Role of AI in modern computing curriculum

In the era of accelerated digital transformation, artificial intelligence has emerged as a strategic enabler of innovation and productivity, significantly influencing national economic growth and industrial competitiveness. As AI technologies continue to reshape organisational practices and decision-making processes across sectors, the demand for a highly skilled workforce capable of meeting future challenges grows.
Lee and Cho (2024) conducted a comprehensive analysis of AI-related degree curricula across leading universities worldwide to identify core courses offered throughout academic semesters. The analysis was based on systematically collected data from course titles, syllabi and stated learning objectives, which were refined and examined to determine prevailing curricular structures and instructional priorities.
Based on the study's findings, the authors proposed an integrated curriculum framework for AI departments that combines comprehensive computer science education with specialised AI coursework.
Considering the Importance of AI in Computing Education: Incorporating AI into the computing curriculum addresses the growing skills gap in the workforce and prepares graduates for a dynamic technological environment. A robust AI curriculum fosters interdisciplinary learning by integrating concepts from computer science, mathematics, statistics and cognitive science. This integration enables students to develop a comprehensive understanding of AI principles and applications, equipping them with the analytical and problem-solving skills essential for innovation. Furthermore, AI education supports ethical awareness and critical thinking, encouraging students to consider societal implications such as data privacy, algorithmic bias and the ethical deployment of AI systems. Thus, AI in computing curricula is not only a technical imperative but also a social responsibility.
On the other hand, current trends and challenges, as well as recent curriculum frameworks — including the Computer Science Curricula 2023 — emphasise the foundational role of computer science knowledge alongside specialised AI topics. Many institutions have introduced dedicated AI tracks or courses; however, challenges persist.
These include rapidly changing AI technologies that outpace curriculum updates, a scarcity of qualified instructors, and the need to balance AI specialisation with core computing competencies. Moreover, curriculum designers must ensure accessibility to AI education for diverse student populations by incorporating practical experiences through labs, projects and industry partnerships. This experiential learning enhances competency and readiness for professional roles (Lee & Choo, 2024).
Another key point is that an effective computing curriculum incorporating AI should adopt a layered structure: 1. Foundational Courses: Cover essential topics such as programming, data structures, algorithms and mathematics to build computational thinking. 2. Core AI Courses: Introduce machine learning, data mining, computer vision and natural language processing. 3. Advanced Specialisations: Offer elective courses in areas like reinforcement learning, AI ethics, robotics and AI system deployment. 4. Practical Experience: Integrate project-based learning, internships, and collaboration with industry to bridge theory and practice.
In summary, curriculum flexibility is critical to accommodate emerging AI trends and evolving technological demands. In addition, the increasing dependence on artificial intelligence highlights the critical need for well-designed, research-based educational programmes that provide learners with a solid foundation in computational principles alongside specialised AI skills, thereby fostering sustainable growth and maintaining a lasting competitive edge.