Artificial Intelligence Use in Nursing Education and Its Impact on Faculty Time Management, Task Efficiency, And Productivity

This study examined the use of Artificial Intelligence (AI) in Nursing Education and its impact on time management, task efficiency, and productivity among faculty members at the University of Saint Anthony (USANT) College of Health Care Education (CHCE). A descriptive survey was utilized, with the entire population being 46 USANT CHCE faculty members. The instrument to collect data was distributed through Google Forms, measuring the following: profiles of the respondents, initial conditions of the respondents' familiarity regarding AI, preferred tools, frequency of use, impact of AI use on faculty demands, workload management, and educational outcomes, as well as perceived challenges of using AI. The demographic profile shows that the employed participants in this study are predominantly younger to middle-aged (43.5% were aged 30-39), heavily female (73.9%), and mostly entry-level junior instructors (69.6%) who work for a long period of time per week (28.3% of whom worked over 50 hours/week). There is a moderate level of familiarity with AI (45.7%), and regular use of AI solutions is not common enough (6.5%), with the most typical AI tools being text-based chatbots (65.2%) and writing assistants (54.3%). With respect to operational impact, it was found that there is a strong agreement that AI highly optimizes the ability to complete tasks and faculty demands productivity (WM = 3.83) and workload (WM = 3.78). Its constituent ratings showed the strongest agreement in educational outcomes (WM = 3.91), and the greater the increase in the efficiency of the instructors, the more effective the mentorship for the students (WM = 4.02). Full technological integration, however, is heavily challenged by systemic barriers (WM = 3.88) fueled by fluctuating institutional internet, moral and ethical issues of academic honesty, absence of formal technological training, and critical mismatch of contexts when it comes to AI-generated outputs not having a sense of locality or clinical and regulatory authenticity in the Philippines. The study indicates that while AI can effectively streamline repetitive teaching tasks and manage instruction schedules, the indiscriminate use of AI poses a potential risk to educational integrity and the quality of teaching and learning. It shifts faculty workloads, but does not necessarily decrease them. The institution is encouraged to evolve from the current piecemeal approach to a more systematic one through creating tiered professional development training programs, improving its digitization infrastructure, establishing clear ethical guidelines for the use of AI, adjusting evaluation methods to remain aligned with Critical Thinking, and localizing AI content to strictly adhere to the Philippine healthcare standards.