When AI Becomes a Resource: AI-Supported Teacher Design Capacity in Mathematics Teacher Education
Abstract
Generative artificial intelligence (AI) can produce apparently complete mathematics lesson plans within seconds, raising a central question for teacher education: how can pre-service teachers use AI-generated resources while remaining responsible for mathematical and pedagogical design? This qualitative study investigates AI-supported lesson design during an 11-week course at a Chinese university. Data comprised AI dialogues, reports, three lessonplan versions, microteaching videos, and evaluation reports from 20 participants; three focal cases were examined through follow-up interviews. Drawing on the Documentational Approach to Didactics and Teacher Design Capacity, we propose AI-supported Teacher Design Capacity (AI-TDC) as an analytical construct characterising five interrelated dimensions of knowledge-in-action: Orienting, Verifying, Transforming, Regulating, and Collectivising. The focal cases revealed contrasting configurations in which AI became a mediating hub, a task-specific mediator, or a supplementary reviewing resource; these configurations could not be reduced to interaction volume. AI-generated resources became components of teaching documents through mathematical and didactical transformation: participants reorganised conceptual relations, constructed problem sequences, and selectively retained or rejected proposals according to intended mathematical trajectories for students. The course created opportunities for such work through cycles of individual design, collective scrutiny, enactment, and redesign. AI-TDC thus offers a framework for analysing and supporting documentational work with AI in mathematics teacher education.
Related
- Changes and Resistance Regarding the Integration of New Technologies into Elementary School Mathematics Education
- Support in the domain of numerical algorithms in the education of future secondary school mathematics teachers
- On a fair and risk-averse urban air mobility resource allocation problem under demand and capacity uncertainties
- A comparison of three models of a whirling string as it becomes chaotic
- How Divergence Becomes Decision Flips in Compressed Language Models
BibTeX
@misc{wang2026when,
title = {{When AI Becomes a Resource: AI-Supported Teacher Design Capacity in Mathematics Teacher Education}},
author = {Chongyang Wang and Luc Trouche},
howpublished = {HAL},
year = {2026},
month = sep,
url = {https://hal.science/hal-05740229v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - When AI Becomes a Resource: AI-Supported Teacher Design Capacity in Mathematics Teacher Education
AU - Chongyang Wang
AU - Luc Trouche
PY - 2026
DA - 2026/09/07
PB - HAL
UR - https://hal.science/hal-05740229v1
AB - Generative artificial intelligence (AI) can produce apparently complete mathematics lesson plans within seconds, raising a central question for teacher education: how can pre-service teachers use AI-generated resources while remaining responsible for mathematical and pedagogical design? This qualitative study investigates AI-supported lesson design during an 11-week course at a Chinese university. Data comprised AI dialogues, reports, three lessonplan versions, microteaching videos, and evaluation reports from 20 participants; three focal cases were examined through follow-up interviews. Drawing on the Documentational Approach to Didactics and Teacher Design Capacity, we propose AI-supported Teacher Design Capacity (AI-TDC) as an analytical construct characterising five interrelated dimensions of knowledge-in-action: Orienting, Verifying, Transforming, Regulating, and Collectivising. The focal cases revealed contrasting configurations in which AI became a mediating hub, a task-specific mediator, or a supplementary reviewing resource; these configurations could not be reduced to interaction volume. AI-generated resources became components of teaching documents through mathematical and didactical transformation: participants reorganised conceptual relations, constructed problem sequences, and selectively retained or rejected proposals according to intended mathematical trajectories for students. The course created opportunities for such work through cycles of individual design, collective scrutiny, enactment, and redesign. AI-TDC thus offers a framework for analysing and supporting documentational work with AI in mathematics teacher education.
ER -