Year: 2026
Type: review
Study design: Systematic Review
Abstract: Academic advising is fundamental to student success, yet the rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming the delivery of academic support. While predictive models and Recommendation Systems (RS) are becoming more accessible, the existing literature remains fragmented across diverse technical architectures and institutional objectives, preventing a clear understanding of the field’s evolution. In view of this, we present a Systematic Literature Review (SLR) of AI-driven academic advising, adhering to the PRISMA 2020 framework. We analyzed 27 peer-reviewed studies published between 2018 and 2025 to synthesize methodological trends and functional applications. Our findings reveal that while most systems prioritize pathway recommendations via classical ML or hybrid architectures, Early-Warning Systems (EWS) remain anchored in predictive classification. Furthermore, a nascent shift toward Generative AI (GenAI) indicates a move toward more interactive advising, though transparency and evaluation standards remain inconsistent. This review identifies a critical tension between algorithmic performance and institutional interpretability. We conclude by proposing a research agenda that emphasizes the need for cross-context validation and the development of socio-technical frameworks that integrate AI into existing higher education management structures.
Source: Information
DOI: 10.3390/info17040335
Authors: Ilyas Alloug, Mohamed Daoudi, Ilham Oumaira
Institution: Université Ibn-Tofail
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7147345548