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Intelligent Teaching Design Assistant for Primary Mathematics: A Large Language Model-Driven Framework with Retrieval-Augmented Generation and Problem-Chain Pedagogy

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Title: Intelligent Teaching Design Assistant for Primary Mathematics: A Large Language Model-Driven Framework with Retrieval-Augmented Generation and Problem-Chain Pedagogy
Language: English
Authors: Danna Tang (ORCID 0009-0000-7686-3521), Ran Ding (ORCID 0009-0009-1458-2754), Meng He (ORCID 0000-0001-6869-9194), Yushen Wang (ORCID 0009-0005-4470-9390), Kaka Cheng (ORCID 0000-0002-9931-0572)
Source: International Electronic Journal of Mathematics Education. 2026 21(1).
Availability: International Electronic Journal of Mathematics Education. Suite 124, Challenge House 616 Mitcham Road, CR0 3AA, Croydon, London, UK. Tel: +44-208-936-7681; e-mail: iejme@iejme.com; Web site: https://www.iejme.com
Peer Reviewed: Y
Page Count: 12
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Descriptors: Elementary School Mathematics, Mathematics Education, Intelligent Tutoring Systems, Elementary School Teachers, Mathematics Teachers, Artificial Intelligence, Technology Uses in Education, Program Effectiveness, Usability, Value Judgment
ISSN: 1306-3030
Abstract: Primary mathematics education faces systemic challenges in translating curriculum reforms into classroom practice, exacerbated by teachers' cognitive overload and limited support for pedagogical innovation. This study develops an Intelligent Teaching Design Assistant grounded in socio-constructivist and cognitive load theories to address these challenges. Thirty-four primary mathematics teachers participated in a quasi-experimental study. The Intelligent Teaching Design Assistant integrates Large Language Models with multi-dimensional knowledge bases (curriculum standards, teaching strategies, student profiles) and a multi-agent architecture (process planner, student simulator). The Intelligent Teaching Design Assistant significantly outperformed generic Large Language Models, improving overall lesson plan quality. This work pioneers a replicable pathway for AI to empower teacher agency and advance 21st-century educational transformation.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1505528
Database: ERIC
FullText Text:
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  Data: Intelligent Teaching Design Assistant for Primary Mathematics: A Large Language Model-Driven Framework with Retrieval-Augmented Generation and Problem-Chain Pedagogy
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  Data: <searchLink fieldCode="AR" term="%22Danna+Tang%22">Danna Tang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-7686-3521">0009-0000-7686-3521</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ran+Ding%22">Ran Ding</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0009-1458-2754">0009-0009-1458-2754</externalLink>)<br /><searchLink fieldCode="AR" term="%22Meng+He%22">Meng He</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6869-9194">0000-0001-6869-9194</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yushen+Wang%22">Yushen Wang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-4470-9390">0009-0005-4470-9390</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kaka+Cheng%22">Kaka Cheng</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9931-0572">0000-0002-9931-0572</externalLink>)
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  Data: International Electronic Journal of Mathematics Education. Suite 124, Challenge House 616 Mitcham Road, CR0 3AA, Croydon, London, UK. Tel: +44-208-936-7681; e-mail: iejme@iejme.com; Web site: https://www.iejme.com
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  Data: Primary mathematics education faces systemic challenges in translating curriculum reforms into classroom practice, exacerbated by teachers' cognitive overload and limited support for pedagogical innovation. This study develops an Intelligent Teaching Design Assistant grounded in socio-constructivist and cognitive load theories to address these challenges. Thirty-four primary mathematics teachers participated in a quasi-experimental study. The Intelligent Teaching Design Assistant integrates Large Language Models with multi-dimensional knowledge bases (curriculum standards, teaching strategies, student profiles) and a multi-agent architecture (process planner, student simulator). The Intelligent Teaching Design Assistant significantly outperformed generic Large Language Models, improving overall lesson plan quality. This work pioneers a replicable pathway for AI to empower teacher agency and advance 21st-century educational transformation.
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
    Subjects:
      – SubjectFull: Elementary School Mathematics
        Type: general
      – SubjectFull: Mathematics Education
        Type: general
      – SubjectFull: Intelligent Tutoring Systems
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      – SubjectFull: Elementary School Teachers
        Type: general
      – SubjectFull: Mathematics Teachers
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Program Effectiveness
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      – SubjectFull: Usability
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      – SubjectFull: Value Judgment
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      – TitleFull: Intelligent Teaching Design Assistant for Primary Mathematics: A Large Language Model-Driven Framework with Retrieval-Augmented Generation and Problem-Chain Pedagogy
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