Academic Journal
Προβολή σε EDS

Teacher-centered LLM-based multi-agent systems–towards differentiated educational worksheets.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Teacher-centered LLM-based multi-agent systems–towards differentiated educational worksheets.
Συγγραφείς: Gonnermann-Müller, Jana1,2 (AUTHOR) gonnermann-mueller@zib.de, Haase, Jennifer2,3 (AUTHOR), Fackeldey, Konstantin1,4 (AUTHOR), Pokutta, Sebastian1,4 (AUTHOR)
Πηγή: Innovation: The European Journal of Social Sciences. Sep2026, p1-23. 23p. 3 Illustrations.
Θεματικοί όροι: Multiagent systems, Individualized instruction, Mathematics education, Artificial intelligence in education, Instructional systems, Academic motivation, Language models
Περίληψη: Classrooms are increasingly heterogeneous with respect to students' language backgrounds, prior knowledge, and motivational dispositions, making differentiated instruction essential for equitable learning. Yet implementing differentiation remains challenging, as teachers face high workloads, and existing Artificial Intelligence (AI) tools mainly focus on performance while neglecting motivational and emotional factors. This paper presents a teacher-facing, large language model (LLM)-based multi-agent system for generating differentiated mathematics worksheets that account for both cognitive and motivational learner characteristics, supporting teacher-centered, AI-driven personalization in mathematics education. The framework includes three specialized agents: (1) learner agents that simulate diverse profiles incorporating topic proficiency and intrinsic motivation, (2) a teacher agent that adapts instructional content according to didactical principles, and (3) an evaluator agent that provides automated quality assurance. We tested the system using authentic grade 8 mathematics curriculum content and evaluated its feasibility through a) automated agent-based assessment of output quality and b) exploratory feedback from K-12 in-service teachers. Preliminary findings from ten evaluations highlight stability and alignment between generated materials and learner profiles. Teacher feedback particularly emphasized the structure and suitability of tasks. Findings suggest that multi-agent LLM architectures have the potential to enable scalable, context-aware differentiation in heterogeneous classroom settings and outline directions for extending the framework to richer learner profiles and real-world classroom trials. [ABSTRACT FROM AUTHOR]
Copyright of Innovation: The European Journal of Social Sciences is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Business Source Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:bsx&genre=article&issn=13511610&ISBN=&volume=&issue=&date=20260916&spage=1&pages=1-23&title=Innovation: The European Journal of Social Sciences&atitle=Teacher-centered%20LLM-based%20multi-agent%20systems%E2%80%93towards%20differentiated%20educational%20worksheets.&aulast=Gonnermann-M%C3%BCller%2C%20Jana&id=DOI:10.1080/13511610.2026.2727110
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: bsx
DbLabel: Business Source Index
An: 197035716
RelevancyScore: 1435
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1435
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Teacher-centered LLM-based multi-agent systems–towards differentiated educational worksheets.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gonnermann-Müller%2C+Jana%22">Gonnermann-Müller, Jana</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> gonnermann-mueller@zib.de</i><br /><searchLink fieldCode="AR" term="%22Haase%2C+Jennifer%22">Haase, Jennifer</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fackeldey%2C+Konstantin%22">Fackeldey, Konstantin</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pokutta%2C+Sebastian%22">Pokutta, Sebastian</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Innovation%3A+The+European+Journal+of+Social+Sciences%22">Innovation: The European Journal of Social Sciences</searchLink>. Sep2026, p1-23. 23p. 3 Illustrations.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+education%22">Mathematics education</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+in+education%22">Artificial intelligence in education</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+motivation%22">Academic motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Classrooms are increasingly heterogeneous with respect to students' language backgrounds, prior knowledge, and motivational dispositions, making differentiated instruction essential for equitable learning. Yet implementing differentiation remains challenging, as teachers face high workloads, and existing Artificial Intelligence (AI) tools mainly focus on performance while neglecting motivational and emotional factors. This paper presents a teacher-facing, large language model (LLM)-based multi-agent system for generating differentiated mathematics worksheets that account for both cognitive and motivational learner characteristics, supporting teacher-centered, AI-driven personalization in mathematics education. The framework includes three specialized agents: (1) learner agents that simulate diverse profiles incorporating topic proficiency and intrinsic motivation, (2) a teacher agent that adapts instructional content according to didactical principles, and (3) an evaluator agent that provides automated quality assurance. We tested the system using authentic grade 8 mathematics curriculum content and evaluated its feasibility through a) automated agent-based assessment of output quality and b) exploratory feedback from K-12 in-service teachers. Preliminary findings from ten evaluations highlight stability and alignment between generated materials and learner profiles. Teacher feedback particularly emphasized the structure and suitability of tasks. Findings suggest that multi-agent LLM architectures have the potential to enable scalable, context-aware differentiation in heterogeneous classroom settings and outline directions for extending the framework to richer learner profiles and real-world classroom trials. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Innovation: The European Journal of Social Sciences is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsx&AN=197035716
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/13511610.2026.2727110
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1
    Subjects:
      – SubjectFull: Multiagent systems
        Type: general
      – SubjectFull: Individualized instruction
        Type: general
      – SubjectFull: Mathematics education
        Type: general
      – SubjectFull: Artificial intelligence in education
        Type: general
      – SubjectFull: Instructional systems
        Type: general
      – SubjectFull: Academic motivation
        Type: general
      – SubjectFull: Language models
        Type: general
    Titles:
      – TitleFull: Teacher-centered LLM-based multi-agent systems–towards differentiated educational worksheets.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Gonnermann-Müller, Jana
      – PersonEntity:
          Name:
            NameFull: Haase, Jennifer
      – PersonEntity:
          Name:
            NameFull: Fackeldey, Konstantin
      – PersonEntity:
          Name:
            NameFull: Pokutta, Sebastian
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 16
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 13511610
          Titles:
            – TitleFull: Innovation: The European Journal of Social Sciences
              Type: main
ResultId 1