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Cited by 91Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningStudent Assessment and Feedback

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Unraveling the mechanisms and effectiveness of AI-assisted feedback in education: A systematic literature review

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Ba, Shen, Yang, Lan, Yan, Zi, Looi, Chee Kit, Gašević, Dragan·2025·Computers and Education Open
DOI 10.1016/j.caeo.2025.100284
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Abstract

Rapid advancements in Artificial Intelligence (AI) have prompted growing interest in leveraging AI for educational feedback. Yet, the centrality of the learner in this process is often overshadowed by technological excitement, and a broad understanding of AI-assisted feedback (AIFB) in education remains evolving. To address this gap, we conducted a systematic review of 129 peer-reviewed journal articles (2014–2023) based on widely used AI-related search terms to examine how AI, especially generative AI, supports feedback mechanisms and influences learner perceptions, actions, and outcomes. Our analysis identified a sharp rise in AIFB research after 2018, driven by modern large language models. We found that AI tools flexibly cater to multiple feedback foci (task, process, self-regulation, and self) and complexity levels (basic, intermediate, and elaborated). Our findings demonstrate that AIFB can effectively enhance targeted learning outcomes. By employing a transparent and field-aligned methodology, we synthesized recent advances and offers actionable insights for both research and practice. While the focus on widely recognized AI-related search terms ensures strong comparability and relevance, some specialized subfields (e.g., Automated Writing Evaluation), are less prominent in this synthesis. The study also highlights the ongoing need for clearer reporting of underlying AI algorithms. Building on these findings, we propose an original conceptual model that synthesizes current progress and offers a roadmap for future explorations. By illuminating the affordances and constraints of AIFB, we highlight the necessity for transparent methodological reporting and underscores the importance of integrating pedagogical and technological insights to promote meaningful, learner-centered feedback.

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Cited by

Sorted by citations

Metadata

Authors
Ba, S., Yang, L., Yan, Z., Looi, C. K., & Gašević, D.
Affiliations
  • Education University of Hong Kong (HK)
  • Monash University (AU)
Corresponding author
Yang, Lan
ORCID
Venue
Computers and Education Open
Publisher
Elsevier BV
Published
2025-12
Volume
9
Pages
100284
ISSN
2666-5573 (ISSN-L)
DOI
10.1016/j.caeo.2025.100284
OpenAlex
W4413478951
Cited by
91
References
43
Citations by year
2026: 85 · 2025: 6
FWCI
9.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Education Open (publishedVersion, cc-by)
  • Monash University Research Portal (Monash University) (submittedVersion, cc-by)
  • HAL (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Education University of Hong Kong
  • The Education University of Hong Kong (T-24-07, 04377, 02189)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
AI-generated feedback, generative AI, large language models, automated feedback, student-centered feedback, student self-regulation, learner perceptions, learning outcomes, automated writing evaluation

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