Leveraging Machine Learning in Student Peer Review: A Systematic Literature Review

This publication was part of the Technology and Innovation for Culture, Education, Language, and Society Research Group at Informatics Department Universitas Atma Jaya Yogyakarta. This article was published in Jurnal Buana Informatika, Volume 17, Number 1, April 2026, pages 76-94.

Authors: Theresia Devi Indriasari & Yohanes Sigit Purnomo W.P.

Language: English

Abstract:
Our study examines how machine learning techniques are integrated into student peer review processes, focusing on the challenges that motivate their adoption and the methods used to address them. Using Kitchenham’s systematic literature review framework, 328 articles were screened, and 25 empirical studies on machine learning applications in student peer review were selected. The findings show that machine learning is mainly used to manage large volumes of reviews, support automated grading, and improve feedback quality. Common techniques include classification, prediction, ranking, and clustering, which help improve the fairness, efficiency, and objectivity of peer review. This study provides a rigorous synthesis of machine learning adoption in student peer review and highlights its potential to enhance assessment accuracy, support learning outcomes, and guide future research and broader implementation in educational contexts.

Keywords: Automated Feedback, Machine Learning, Peer Assessment, Student Peer Review, Systematic Literature Review

DOI: 10.24002/jbi.v17i1.14753

How to Cite

If you extend or use this work, please cite the paper where it was introduced:

@article{Indriasari2026Leveraging,
	title = {Leveraging Machine Learning in Student Peer Review: A Systematic Literature Review},
	journal = {Jurnal Buana Informatika},
	volume = {17},
	number = {1},
	pages = {76--94},
	year = {2026},
	issn = {2089-7642},
	doi = {10.24002/jbi.v17i1.14753},
	url = {https://ojs.uajy.ac.id/index.php/jbi/article/view/14753},
	author = {Indriasari, Theresia Devi and Purnomo W.P., Yohanes Sigit},
	keywords = {Automated Feedback, Machine Learning, Peer Assessment, Student Peer Review, Systematic Literature Review},
	abstract = {Our study examines how machine learning techniques are integrated into student peer review processes, focusing on the challenges that motivate their adoption and the methods used to address them. Using Kitchenham's systematic literature review framework, 328 articles were screened, and 25 empirical studies on machine learning applications in student peer review were selected. The findings show that machine learning is mainly used to manage large volumes of reviews, support automated grading, and improve feedback quality. Common techniques include classification, prediction, ranking, and clustering, which help improve the fairness, efficiency, and objectivity of peer review. This study provides a rigorous synthesis of machine learning adoption in student peer review and highlights its potential to enhance assessment accuracy, support learning outcomes, and guide future research and broader implementation in educational contexts.}
}