A Systematic Review of Recommender Systems for Student Academic and Career Guidance

Hasna Mahmoud (), Mohamed Badouch, Mehdi Boutaounte, Omar Zioudi and Es-Said Boulmane
Additional contact information
Hasna Mahmoud: Ibnou Zohr University, LabSIV, Department of Computer Science, Faculty of Science
Mohamed Badouch: Ibnou Zohr University, LabSIV, Department of Computer Science, Faculty of Science
Mehdi Boutaounte: Ibnou Zohr University, National School of Commerce and Management
Omar Zioudi: Ibnou Zohr University, LabSIV, Department of Computer Science, Faculty of Science
Es-Said Boulmane: Ibnou Zohr University, LabSIV, Department of Computer Science, Faculty of Science

A chapter in Technological Innovations for Sustainable Development, 2025, pp 166-177 from Springer

Abstract: Abstract Academic and career guidance is crucial for student success. This systematic review examines recent advancements in recommender systems (RS) designed to support students’ academic and career decision-making. Following PRISMA 2020 guidelines, we analyzed 21 peer-reviewed papers published between 2020 and 2025, focusing on methodologies, effectiveness, evaluation metrics, challenges, and emerging trends. Our findings reveal a dominance of knowledge-based and hybrid approaches, leveraging machine learning techniques. While these systems demonstrate high accuracy (average 86.4%) in predicting suitable career paths and educational programs, significant challenges remain. These include integrating real-time labor market data, enhancing personalization beyond academic performance, addressing data sparsity and the cold-start problem, ensuring transparency and interpretability of recommendations, and adapting to evolving student preferences and career landscapes. Emerging trends highlight the potential of deep learning, knowledge graphs, and social media data integration. Future research should focus on enhancing model interpretability, reducing biases, and developing adaptive frameworks that align with evolving student needs and labor market demands. Advancing these systems will strengthen their role in supporting students’ academic and career decision-making.

Keywords: Recommender Systems; Career Guidance; Student Support; Academic choice; Machine Learning; Deep Learning; Hybrid Models; Personalization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-06725-8_14

Ordering information: This item can be ordered from
http://www.springer.com/9783032067258

DOI: 10.1007/978-3-032-06725-8_14

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().