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A Systematic Literature Review of Automated Feedback Generation in Education

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Title: A Systematic Literature Review of Automated Feedback Generation in Education
Language: English
Authors: Yajie Song (ORCID 0009-0007-5910-6319), Yimei Zhang (ORCID 0000-0002-4955-6726), Maria Cutumisu (ORCID 0000-0003-2475-9647)
Source: International Journal of Technology in Education. 2026 9(2):512-556.
Availability: International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijteoffice@gmail.com; Web site: https://www.ijte.net/index.php/ijte/about
Peer Reviewed: Y
Page Count: 45
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Descriptors: Literature Reviews, Automation, Feedback (Response), Artificial Intelligence, Error Correction, Technology Uses in Education, Educational Technology, Educational Environment, Ethics, Context Effect, Student Evaluation
ISSN: 2689-2758
Abstract: Feedback that is individualized and immediate is essential to improving learning outcomes but providing it to every learner is difficult. Automatic feedback generation (AFG) aims to alleviate this problem, especially with technology-enhanced learning environments. This systematic literature review of AFG in education, following the PRISMA framework, examines 34 peer-reviewed publications. The findings revealed that the reviewed studies (1) gained momentum after 2019; (2) often used secondary cognitive data to evaluate AFG approaches; (3) mainly targeted computer science domain; (4) frequently combined multiple methods to generate feedback; (5) employed multiple performance evaluations; and (6) mostly provided written feedback aimed at correcting student errors. This review also highlighted several gaps, including the lack of (1) in-depth cognitive and affective data from user studies to evaluate feedback and understand how students interpret it; (2) research on feedback use and strategies to close feedback loop; (3) AFG systems for ill-defined domains with strong transferability; (4) elaborated feedback that scaffolds problem-solving rather than giving answers; (5) feedback using multiple modalities and valences; and (6) integration of learning theories in AFG design. This review advances understanding of current AFG practices, evaluates and extends conceptual frameworks of AFG, and provides insights for future AFG design and evaluation.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1506278
Database: ERIC
FullText Text:
  Availability: 0
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  Data: A Systematic Literature Review of Automated Feedback Generation in Education
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Yajie+Song%22">Yajie Song</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0007-5910-6319">0009-0007-5910-6319</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yimei+Zhang%22">Yimei Zhang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4955-6726">0000-0002-4955-6726</externalLink>)<br /><searchLink fieldCode="AR" term="%22Maria+Cutumisu%22">Maria Cutumisu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2475-9647">0000-0003-2475-9647</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Technology+in+Education%22"><i>International Journal of Technology in Education</i></searchLink>. 2026 9(2):512-556.
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  Data: International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijteoffice@gmail.com; Web site: https://www.ijte.net/index.php/ijte/about
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  Data: Y
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  Data: 45
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  Data: 2026
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  Data: Journal Articles<br />Information Analyses
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  Data: <searchLink fieldCode="DE" term="%22Literature+Reviews%22">Literature Reviews</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Error+Correction%22">Error Correction</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Environment%22">Educational Environment</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Context+Effect%22">Context Effect</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Evaluation%22">Student Evaluation</searchLink>
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  Data: 2689-2758
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Feedback that is individualized and immediate is essential to improving learning outcomes but providing it to every learner is difficult. Automatic feedback generation (AFG) aims to alleviate this problem, especially with technology-enhanced learning environments. This systematic literature review of AFG in education, following the PRISMA framework, examines 34 peer-reviewed publications. The findings revealed that the reviewed studies (1) gained momentum after 2019; (2) often used secondary cognitive data to evaluate AFG approaches; (3) mainly targeted computer science domain; (4) frequently combined multiple methods to generate feedback; (5) employed multiple performance evaluations; and (6) mostly provided written feedback aimed at correcting student errors. This review also highlighted several gaps, including the lack of (1) in-depth cognitive and affective data from user studies to evaluate feedback and understand how students interpret it; (2) research on feedback use and strategies to close feedback loop; (3) AFG systems for ill-defined domains with strong transferability; (4) elaborated feedback that scaffolds problem-solving rather than giving answers; (5) feedback using multiple modalities and valences; and (6) integration of learning theories in AFG design. This review advances understanding of current AFG practices, evaluates and extends conceptual frameworks of AFG, and provides insights for future AFG design and evaluation.
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  Data: 2026
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      – Text: English
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        PageCount: 45
        StartPage: 512
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      – SubjectFull: Literature Reviews
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Feedback (Response)
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      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Error Correction
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Educational Technology
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      – SubjectFull: Educational Environment
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      – SubjectFull: Ethics
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      – SubjectFull: Context Effect
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      – SubjectFull: Student Evaluation
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