Efficiency or intensification? A systematic review of generative AI and academic workload in higher education

The rapid emergence of generative artificial intelligence (GenAI) in higher education has prompted critical discussions regarding its impact on faculty workload. Existing evidence is fragmented and lacks systematic synthesis, leaving it unclear whether GenAI alleviates or intensifies academic work. To address this gap, this study conducted a systematic review of 30 empirical studies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the methodological quality of included studies assessed using the Mixed Methods Appraisal Tool (MMAT). Findings reveal that GenAI's impact on faculty workload is not a simple increase or decrease but represents a workload transformation. It improves efficiency in routine and procedural tasks (e.g., teaching material preparation, basic grading), while simultaneously introducing new professional responsibilities (e.g., quality review of AI outputs, ethical judgement, academic integrity monitoring) that may increase workload in specific areas. The direction and extent of workload change depend on both individual and organisational conditions. At the individual level, digital literacy, teaching experience, and AI judgement ability shape faculty members' capacity to use GenAI effectively. At the organisational level, policy clarity, systematic training, ethical guidance, and reliable technical infrastructure determine whether GenAI functions as a work resource or becomes an additional burden. These findings highlight the conditional and context-dependent nature of GenAI's effects, providing actionable guidance for higher education institutions to integrate AI technologies effectively while supporting faculty development and maintaining educational standards.