Generative AI in Mathematics Teacher Education: Designing Simulated Students to Generate Approximations to Practice Around Equal-Sign Misconceptions.
Saved in:
| Title: | Generative AI in Mathematics Teacher Education: Designing Simulated Students to Generate Approximations to Practice Around Equal-Sign Misconceptions. |
|---|---|
| Authors: | Tejera, Mathias1,2 mathias.tejera@utec.edu.uy, Parodi, Sebastián1 sebastian.parodi@utec.edu.uy, Galiç, Selen2 selen.galic@jku.at, Lavicza, Zsolt2 zsolt.lavicza@jku.at |
| Source: | International Journal for Technology in Mathematics Education. 2026, Vol. 33 Issue 2, p57-63. 7p. |
| Subject Terms: | *Simulation methods in education, *Instructional systems design, *Generative artificial intelligence, *Mathematics teachers, *Formative evaluation, Chatbots, Mathematical equivalence |
| Abstract: | This paper reports the design and refinement of a generative-AI simulation for mathematics teacher education centred on equal-sign misconceptions. We developed three custom chatbots that simulate middle-school students (11-12 years old) who exhibit distinct operational interpretations of "=" across three research-based tasks: ..., a chained equality (4 + 5 = 9 + 3 = 12), and ... Each simulated student was paired with a short video depicting the student's incorrect reasoning and was engineered through persona prompting to sustain "student-like" dialogue (brief initial justifications, resistance to immediate correction, inconsistent improvement, and spontaneous doubts) to preserve teachers' opportunities to elicit, interpret, and respond to student thinking. In parallel, we designed a mentor chatbot that provides structured formative feedback (strengths, areas for improvement, suggestions) anchored in equal-sign instruction and responsive teaching. We describe iterative development and cross-linguistic adaptation that addressed common failure modes of GenAI-based simulations, such as overly articulate student responses and generic feedback. The paper contributes a practical design account of how prompt constraints, role separation, and taskmisconception alignment can make GenAI-based simulations more stable and instructionally useful as approximations to practice. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal for Technology in Mathematics Education is the property of Research Information 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.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 195201990 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Generative AI in Mathematics Teacher Education: Designing Simulated Students to Generate Approximations to Practice Around Equal-Sign Misconceptions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tejera%2C+Mathias%22">Tejera, Mathias</searchLink><relatesTo>1,2</relatesTo><i> mathias.tejera@utec.edu.uy</i><br /><searchLink fieldCode="AR" term="%22Parodi%2C+Sebastián%22">Parodi, Sebastián</searchLink><relatesTo>1</relatesTo><i> sebastian.parodi@utec.edu.uy</i><br /><searchLink fieldCode="AR" term="%22Galiç%2C+Selen%22">Galiç, Selen</searchLink><relatesTo>2</relatesTo><i> selen.galic@jku.at</i><br /><searchLink fieldCode="AR" term="%22Lavicza%2C+Zsolt%22">Lavicza, Zsolt</searchLink><relatesTo>2</relatesTo><i> zsolt.lavicza@jku.at</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+for+Technology+in+Mathematics+Education%22">International Journal for Technology in Mathematics Education</searchLink>. 2026, Vol. 33 Issue 2, p57-63. 7p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Simulation+methods+in+education%22">Simulation methods in education</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems+design%22">Instructional systems design</searchLink><br />*<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematics+teachers%22">Mathematics teachers</searchLink><br />*<searchLink fieldCode="DE" term="%22Formative+evaluation%22">Formative evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+equivalence%22">Mathematical equivalence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper reports the design and refinement of a generative-AI simulation for mathematics teacher education centred on equal-sign misconceptions. We developed three custom chatbots that simulate middle-school students (11-12 years old) who exhibit distinct operational interpretations of "=" across three research-based tasks: ..., a chained equality (4 + 5 = 9 + 3 = 12), and ... Each simulated student was paired with a short video depicting the student's incorrect reasoning and was engineered through persona prompting to sustain "student-like" dialogue (brief initial justifications, resistance to immediate correction, inconsistent improvement, and spontaneous doubts) to preserve teachers' opportunities to elicit, interpret, and respond to student thinking. In parallel, we designed a mentor chatbot that provides structured formative feedback (strengths, areas for improvement, suggestions) anchored in equal-sign instruction and responsive teaching. We describe iterative development and cross-linguistic adaptation that addressed common failure modes of GenAI-based simulations, such as overly articulate student responses and generic feedback. The paper contributes a practical design account of how prompt constraints, role separation, and taskmisconception alignment can make GenAI-based simulations more stable and instructionally useful as approximations to practice. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal for Technology in Mathematics Education is the property of Research Information 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=ehh&AN=195201990 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1564/tme_v33.2.03 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 57 Subjects: – SubjectFull: Simulation methods in education Type: general – SubjectFull: Instructional systems design Type: general – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Mathematics teachers Type: general – SubjectFull: Formative evaluation Type: general – SubjectFull: Chatbots Type: general – SubjectFull: Mathematical equivalence Type: general Titles: – TitleFull: Generative AI in Mathematics Teacher Education: Designing Simulated Students to Generate Approximations to Practice Around Equal-Sign Misconceptions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tejera, Mathias – PersonEntity: Name: NameFull: Parodi, Sebastián – PersonEntity: Name: NameFull: Galiç, Selen – PersonEntity: Name: NameFull: Lavicza, Zsolt IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 17442710 Numbering: – Type: volume Value: 33 – Type: issue Value: 2 Titles: – TitleFull: International Journal for Technology in Mathematics Education Type: main |
| ResultId | 1 |
