What does this research mean for the field?

Machine learning and hybrid approaches in AI-powered recommendation systems improve the understanding of student needs, enabling more accurate guidance that can lower dropout rates and enhance student success. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This review examines how AI-powered recommendation systems can improve university guidance for students.

June 6, 2026Open Information ScienceOpen Access

A Systematic Literature Review: Improving University Guidance with an AI-Powered Automatic Recommendation System

View Full Paper

Ask AI

Bookmark

This review examines how AI-powered recommendation systems can improve university guidance for students.
Conducted a systematic literature review following PRISMA guidelines
Analyzed 28 relevant studies from 451 articles published between 2013 and 2024
Explored recommendation system development methods and evaluation metrics
AI-powered recommendation systems enhance the understanding of student needs and behaviors
Machine learning approaches contribute to lower dropout rates and improved student success
Commonly used data types include academic, personal, demographic, and skills-related data

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Systematic review explores AI's role in enhancing student guidance, suggesting better outcomes for academic success.

Cite This Study

Sammah et al. (2026) studied this question.

synapsesocial.com/papers/6a23ba8771a5da9775e76273 https://doi.org/10.1515/opis-2025-0021

View Full Paper

Ask AI

Bookmark

A Systematic Literature Review: Improving University Guidance with an AI-Powered Automatic Recommendation System

Key Points

Discussion

Implication

Cite This Study

A Systematic Literature Review: Improving University Guidance with an AI-Powered Automatic Recommendation System

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider