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Qualitative Study on the Integration of AI-Powered Peer Review Systems in Learning Management Systems

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Title: Qualitative Study on the Integration of AI-Powered Peer Review Systems in Learning Management Systems
Language: English
Authors: Pravitha Vijaykumar (ORCID 0009-0000-8550-2197), Madhumita Das (ORCID 0009-0008-8165-5389), Mamata Bhandar (ORCID 0000-0002-6178-449X)
Source: Journal of Educators Online. 2026 23(1).
Availability: Journal of Educators Online. Grand Canyon University, 23300 West Camelback Road, Phoenix, AZ 85017. e-mail: CIRT@gcu.edu. Web site: https://www.thejeo.com
Peer Reviewed: Y
Page Count: 17
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Adult Education
Higher Education
Postsecondary Education
Descriptors: Peer Evaluation, Artificial Intelligence, Technology Uses in Education, Learning Management Systems, Foreign Countries, Adult Learning, Higher Education, Electronic Learning, Feedback (Response), Assignments, Student Attitudes, Doctoral Programs
Geographic Terms: Malaysia
ISSN: 1547-500X
Abstract: The rapid development of AI technologies could add a new dimension to the peer review process in a learning management system (LMS) platform. Peer review is essential to many assessment components, particularly in the LMS but the traditional peer review process has limitations, including the quality of the feedback provided by the peer, the peer's knowledge of the topic, and grammatical errors in the written review. These, in turn, may affect the peer learner's performance in an academic setting. AI technologies are increasingly being utilized in the peer review process in education, notably in research. This qualitative study examines the perceptions of students regarding the peer review process in one or more modules of their doctoral program of study at a Malaysian university, with a focus on enhancing their learning process, discussions, and the pedagogical impact that these AI technologies may have on their learning outcomes. The findings reveal diverse perspectives on the usability, accessibility, ease of use, effectiveness, and insights on the different features that an AI-powered peer review system could incorporate. The findings further highlight both the potential to enhance feedback quality and critical thinking and the challenges related to technical implementation, reliability, and ethical considerations. This study sets the groundwork for further research that is urgently needed to identify optimal ways of integrating AI technologies into the LMS, thereby providing practical guidelines for educational institutions and technology developers to use AI ethically to improve the learning outcomes of students. The findings can also assist LMS and AI solution providers to create a list of features learners expect to be available in the tool.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499033
Database: ERIC
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  Data: Qualitative Study on the Integration of AI-Powered Peer Review Systems in Learning Management Systems
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  Data: <searchLink fieldCode="AR" term="%22Pravitha+Vijaykumar%22">Pravitha Vijaykumar</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-8550-2197">0009-0000-8550-2197</externalLink>)<br /><searchLink fieldCode="AR" term="%22Madhumita+Das%22">Madhumita Das</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0008-8165-5389">0009-0008-8165-5389</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mamata+Bhandar%22">Mamata Bhandar</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6178-449X">0000-0002-6178-449X</externalLink>)
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  Data: Journal of Educators Online. Grand Canyon University, 23300 West Camelback Road, Phoenix, AZ 85017. e-mail: CIRT@gcu.edu. Web site: https://www.thejeo.com
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  Data: The rapid development of AI technologies could add a new dimension to the peer review process in a learning management system (LMS) platform. Peer review is essential to many assessment components, particularly in the LMS but the traditional peer review process has limitations, including the quality of the feedback provided by the peer, the peer's knowledge of the topic, and grammatical errors in the written review. These, in turn, may affect the peer learner's performance in an academic setting. AI technologies are increasingly being utilized in the peer review process in education, notably in research. This qualitative study examines the perceptions of students regarding the peer review process in one or more modules of their doctoral program of study at a Malaysian university, with a focus on enhancing their learning process, discussions, and the pedagogical impact that these AI technologies may have on their learning outcomes. The findings reveal diverse perspectives on the usability, accessibility, ease of use, effectiveness, and insights on the different features that an AI-powered peer review system could incorporate. The findings further highlight both the potential to enhance feedback quality and critical thinking and the challenges related to technical implementation, reliability, and ethical considerations. This study sets the groundwork for further research that is urgently needed to identify optimal ways of integrating AI technologies into the LMS, thereby providing practical guidelines for educational institutions and technology developers to use AI ethically to improve the learning outcomes of students. The findings can also assist LMS and AI solution providers to create a list of features learners expect to be available in the tool.
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
    Subjects:
      – SubjectFull: Peer Evaluation
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Learning Management Systems
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Adult Learning
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: Electronic Learning
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      – SubjectFull: Feedback (Response)
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      – SubjectFull: Assignments
        Type: general
      – SubjectFull: Student Attitudes
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      – SubjectFull: Doctoral Programs
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      – SubjectFull: Malaysia
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    Titles:
      – TitleFull: Qualitative Study on the Integration of AI-Powered Peer Review Systems in Learning Management Systems
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