Implementation of Artificial Intelligence (AI)-Based Recruitment Models in the Selection of Lecturers and Educational Staff at the Academy of Hospital Administration Mataram (AARS)

Abstract

The Academy of Hospital Administration Mataram (AARS) faces significant challenges in its recruitment process, which is often time-consuming and heavily reliant on the subjectivity of decision-makers, particularly during the initial screening and interview stages. A major difficulty lies in selecting candidates who align with academic and administrative needs in the absence of an integrated technological system. The implementation of Artificial Intelligence (AI) has the potential to enhance efficiency by conducting preliminary screenings, evaluating applicants’ suitability based on data, and recommending the most appropriate candidates. The aim of this study is to evaluate the potential implementation of AI-based recruitment models in the selection process of lecturers and educational staff at AARS, with a particular focus on examining AI’s role in reducing bias in recruitment. This research employed a qualitative evaluative method using a case study approach at AARS. Data were collected through participatory observation, in-depth interviews, documentation studies, and Focus Group Discussions (FGDs) to assess the effectiveness of AI in the recruitment process. Data analysis was carried out through data reduction, data display, source triangulation, and conclusion drawing. The findings reveal that AI-based recruitment at AARS significantly improves the effectiveness of the selection process for lecturers and educational staff. Furthermore, AI supports the enhancement of human resource quality because selected candidates are more aligned with organizational needs. Nevertheless, this study also identified several challenges, including infrastructure readiness, digital literacy among stakeholders, as well as regulatory and ethical considerations.