Artificial intelligence: challenges and opportunities in engineering programming courses

Abstract

Artificial Intelligence (AI) is transforming programming education in undergraduate and graduate courses, as well as its application in daily life, presenting both challenges and opportunities. Instant access to vast datasets and the proliferation of AI tools—such as automatic code generators responding to natural language prompts—are reshaping traditional learning processes. On one hand, these technologies can facilitate understanding and applying complex concepts, potentially reducing problem-solving time. Tools like ChatGPT, GitHub Copilot, and other advanced systems can generate functional code from basic instructions, aiding problem-solving during hands-on programming events, collaborative projects, and academic hackathons. On the other hand, they raise questions about student autonomy and the development of critical skills in algorithmic and logical thinking. Furthermore, the widespread adoption of object-oriented programming and high-level libraries can obscure traditional procedural logic. This fosters a paradigm where solutions are often constructed by integrating existing objects or library functions (e.g., using visual block-based tools like Scratch or leveraging extensive libraries within languages like Python), rather than writing large volumes of imperative code. This essay analyzes how AI integration can complement traditional methodologies in engineering education, potentially enabling more personalized learning focused on creativity and complex problem-solving. It also examines associated risks, such as excessive reliance on automated tools and the need to adjust assessment strategies. Effectively adopting AI in programming education requires balancing technology leverage with the reinforcement of fundamental skills to ensure the development of competent and reflective professionals. This approach allows students to concentrate on the critical analysis and integration of AI-generated code. By evaluating, adapting, and optimizing AI-proposed solutions within challenging projects, students can foster a deeper understanding of fundamental programming principles.

Therefore, a didactic strategy is described for incorporating these tools, applicable in competitive programming environments like hackathons and general-purpose programming courses.