A Comparative Analysis of AI Maturity Frameworks: Toward Transformative Applications in Higher Education

Abstract

This research analyzes the usefulness of three leading Artificial Intelligence (AI) maturity frameworks: Gartner AI Maturity Model, MITRE AI Maturity Framework, and Accenture AI Maturity Framework about higher education. Gaps in technology systems, AI-enabled education, innovation, governance, administrative functions, and stakeholder engagement were examined through comparative analysis of the frameworks in the industry. These models were picked based on their expected relevance in the higher education sector and increased popularity in other industries. The methodology consisted of comparing the stages and dimensions of each framework, as well as their strengths and weaknesses in resolving specific problems of academic institutions. Following the results, an AI maturity model was suggested, which consisted of 5 stages: awareness, experimentation, adoption, integration, and transformation. The purpose of the model is to allow institutions to assess their current AI maturity levels and devise appropriate measures for effective utilization of AI in teaching, research, and administration. The model expands the scope of existing frameworks primarily focused on AI training and adopting ethical measures by providing a solution to gaps in curriculum integration and stakeholder ethics governance. With the adoption of this framework, higher education institutions can increase AI adoption and overcome issues of limited resources and ethical challenges. More exploration is necessary to test the framework across multiple educational contexts and to examine how AI can be implemented in specific regions

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