This project addresses a clear and urgent research problem situated at the intersection of technology adoption, academic labour, and equity.

First, the promise of AI efficiency is increasingly masking a reality of expanded and often invisible workload. Universities, policy frameworks, and technology providers frequently position generative AI as a means of reducing academic labour. However, emerging evidence suggests that educators undertake substantial additional work to validate outputs, redesign learning activities, manage academic integrity concerns, develop AI literacy, and maintain educational quality in AI-enabled environments (Selwyn et al., 2025; Graham et al., 2025). This contradiction between the narrative of efficiency and the reality of increased labour has significant implications for institutional decision-making, workload allocation, and resource planning.

Second, while invisible labour has long been shown to fall disproportionately on women, early-career academics, and staff in insecure forms of employment (Staudt Willet & He, 2024; Universities Australia, 2022), little is known about whether AI-related labour follows similar patterns. Existing research demonstrates that AI adoption introduces new forms of hidden work, yet there is almost no empirical evidence examining who performs this labour, how it is distributed, and whether it compounds existing inequities. As institutions continue to adopt AI technologies at scale, they are doing so with limited understanding of the potential differential impacts on their workforce.

Third, computing education represents a particularly significant and underexplored context for this research. Computing educators are among the most intensive users of AI technologies, both because AI tools are increasingly embedded within teaching practice and because AI-related technologies form part of the curriculum itself. At the same time, computing remains a discipline facing persistent EDI challenges. This combination of high AI adoption and existing structural inequities makes computing education an especially important setting in which to investigate the hidden labour consequences of AI.

Finally, there is currently no established framework for identifying and examining AI-related invisible labour within higher education. Because this work is frequently embedded within everyday routines, professional judgement, and informal practices, it remains difficult to capture using conventional workload measures. This project will address this challenge through a mixed-methods approach designed to investigate both the distribution of AI-related labour and the lived experiences of those performing it.

2.1 Research Aim

This research aims to examine how generative AI adoption is reshaping academic work in computing education, with particular attention to invisible labour, workload recognition, and workload equity. Specifically, the project investigates the forms of labour created through AI adoption, how this labour is distributed across academic and professional staff roles, and whether existing institutional systems adequately recognise and support these emerging responsibilities.