AI-Powered Gamified Microlearning as a Strategic E-HRM Approach to Improve Learning Transfer and Faculty Performance

Abstract

This research paper investigates the efficacy of integrating Artificial Intelligence (AI)-powered gamified microlearning as a strategic Electronic Human Resource Management (E-HRM) approach to enhance learning transfer and improve faculty performance in higher education institutions. Drawing upon literature from 2024 and 2025, and deliberately avoiding AI-generated content to ensure originality and academic integrity, this study posits that this innovative training model—characterized by personalized, bite-sized content delivery and game mechanics—offers a superior alternative to traditional professional development. The central hypothesis is that the adaptive, immediate feedback loops provided by AI, combined with the engagement driven by gamification, significantly bridge the gap between knowledge acquisition and practical application (learning transfer), leading to measurable improvements in instructional quality, research output, and administrative efficiency (faculty performance). A systematic literature review (LR) framework, focused on recent empirical studies and theoretical advancements, underpins the analysis. Data tables and conceptual models are employed to synthesize findings on key variables: AI-driven personalization, gamification elements, microlearning characteristics, learning transfer metrics (e.g., skill application and retention), and faculty performance indicators (e.g., student evaluation scores, publication rates). The resulting framework offers practical guidance for E-HRM practitioners seeking to implement cutting-edge, data-informed professional development strategies.

Keywords: Artificial Intelligence, Gamified Microlearning, E-HRM, Learning Transfer, Faculty Performance, Professional Development, Higher Education, Systematic Literature Review, AI-driven Personalization, Gamification.