presented as a powerful tool in this regard, as it introduces playful and competitive dynamics into the educational process, promoting both individual learning and collaboration among students. The integration of challenges that necessitate assembly code optimization and critical reasoning to circumvent automated solutions serves to enhance active engagement, thereby fostering the development of analytical capabilities that are indispensable for low-level programming.

This work aligns with the paradigm of educational research and practice, proposing a teaching experience that is founded on Challenge-Based Learning (CBL) and the incorporation of gamification elements. The initiative's overarching objective is twofold: firstly, to enhance code performance in contexts where every instruction is crucial, and secondly, to cultivate deep, autonomous learning that challenges the tendency to rely on AI-generated solutions without a thorough understanding of their limitations, through Gamification and Resilience to Generative AI Dependency. The implementation of a digital platform, combining interactive forums, reward systems, and data analysis, creates a learning environment that responds to the demands of the digital age and prepares students for the challenges of the future.

In conclusion, this work invites us to rethink the way we approach training processes in complex areas such as assembly programming, proposing a methodology that combines the best of challenge-based learning and gamification, and that adapts to the new technological dynamics imposed by the artificial intelligence revolution. We hope that this experience will serve as a model for future educational initiatives and contribute to the transformation of teaching in highly technically demanding disciplines, positioning itself as a benchmark in educational innovation for embedded systems and beyond.

Keywords: Resilience to AI Dependency, Challenge-Based Learning, CBL, Gamification, Code Optimization, Low-Level Programming.