AUTOMASI PENGELOLAAN BEBAN KERJA DOSEN DI PERGURUAN TINGGI SWASTA (PTS) SECARA EFEKTIF DENGAN AI

Abstract

This study aims to explore the potential utilization of Artificial Intelligence (AI) in managing the workload of lecturers at Private Higher Education Institutions (PTS). The background of this research stems from the high administrative, academic, and research workload faced by lecturers at PTS due to limited infrastructure and resources (Rakhmani & Siregar, 2016; Priyono, 2018). Using a mixed-methods approach, this research involved the distribution of pre-simulation (n = 47) and post-simulation (n = 48) questionnaires, along with interviews with 15 lecturers from three PTS. The AI implementation simulation focused on automated grading and class schedule management. The results of the study show a significant reduction in administrative workload from the "very high" category (M = 4.2/84%) to "moderate" (M = 3.2/64%) post-simulation. Lecturers' technology literacy increased from "moderate" (M = 3.3/66%) to "high" (M = 3.7/74%), while the perception of AI effectiveness reached a "very high" category (M = 4.3/86%). These findings align with previous studies emphasizing AI's role in enhancing lecturer productivity through time efficiency and reducing administrative workload (Gupta & Kumar, 2024; Namutebi, 2024; Aithal & Aithal, 2023). However, challenges such as limited funding, infrastructure, and technology literacy remain barriers to implementation (Nair et al., 2024). Therefore, this study concludes that AI has significant potential to support academic productivity at PTS, provided there is institutional policy support, enhanced digital literacy, and sustainable infrastructure development.