Generative AI in Computer Engineering

This academic practice guide provides a curriculum-wide framework for the responsible use of generative artificial intelligence in Computer Engineering(CNG) education and research. It is written specifically for CNG students, teaching assistants, supervisors, lecturers, and academic programme teams who need practical guidance on using AI tools without compromising technical understanding, verification, academic integrity, or reproducibility. The document covers the use of generative AI across major Computer Engineering areas, including programming, algorithms, mathematics, digital logic, computer architecture, embedded systems, operating systems, databases, networks, cybersecurity, artificial intelligence, machine learning, data science, software engineering, capstone projects, thesis work, and technical communication. It emphasises that AI-generated outputs must be treated as candidate artefacts requiring validation through compilers, tests, simulations, synthesis tools, benchmarks, proof reconstruction, verified sources, and human engineering judgement. The guide also introduces responsible workflows, assessment considerations, disclosure practices, reproducibility expectations, thesis and research directions, prompt templates, and the ACCEPT framework for accountable AI use in CNG academic contexts.