Mapping artificial intelligence integration in higher education: a systematic review using the FACETS and SAMR frameworks

A systematic review is underway to map the integration of Artificial Intelligence (AI) into higher education, addressing the fragmented nature of existing literature. The review aims to classify AI applications using the SAMR model (Substitution, Augmentation, Modification, Redefinition) and analyze reported outcomes and challenges. It also intends to apply the FACETS framework to enhance comparability across studies.

Why it matters

The systematic analysis of AI integration in higher education is crucial for understanding its transformative potential and managing its implications across various sectors. This research provides a foundational framework for evaluating AI's impact on educational models, workforce development, and organizational strategy, informing decisions on technology adoption and skill development at an institutional level.

Key insights

• Artificial intelligence (AI) is significantly influencing higher education across various domains including teaching, learning, assessment, and curriculum design.
• Current literature on AI integration in higher education is fragmented, indicating a lack of structured frameworks for evaluation.
• The review seeks to systematically map AI integration, classify applications using the SAMR model, and analyze associated outcomes and challenges.
• The FACETS framework (Form, AI use case, Context, Education focus, Technology, SAMR) will be applied to improve the comparability of findings across different studies.
• The methodology adheres to PRISMA 2020 guidelines and is registered via the PRISMA-P protocol, involving searches across eight databases.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1871468