Towards the transformation of Communication Networks curriculum and assessment using Artificial Intelligence in a Higher Education setting

Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming higher education and giving rise to new approaches to teaching complex modules such as Communication Networks. Therefore, traditional lecture-based methods and fixed assessments are increasingly misaligned with the digital era’s demand for flexible, skills-driven, and technologically adept graduates. This study explored the efficacy of integrating AI into teaching, learning, and assessment for a third-year Communication Networks module, with the broader goal of enhancing student engagement, critical thinking, and problem-solving skills. A conceptual qualitative methodology was employed, drawing on contemporary literature, expert opinion, and existing AI-driven education models. Anchored on constructivist learning theory, the study prioritizes student-centred learning and the active construction of knowledge. The analysis focuses on AI’s potential to support personalised learning pathways and more responsive teaching practices. The findings indicate that Intelligent Tutoring Systems, adaptive assessments, and data-driven learning dashboards offer substantial opportunities to improve learning experiences. These tools enable personalised instruction, provide instant feedback, and automate routine grading tasks, while identifying student learning gaps early. Recommendations include integrating these AI tools into Communication Networks curricula, ensuring staff training, and establishing ongoing evaluation mechanisms. This paper contributes to the scholarship of teaching and learning by presenting a theoretically grounded and practically applicable framework for AI integration in higher education. It demonstrates how AI can bridge the gap between traditional academic approaches and the evolving demands of the workplace, ultimately preparing graduates for a technologically advanced future.

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