Balancing Innovation and Pressure: Oman Faculty’s Responses to AI Enablement, Agility and Technostress

Abstract

The enablement of AI has transformed faculty dynamics by improving their instructional efficiency, work agility, decision making agility, resource utilization and research pro-ductivity. The technostress in work environment disturbs the cognitive load and emotional resilience of the faculty leading to the mental fatigue. This proposed study aims to examine how AI enablement, work agility and technostress impact the work engagement among university faculties. A survey was conducted from 300 faculties across different age groups, qualification and work experience and it is normalized for handling missing data. The proposed Optimized Light Gradient Boosting Machine (LGBM) Multi Output Likert Explainer (OLMOLE) model classifies the faculty members based on their levels of work engagement using their experience with AI tools, agility to adapt and their technostress level. The predictive LBGM model is tuned with Optuna algorithm and interpretation is derived with the Shapely Additive Explanation (SHAP) AI method with an accuracy of 90