Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers’ uptake, revision, and modeling task-design quality
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Resumo
IntroductionAs 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.MethodsUsing 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.ResultsParticipants 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.DiscussionFeedback 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.
Dados da obra
- Tipo
- Artigo
- Periódico
- Frontiers in Psychology, v. 17
- Editora
- Frontiers Media S.A.
- Ano
- 2026 (2026-10)
- Idioma
- en
- Palavras-chave
- feedback literacy; feedback translation; generative AI; mathematical modeling; peer feedback; pre-service mathematics teacher education
- Temas
- Formação de Professores de Matemática, Modelagem Matemática
- DOI
- 10.3389/fpsyg.2026.1922989
- ISSN
- 1664-1078
- Acesso
- Acesso aberto
- Fontes
- DOAJ — Directory of Open Access Journals
- Na Biblioteca desde
- 01/11/2026 · verificada em 01/11/2026
Como citar
ABNT
OH, Sejun. Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers’ uptake, revision, and modeling task-design quality. Frontiers in Psychology, v. 17, 2026. DOI: https://doi.org/10.3389/fpsyg.2026.1922989.
APA
Oh, S. (2026). Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers’ uptake, revision, and modeling task-design quality. Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1922989
BibTeX
@article{oh2026,
author = {Oh, Sejun},
title = {Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers’ uptake, revision, and modeling task-design quality},
year = {2026},
journal = {Frontiers in Psychology},
volume = {17},
publisher = {Frontiers Media S.A.},
doi = {10.3389/fpsyg.2026.1922989},
language = {en}
}RIS
TY - JOUR AU - Oh, Sejun TI - Translating AI and peer feedback in sustainability-oriented STEM teacher education: pre-service mathematics teachers’ uptake, revision, and modeling task-design quality PY - 2026 JO - Frontiers in Psychology VL - 17 DO - 10.3389/fpsyg.2026.1922989 LA - en ER -
Referência gerada automaticamente a partir dos metadados das fontes; confira com o documento original. Baixar BibTeX · Baixar RIS
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