Leveraging AI for Higher Education Human Resources:A Systematic Literature Review

Abstract

The integration of Artificial Intelligence (AI) in Human Resource Development (HRD) policies for academic staff in Higher Education Institutions (HEIs) is often constrained by the misalignment between computational capabilities and institutional governance. While HEIs generate substantial datasets through the Tri Dharma (the three pillars of higher education: teaching, research, and community service), this data remains underutilized for proactive policy-making. To map the state-of-the-art and identify existing research gaps, this study conducts a Systematic Literature Review (SLR) and bibliometric analysis of 400 unique peer-reviewed documents, obtained from an initial 485 records retrieved from Scopus (n = 260) and Web of Science (n = 225) after removing 85 duplicates, covering the seven-year period from 2018 to 2024. Visual analysis using VOSviewer reveals a distinct semantic-policy discrepancy, showing that semantic technologies and predictive AI models are researched as isolated technical domains, seldom integrated into comprehensive institutional governance. To bridge this gap, this paper proposes a framework for Higher Education Human Resources for Lecturer Career Development. The proposed framework integrates machine learning classifiers (Naïve Bayes, K-Means, and Apriori association) with a semantic Knowledge Graph layer to transform descriptive academic metrics into explainable, policy-conforming recommendations. Preliminary expert consultation with LLDIKTI Region II highlights the conceptual viability of the Framework for Higher Education Human Resources for Lecturer Career Development in mitigating administrative bottlenecks, supporting objective career path design, and minimizing human subjectivity in lecturer performance evaluations.