The Role of Artificial Intelligence in Enhancing Competency-Based Learning: A Systematic Literature Review

Abstract

This systematic literature review examined the role of Artificial Intelligence (AI) in enhancing Competency-Based Learning (CBL) by synthesizing evidence from 46 peer-reviewed studies published in reputable international journals and conference proceedings. The review followed the PRISMA protocol across the identification, screening, eligibility, and inclusion stages, and applied a quality-assessment instrument before extracting and synthesizing data. The findings indicated that adaptive learning platforms, intelligent tutoring systems, learning analytics, machine learning models, recommendation systems, and generative AI were the technologies most frequently applied to support competency development. The synthesized evidence showed that AI improved competency achievement, personalized learning pathways, learner engagement, and assessment efficiency, while also raising challenges related to data privacy, algorithmic transparency, infrastructure cost, teacher readiness, and equity. Reported implementation models emphasized learner modeling, continuous formative feedback, and analytics-informed instructional design. The review identified persistent gaps in longitudinal evidence, standardized competency frameworks, and rigorous experimental designs. The study concluded with evidence-based recommendations for educators, institutions, and policymakers seeking to integrate AI into competency-based education.