TITLE:
The Future of Competency-Based Education and Generative-AI: Solving the Scalability-Verification Paradox through Process-Based Assessment
AUTHORS:
Elycheikh Ahmedtelba, Svetlana Mitereva, Magda Ponce
KEYWORDS:
Competency-Based Education, AI, Higher Education, Assessment, Learning Analytics, Academic Integrity, Personalized Learning, Educational Technology, Process-Based Assessment, Scalability-Verification Paradox
JOURNAL NAME:
Creative Education,
Vol.17 No.10,
October
16,
2026
ABSTRACT: Competency-Based Education (CBE) has emerged as one of the most promising models for higher education by shifting the focus from time-based progression to the demonstrated mastery of competencies. Despite its strong theoretical foundation, widespread institutional adoption has remained limited because CBE is inherently difficult to scale. Personalized learning pathways, continuous assessment, competency mapping, individualized feedback, and ongoing learner monitoring require levels of faculty engagement and administrative support that often exceed institutional capacity. Recent advances in Artificial Intelligence, however, offer unprecedented opportunities to overcome these operational barriers by enabling adaptive learning, automated competency mapping, real-time feedback, and personalized learner support at scale. The framework distinguishes the operational scalability of AI-supported CBE from the verification of the evidence used to certify competence, emphasizing that scalable learning support must be paired with proportionate human-led verification. While AI provides the technological infrastructure needed to operationalize CBE, it also introduces a critical challenge to the validity of competency assessment. As generative AI increasingly contributes to the production of student work, the connection between submitted artifacts and authentic demonstrations of independent competence becomes less certain, creating what this paper terms the Scalability-Verification Paradox. Drawing on contemporary research in competency-based education, educational technology, artificial intelligence, and assessment theory, this article proposes an integrated framework that reconciles scalable AI-supported learning with rigorous competency verification through process-based assessment.