Abstract:

This study explores the development and implementation of an academic workload model at the University of South Africa (UNISA). Using principles of fairness, transparency, and sustainability, the model was designed to equitably distribute workloads across colleges and departments. This paper evaluates the model's effectiveness and contextualizes it within broader discussions on workload frameworks in higher education. The study draws on prior research, including Kenny and Fluck’s (2014, 2023) discussions on academic workload models, Soliman and Soliman’s (1997) work on workload and quality, and Twabu and Nakene-Mginqi’s (2024) research on AI-driven workload reduction. Additionally, research by Tynan et al. (2014), Porter and Umbach (2001), Graham (2014), Cawood et al. (2020), and Crisp (2022) provides further insights into workload distribution and its impact on academic performance. The findings highlight key challenges and opportunities for workload allocation, with implications for institutional policy and strategic planning.

Keywords:

Academic workload, Higher education policy, Academic workload allocation, AI in education.