Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers' uptake, revision, and modeling task-design quality.
- 1Department of Mathematics Education, Hongik University, Seoul, Republic of Korea.
Abstract
Introduction
As generative AI becomes increasingly available in teacher education, a central question is how pre-service teachers interpret, evaluate, and translate different feedback sources into coherent design decisions. This study examined how 22 South Korean pre-service mathematics teachers used AI and peer feedback while revising sustainability-oriented mathematical modeling tasks on school cafeteria food waste, a context aligned with Sustainable Development Goal 12.Methods
Using an exploratory, comparative mixed-methods sequence-comparison design embedded in an authentic teacher-education course, participants received two rounds of feedback in different sequences: AI followed by peer feedback or peer feedback followed by AI. Framed within cognitive, affective, and behavioral dimensions of feedback engagement, the analysis combined double-coded ratings of initial and final task designs, feedback-response records, revision-depth coding, validation-focused revision indicators, and post-activity reflections.Results
Participants rarely copied feedback passively: 20 selectively accepted feedback in at least one round, and 17 made at least one structural or reconceptual revision. Five participants showed a positive composite gain in rated task-design quality. Participants valued AI feedback for its specificity, new perspectives, and speed, and more often made validation-focused revisions after it, whereas they valued peer feedback for mutual communication, classroom realism, and student perspective. Descriptive comparisons showed different engagement trajectories across the sequence conditions alongside similar composite-quality changes.Discussion
Feedback translation provides an analytic perspective on the divergence between revision activity and artifact quality. Within this course context, the findings show how feedback evaluation, revision decisions, and artifact quality can diverge and suggest that teacher educators should scaffold evaluative judgment, source comparison, and post-revision coherence checking.
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BibTeX
@article{oh2026translating,
title = {{Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers' uptake, revision, and modeling task-design quality.}},
author = {Sejun Oh},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1922989},
url = {https://europepmc.org/article/MED/42903283},
note = {CC BY},
}
RIS
TY - JOUR
TI - Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers' uptake, revision, and modeling task-design quality.
AU - Sejun Oh
PY - 2026
DA - 2026/10/06
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1922989
UR - https://europepmc.org/article/MED/42903283
AB - Introduction As generative AI becomes increasingly available in teacher education, a central question is how pre-service teachers interpret, evaluate, and translate different feedback sources into coherent design decisions. This study examined how 22 South Korean pre-service mathematics teachers used AI and peer feedback while revising sustainability-oriented mathematical modeling tasks on school cafeteria food waste, a context aligned with Sustainable Development Goal 12.Methods Using an exploratory, comparative mixed-methods sequence-comparison design embedded in an authentic teacher-education course, participants received two rounds of feedback in different sequences: AI followed by peer feedback or peer feedback followed by AI. Framed within cognitive, affective, and behavioral dimensions of feedback engagement, the analysis combined double-coded ratings of initial and final task designs, feedback-response records, revision-depth coding, validation-focused revision indicators, and post-activity reflections.Results Participants rarely copied feedback passively: 20 selectively accepted feedback in at least one round, and 17 made at least one structural or reconceptual revision. Five participants showed a positive composite gain in rated task-design quality. Participants valued AI feedback for its specificity, new perspectives, and speed, and more often made validation-focused revisions after it, whereas they valued peer feedback for mutual communication, classroom realism, and student perspective. Descriptive comparisons showed different engagement trajectories across the sequence conditions alongside similar composite-quality changes.Discussion Feedback translation provides an analytic perspective on the divergence between revision activity and artifact quality. Within this course context, the findings show how feedback evaluation, revision decisions, and artifact quality can diverge and suggest that teacher educators should scaffold evaluative judgment, source comparison, and post-revision coherence checking.
SN - 1664-1078
ER -