Micro-credentials as a pathway to higher learning: the case of an MSc in data science and artificial intelligence degree

Micro-credentials (M-Cs) are gaining prominence as credible and flexible options that support lifelong learning. Increasing acceptance by business for capacity building is pressuring higher education institutions to recognize and integrate these certifications. Pioneered by universities like MIT and Deakin, M-Cs must yet be scaled for parity with traditional degree programs. The primary challenges to integration include non-standardized quality assurance, difficulties in credit mapping, and institutional governance barriers. This gap is critical in emerging technology fields, including data science and artificial intelligence, where industry needs outpace university curriculum accreditation, new staff appointments for course delivery, and degree completion times. Consequently, industry’s increasing use of M-Cs for skilling staff in new technologies creates a disconnect between professional and academic recognition. This study investigates this challenge by comparatively analyzing a conventional MSc in Data Science and Artificial Intelligence and industry-recognized M-Cs. Furthermore, it explores using M-Cs as a pathway toward a Master of Science degree in Data Science and Artificial Intelligence. The research compares the skills and competencies provided by M-Cs to those of the traditional degree program, identifies gaps, and proposes a mechanism for their formal recognition and integration into an MSc degree in Data Science and Artificial Intelligence.