The rise in artificial intelligence (AI) tools in education presents exciting opportunities but also challenges, for both students and educators. Particularly in the field of computer science, Large Language Models (LLMs) are becoming increasingly accessible and effective in completing unsupervised tasks (i.e., take-home assignments, problem sets, etc.) and supervised tasks (i.e., labs, quizzes, exams, etc.) assigned to post-secondary students. This alarming trend raises questions about the role of educators in higher education and institutions in relation to regulating generative AI (GenAI) tools for course work.

Through a literature review of 40 sources, this study provides a comprehensive overview on the transient state of AI within computing education. The resources were categorized based on common themes such as the effectiveness of AI in coding, debugging, and refactoring, as well as educator training, policies, and plagiarism detection tools. These sources were then analyzed based on the collective findings regarding their respective focuses. Subsequently, the study explores how educators can adapt their teaching strategies to navigate today’s educational landscape, more specifically how they can act as facilitators rather than relaying traditional teacher-centred approaches in teaching computer science courses. Educators need to explore ways to effectively incorporate GenAI tools such as GitHub’s Copilot, OpenAI’s ChatGPT, Google’s Bard, and DeepSeek into computer science course syllabi. They must also consider how to reframe course syllabi and learning objectives to be more technology-forward. Additionally, educators should be aware of the precautions necessary when integrating GenAI tools into the curriculum, given the dynamic nature of these technologies.

Furthermore, the study explores how post-secondary institutions can uphold academic integrity through all-encompassing policies and clear guidelines on appropriate usage. By addressing these challenges, institutions will be able to retain a nourishing environment for students to thrive and critically utilize the GenAI as a supplementary tool for both supervised and unsupervised course work.

The synergy between responsibly integrating AI tools in the post-secondary computer science curriculum provides a viable pathway for students to prepare for an AI-powered workforce and thoughtful integration of GenAI tools should serve as a new baseline for upper-year computer science courses.

Keywords: Generative AI, Computer Science Education, Large Language Models, Higher Education, Academic Integrity, Teaching Strategies, Curriculum Development.