Rewriting the Curriculum: A Systematic Review of Generative AI-Driven Pedagogical Change and Emerging Systems of Learning in Higher Education

Synthesis: Sabani et al. (2026) examine how Generative AI is driving a more profound transformation of Higher Education pedagogy and curriculum beyond Writing support, proposing an AI-Augmented Learning System framework that treats GenAI as a catalyst for curricular reconfiguration. Using a triangulated mixed-methods design — a scoping review, bibliometric mapping (VOSviewer, n=209), a systematic review of 36 peer-reviewed articles (2023–2025), and ten interviews with academic leaders across five institutions in Australia and Indonesia — they identify five interrelated system shifts: from static to dynamic AI-integrated curricula, teacher-centered to AI-augmented facilitation, knowledge transmission to capability development, local experimentation to institutional AI Governance, and fragmented to ecosystemic integration. Interpreted through constructivism, connectivism, Technological Pedagogical Content Knowledge (TPACK), the SAMR model and constructive alignment, the framework is offered as an analytical heuristic rather than a prescriptive blueprint.

Key Findings

Five system shifts. GenAI reconfigures higher education along five interrelated axes: (1) static curricula → dynamic AI-integrated design; (2) teacher-centered delivery → AI-augmented facilitation; (3) knowledge transmission → capability development; (4) local experimentation → institutional governance; and (5) fragmented implementations → ecosystemic integration. These shifts move GenAI from a supplementary writing/tutoring tool to a structural force reshaping curriculum and the "foundational structures of learning itself."

AI-Augmented Learning System framework. The framework conceptualizes GenAI not merely as an instructional tool but as a catalyst for curricular and pedagogical reconfiguration, giving structure to the otherwise diffuse discourse on Generative AI in Higher Education. It positions learners as more active participants — co-designers of learning within AI-integrated environments — and reframes the institution's role from running isolated experiments to governing an integrated system.

Mixed-methods triangulation. The evidence base combines a scoping review, VOSviewer bibliometric mapping of 209 records, a PRISMA-style systematic review of 36 peer-reviewed articles (2023–2025), and ten semi-structured interviews with academic leaders and educators at five institutions across Australia and Indonesia.

Theoretical anchoring. The five shifts are mapped onto established lenses — constructivism, connectivism, Technological Pedagogical Content Knowledge (TPACK), SAMR, and constructive alignment — clarifying the pedagogical mechanisms through which GenAI reshapes curriculum and teaching, with implications for Learning Analytics and educational innovation.

Caveat. Given the bounded empirical base, the authors position the framework as a starting point for institutional dialogue pending further empirical validation across diverse contexts.

What this means for practice

• Administrators. Replace isolated GenAI pilots with governed, institution-wide integration: the analysis identifies the shift from fragmented experimentation to institutional AI Governance as one of five system-level changes.
• Curriculum designers. Rebuild programs around dynamic, AI-integrated Curriculum Design and capability development rather than adding AI to existing content, and align intended outcomes, learning activities, and assessment through constructive alignment.
• Faculty developers. Prepare staff for AI-augmented facilitation, in which the teacher's role moves from content delivery to designing and steering AI-supported learning.
• Administrators. Use the AI-Augmented Learning System framework to structure institutional dialogue, not as a ready-made blueprint, and validate each of the five shifts against local evidence before acting on it.

Limitations

• The practitioner evidence rests on only ten semi-structured interviews, purposively sampled from five institutions in two countries (Australia and Indonesia), and the authors position the framework as an analytical heuristic awaiting empirical validation across diverse higher education contexts.
• The formal bibliometric corpus and PRISMA-guided review drew on a single database (Scopus, n = 209 records); Web of Science and ProQuest were consulted only in the scoping phase, whose 310 records were not merged into the corpus.
• The systematic review synthesized 36 English-language peer-reviewed articles published 2023–2025, a narrow window in a rapidly moving field.
• Interviews lasted 45–75 minutes and captured self-reported pedagogical change; the interview phase triangulated the literature findings rather than testing the framework against student or institutional outcomes.

Citation

Sabani, A., Farah, M. H., Catyanadika, P. E., Dewi, D. R. S., & Tawani, V. (2026). Rewriting the Curriculum: A Systematic Review of Generative AI-Driven Pedagogical Change and Emerging Systems of Learning in Higher Education. Computers and Education: Artificial Intelligence, 100667. https://doi.org/10.1016/j.caeai.2026.100667