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Volume 13 | Issue 10 | Year 2026 | Article Id. IJCSE-V13I10P101 | DOI : https://doi.org/10.14445/23488387/IJCSE-V13I10P101

A Study on Integrating Artificial Intelligence (AI) and Machine Learning (ML) into Telecommunications Engineering Education to Improve Learning Outcomes for Zimbabwe State Universities

Prosper Mbire, William Dzimiri, Kennedy T. Chitiza, Lewis T. Dube, Shamiso Mabota, Kudakwashe P. Dzingirai

Received Revised Accepted Published
16 Jul 2026 19 Sep 2026 14 Oct 2026 30 Oct 2026

Citation :

Prosper Mbire, William Dzimiri, Kennedy T. Chitiza, Lewis T. Dube, Shamiso Mabota, Kudakwashe P. Dzingirai, "A Study on Integrating Artificial Intelligence (AI) and Machine Learning (ML) into Telecommunications Engineering Education to Improve Learning Outcomes for Zimbabwe State Universities," International Journal of Computer Science and Engineering, vol. 13, no. 10, pp. 1-13, 2026. Crossref, https://doi.org/10.14445/23488387/IJCSE-V13I10P101

Abstract

This study examines contextual challenges in integrating AI and ML (Artificial Intelligence and Machine Learning) into telecommunications engineering education in Zimbabwean state universities. Although there is global attention on AI/ML for improving engineering practices, graduates from Zimbabwe lack competencies in these areas because curricula are misaligned with infrastructure constraints. A mixed-methods multi-site study of 190 students, 20 faculty members and 35 industry experts was undertaken to understand experiences and perceptions of integration of AI/ML. Students were interested in AI/ML (84.2%), but only 53.2% had exposure to the concepts in formal curriculum (interest vs delivery gap); resources (69.5%), infrastructure (48.4%) and syllabi (33.7%) were challenges identified. A statistically significant association was found between study level and AI/ML awareness (χ² = 42.187, df = 4, p < .001, Cramér's V = .333) after collapsing sparse categories to satisfy Cochran's assumption. A context-sensitive framework for AI/ML integration is proposed, comprising four interconnected components: inputs, enablers, integration touch-points, and reform stages. Context-sensitive curriculum reforms and industry partnerships are required to improve educational outcomes and better prepare graduates for the changing technological landscape. This work helps us understand how to implement AI/ML in resource-poor schools and provides practical guidance for those who design curriculum and implement it.

Keywords

Artificial Intelligence (AI), Curriculum Integration, Machine Learning (ML), resource constraints, telecommunications.

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