View in EDS

Empowering Instructors with AI: Evaluating the Impact of an AI-Driven Feedback Tool in Learning Analytics

Saved in:
Bibliographic Details
Title: Empowering Instructors with AI: Evaluating the Impact of an AI-Driven Feedback Tool in Learning Analytics
Language: English
Authors: Cleon Xavier (ORCID 0000-0002-7617-5283), Luiz Rodrigues (ORCID 0000-0003-0343-3701), Newarney Costa (ORCID 0000-0002-4954-176X), Rodrigues Neto, Gabriel Alves (ORCID 0000-0002-2249-7818), Taciana Pontual Falcao (ORCID 0000-0003-2775-4913), Dragan Gasevic (ORCID 0000-0001-9265-1908), Rafael Ferreira Mello (ORCID 0000-0003-3548-9670)
Source: IEEE Transactions on Learning Technologies. 2025 18:498-512.
Availability: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Peer Reviewed: Y
Page Count: 15
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Teacher Empowerment, Learning Analytics, Artificial Intelligence, Computer Software, Technology Integration, Time Management, Learning Management Systems, Feedback (Response), Student Evaluation, Computational Linguistics, Efficiency, Educational Quality, Teacher Attitudes, College Faculty
DOI: 10.1109/TLT.2025.3562379
ISSN: 1939-1382
Abstract: Providing timely and personalized feedback on open-ended student responses is a challenge in education due to the increased workloads and time constraints educators face. While existing research has explored how learning analytic approaches can support feedback provision, previous studies have not sufficiently investigated educators' perspectives of how these strategies affect the assessment process. This article reports on the findings of a study that aimed to evaluate the impact of an artificial intelligence (AI)-driven platform designed to assist educators in the assessment and feedback process. Leveraging large language models and learning analytics, the platform supports educators by offering tag-based recommendations and AI-generated feedback to enhance the quality and efficiency of open-response evaluations. A controlled experiment involving 65 higher education instructors assessed the platform's effectiveness in real-world environments. Using the technology acceptance model, this study investigated the platform's usefulness and relevance from the instructors' perspectives. Moreover, we collected data from the platform's usage to identify partners in instructors' behavior for different scenarios. Results indicate that AI-driven feedback significantly improved instructors' ability to provide detailed personalized feedback in less time. This study contributes to the growing research on AI applications in educational assessment and highlights key considerations for adopting AI-driven tools in instructional settings.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1471122
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1471122
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Empowering Instructors with AI: Evaluating the Impact of an AI-Driven Feedback Tool in Learning Analytics
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Cleon+Xavier%22">Cleon Xavier</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7617-5283">0000-0002-7617-5283</externalLink>)<br /><searchLink fieldCode="AR" term="%22Luiz+Rodrigues%22">Luiz Rodrigues</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0343-3701">0000-0003-0343-3701</externalLink>)<br /><searchLink fieldCode="AR" term="%22Newarney+Costa%22">Newarney Costa</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4954-176X">0000-0002-4954-176X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rodrigues+Neto%22">Rodrigues Neto</searchLink><br /><searchLink fieldCode="AR" term="%22Gabriel+Alves%22">Gabriel Alves</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2249-7818">0000-0002-2249-7818</externalLink>)<br /><searchLink fieldCode="AR" term="%22Taciana+Pontual+Falcao%22">Taciana Pontual Falcao</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2775-4913">0000-0003-2775-4913</externalLink>)<br /><searchLink fieldCode="AR" term="%22Dragan+Gasevic%22">Dragan Gasevic</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9265-1908">0000-0001-9265-1908</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rafael+Ferreira+Mello%22">Rafael Ferreira Mello</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3548-9670">0000-0003-3548-9670</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22IEEE+Transactions+on+Learning+Technologies%22"><i>IEEE Transactions on Learning Technologies</i></searchLink>. 2025 18:498-512.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 15
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Teacher+Empowerment%22">Teacher Empowerment</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Time+Management%22">Time Management</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Management+Systems%22">Learning Management Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Evaluation%22">Student Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Efficiency%22">Efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Quality%22">Educational Quality</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Attitudes%22">Teacher Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22College+Faculty%22">College Faculty</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1109/TLT.2025.3562379
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1939-1382
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Providing timely and personalized feedback on open-ended student responses is a challenge in education due to the increased workloads and time constraints educators face. While existing research has explored how learning analytic approaches can support feedback provision, previous studies have not sufficiently investigated educators' perspectives of how these strategies affect the assessment process. This article reports on the findings of a study that aimed to evaluate the impact of an artificial intelligence (AI)-driven platform designed to assist educators in the assessment and feedback process. Leveraging large language models and learning analytics, the platform supports educators by offering tag-based recommendations and AI-generated feedback to enhance the quality and efficiency of open-response evaluations. A controlled experiment involving 65 higher education instructors assessed the platform's effectiveness in real-world environments. Using the technology acceptance model, this study investigated the platform's usefulness and relevance from the instructors' perspectives. Moreover, we collected data from the platform's usage to identify partners in instructors' behavior for different scenarios. Results indicate that AI-driven feedback significantly improved instructors' ability to provide detailed personalized feedback in less time. This study contributes to the growing research on AI applications in educational assessment and highlights key considerations for adopting AI-driven tools in instructional settings.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1471122
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1471122
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/TLT.2025.3562379
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 498
    Subjects:
      – SubjectFull: Teacher Empowerment
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Computer Software
        Type: general
      – SubjectFull: Technology Integration
        Type: general
      – SubjectFull: Time Management
        Type: general
      – SubjectFull: Learning Management Systems
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Student Evaluation
        Type: general
      – SubjectFull: Computational Linguistics
        Type: general
      – SubjectFull: Efficiency
        Type: general
      – SubjectFull: Educational Quality
        Type: general
      – SubjectFull: Teacher Attitudes
        Type: general
      – SubjectFull: College Faculty
        Type: general
    Titles:
      – TitleFull: Empowering Instructors with AI: Evaluating the Impact of an AI-Driven Feedback Tool in Learning Analytics
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Cleon Xavier
      – PersonEntity:
          Name:
            NameFull: Luiz Rodrigues
      – PersonEntity:
          Name:
            NameFull: Newarney Costa
      – PersonEntity:
          Name:
            NameFull: Rodrigues Neto
      – PersonEntity:
          Name:
            NameFull: Gabriel Alves
      – PersonEntity:
          Name:
            NameFull: Taciana Pontual Falcao
      – PersonEntity:
          Name:
            NameFull: Dragan Gasevic
      – PersonEntity:
          Name:
            NameFull: Rafael Ferreira Mello
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 1939-1382
          Numbering:
            – Type: volume
              Value: 18
          Titles:
            – TitleFull: IEEE Transactions on Learning Technologies
              Type: main
ResultId 1