Knowledge Resource
Research Summary: Mapping artificial intelligence integration in higher education: a systematic review using the FACETS and SAMR frameworks
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 1 September 2026
- Last updated
- 1 October 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
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
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- Verification ID
- ASA-EXE-2026-00055
- Version
- v1.0 · r0
- Issued
- 1 September 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Mapping artificial intelligence integration in higher education: a systematic review using the FACETS and SAMR frameworks
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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