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

created 2026-09-02 · updated 2026-09-02 · higher ed, generative ai, assessment, automated assessment, personalized learning, adaptive learning, meta analysis systematic review, educational development, equity in ai education, curriculum design

Mapping artificial intelligence integration in higher education — a PRISMA 2020 systematic review by AlSheikh et al. (2026) that screened 959 records across eight databases to map how AI is being integrated into undergraduate higher education, classifying 22 included intervention studies with the FACETS framework (Form, AI use case, Context, Education focus, Technology, SAMR) and grading their depth of educational transformation with the SAMR model (Substitution, Augmentation, Modification, Redefinition). The review finds that AI integration remains largely incremental — clustering at Substitution and Augmentation rather than transformation — that it is dominated by generative AI applied to assessment automation and personalized learning support, and that equity, ethics, academic integrity, and faculty readiness remain undertheorized.

Overview

Generative AI tools like ChatGPT are reshaping postsecondary teaching, learning, assessment, and curriculum design, yet the literature on AI integration in higher education remains fragmented and lacks structured frameworks for comparing and evaluating integration. This review addresses that gap by combining two complementary lenses: the FACETS framework (a coding schema that characterizes AI interventions by Form, AI use case, Context, Education focus, Technology, and SAMR level) to enable systematic, comparative description, and the SAMR model to gauge the extent of educational transformation each intervention affords. Rather than appraising effectiveness, the review maps the landscape — documenting what has been implemented, where evidence concentrates, and where it is missing.

Method

  • Design: A systematic review conducted per PRISMA 2020 and registered with PRISMA-P, using narrative synthesis.
  • Sources: Eight databases (PubMed, Medline, Web of Science, ProQuest, Scopus, Dimensions, OpenAlex, IEEE Xplore) were searched for English-language studies published between January 2015 and July 2025, yielding 959 records.
  • Included studies: After duplicate exclusion and title/abstract/full-text screening, 22 intervention-focused studies met the inclusion criteria, most from North America and Asia, spanning medicine, nursing, engineering, computer science, and co-curricular wellness.
  • Coding: Studies were classified with the FACETS framework and graded on the SAMR model; outcomes and ethical and equity considerations were extracted and synthesized thematically.
  • Quality appraisal: No formal risk-of-bias instrument was used, consistent with the review's mapping (rather than efficacy) aim.

Key findings

  • Generative AI dominates the landscape. Large language models — especially ChatGPT, GPT-3/4, and writing-assistance tools — were the most frequently reported platforms, while predictive analytics, LMS integrations, robotics, and Multimodal or affective computing remained peripheral.
  • Assessment automation and personalized support lead the use cases. Applications clustered around automated grading, plagiarism detection, and formative feedback, plus adaptive pathways and recommender systems for personalized learning support.
  • Integration is largely incremental, not transformative. Most studies sat at the SAMR Substitution or Augmentation level, with fewer at Modification and only one nearing Redefinition — AI was usually introduced to improve existing practices rather than reconceptualize curricula or learning outcomes.
  • Two Redefinition exemplars. Zhao et al.'s whole-curriculum biomedical-informatics course used AI as a co-creator across course design, assignments, presentations, reflection, and debate, while Jeong's embodied social robot delivered 24/7 Well Being coaching previously unfeasible at scale — the only interventions approaching genuinely novel educational practices.
  • Benefits versus challenges. Reported benefits were efficiency, personalization for learning and evaluation, engagement, and extended educator reach; challenges centered on equity, Ethics, and academic integrity, alongside risks to critical thinking and over-reliance.
  • Disciplines and geography are uneven. Medicine, nursing, engineering, and computer science led integration, with humanities, social sciences, and interdisciplinary programs scarce; research concentrated in North America, East Asia, and Europe, with minimal representation from the Global South.
  • Five documented evidence gaps. The review flags limited evidence of pedagogical transformation, under-explored equity and access implications, scarce research on faculty readiness and institutional support, inadequate ethical and governance frameworks, and a pronounced over-reliance on generative AI to the neglect of robotics, multimodal, and hybrid human–AI collaboration.

Implications

  • Embed AI as a learning partner, not a threat. Instructors can have students use an AI tutor to draft solutions or explanations, then critique and improve them — leveraging AI for routine, knowledge-based work while freeing class time for deeper discussion.
  • Redesign assessments for AI-rich contexts. Because AI can solve standard questions, assessments should emphasize critical thinking, Creativity, personal input, open-ended reflection, oral defenses, or applied projects, and may transparently incorporate AI (e.g., "use AI to draft, then document how you improved it") to turn a potential cheating tool into part of the learning process.
  • Cultivate AI literacy and ethical use. Students need to understand AI limitations — hallucination, confident-but-wrong answers, bias — and institutions should set clear guidelines distinguishing permitted from prohibited AI uses to promote honesty rather than fear.
  • Personalize at scale while keeping the human touch. AI's greatest potential lies in scaling personalization for learning and evaluation, but educators should intentionally preserve mentorship, empathy, and nuanced feedback that AI cannot replicate.
  • Invest in faculty training and institutional strategy. The review urges institutional investment in educational development — from effective prompting to pedagogical redesign — plus clear honor codes for AI, communities of practice for educators, and attention to institutional strategy, policy, and governance, not just micro-level outcomes.

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Citation

AlSheikh, M. H., Zaini, R., ALmulhem, M. A., & Ahmad, S. (2026). Mapping artificial intelligence integration in higher education: A systematic review using the FACETS and SAMR frameworks. Frontiers in Education, 11, 1871468.

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