AI-Driven Competency-Based Education: Shaping Lifelong Learning and Skill Acquisition in Dynamic Educational Environments - Muhammad Rafiq-uz-Zaman (Artificial Intelligence in Lifelong and Life-Course Education, 2026)
Purpose – This study analyzes how Artificial Intelligence (AI) can strengthen Competency-Based Education (CBE), an approach that prioritizes demonstrated mastery over time-based progression. Since traditional models do not ensure competency attainment, this review evaluates AI’s potential to enhance personalized learning pathways, adaptive assessment mechanisms, and continuous feedback systems that support lifelong competency development. Design/methods/approach – A systematic literature review was conducted following PRISMA guidelines, examining 29 peer-reviewed Q1–Q3 journal articles focusing on AI applications in CBE, personalized learning systems, and lifelong learning models. The synthesis covers technologies such as intelligent tutoring systems, learning analytics, natural language processing, and adaptive algorithms, interpreted through the lenses of the Technology Acceptance Model and mastery learning theory. Findings – The evidence indicates that AI contributes to competency development by enabling individualized instruction, real-time formative assessment, and early detection of learning gaps. AI-supported environments promote adaptive self-regulated learning skills that are central to lifelong learning. However, empirical evidence demonstrating long-term, quantifiable learning outcomes remains limited, and many studies rely on short-term or exploratory designs. Implementation challenges continue, especially in resource-constrained contexts where infrastructure, institutional readiness, and educator expertise are insufficient. Research implications/limitations – The generalizability of findings is restricted by the methodological limitations of existing studies, including limited longitudinal evaluation and contextual validation. Further research is needed to measure sustained mastery outcomes and test AI-enhanced CBE models across diverse educational settings. Originality/value – This study proposes an AI-Driven CBE Framework that integrates competency mapping, personalized learning pathways, dynamic assessment systems, and structured lifelong learning support. It highlights the importance of AI literacy, pedagogically grounded implementation, and ethical safeguards particularly data privacy, algorithmic fairness, and equitable access to ensure responsible and sustainable AI integration in education.