An AI-supported modeling approach for teaching metal bonding: design and implementation of a teacher education program integrating model-based learning and the 5E learning cycle

IF 2.6 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Chemistry Education Research and Practice Pub Date : 2026-05-28 DOI:10.1039/D6RP00139D
Juhye Park and Seoung-Hey Paik
{"title":"An AI-supported modeling approach for teaching metal bonding: design and implementation of a teacher education program integrating model-based learning and the 5E learning cycle","authors":"Juhye Park and Seoung-Hey Paik","doi":"10.1039/D6RP00139D","DOIUrl":null,"url":null,"abstract":"<p >This study presents the design and implementation of an AI-supported modeling-based teacher education program integrating Model-Based Learning (MBL) with the 5E learning cycle. The program was implemented with 14 in-service chemistry teachers over 24 lessons and consisted of pre-interviews, three modeling cycles, and post-interviews. During the modeling process, teachers developed dynamic models through dialogic interactions with generative AI (Claude). Metamodeling knowledge levels were explored using seven questions across six domains: nature of models, purpose of models, modeling processes, variability, diversity, and evaluation criteria. Responses were analyzed from objectivist and subjectivist understandings. Teachers identified limitations of the electron sea model in explaining luster (<em>n</em> = 14), thermal conductivity (<em>n</em> = 7), malleability and ductility (<em>n</em> = 6), and electrical conductivity (<em>n</em> = 3). To address these, teachers developed educational models incorporating the wave nature of electrons, lattice vibrations (phonons), and redistribution of electron clouds. Analysis revealed a shift from predominantly objectivist understandings of models in the pre-assessment toward more subjectivist understandings of models in the post-assessment. Notable improvements were observed in modeling processes, variability, and diversity of models. These changes were observed in the context of modeling activities that involved recognizing model limitations and developing educational models, within which generative AI served as an interactive element supporting dialogic interaction and iterative revision as part of the broader instructional program. Teachers came to understand models not as fixed knowledge but as tentative and revisable inquiry tools. These findings provide direction for teacher education focused on strengthening modeling knowledge in science education.</p>","PeriodicalId":69,"journal":{"name":"Chemistry Education Research and Practice","volume":" 3","pages":" 1138-1153"},"PeriodicalIF":2.6000,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/rp/d6rp00139d?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemistry Education Research and Practice","FirstCategoryId":"95","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2026/rp/d6rp00139d","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0

Abstract

This study presents the design and implementation of an AI-supported modeling-based teacher education program integrating Model-Based Learning (MBL) with the 5E learning cycle. The program was implemented with 14 in-service chemistry teachers over 24 lessons and consisted of pre-interviews, three modeling cycles, and post-interviews. During the modeling process, teachers developed dynamic models through dialogic interactions with generative AI (Claude). Metamodeling knowledge levels were explored using seven questions across six domains: nature of models, purpose of models, modeling processes, variability, diversity, and evaluation criteria. Responses were analyzed from objectivist and subjectivist understandings. Teachers identified limitations of the electron sea model in explaining luster (n = 14), thermal conductivity (n = 7), malleability and ductility (n = 6), and electrical conductivity (n = 3). To address these, teachers developed educational models incorporating the wave nature of electrons, lattice vibrations (phonons), and redistribution of electron clouds. Analysis revealed a shift from predominantly objectivist understandings of models in the pre-assessment toward more subjectivist understandings of models in the post-assessment. Notable improvements were observed in modeling processes, variability, and diversity of models. These changes were observed in the context of modeling activities that involved recognizing model limitations and developing educational models, within which generative AI served as an interactive element supporting dialogic interaction and iterative revision as part of the broader instructional program. Teachers came to understand models not as fixed knowledge but as tentative and revisable inquiry tools. These findings provide direction for teacher education focused on strengthening modeling knowledge in science education.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种人工智能支持的金属键合教学建模方法:整合基于模型的学习和5E学习周期的教师教育计划的设计和实施
本研究提出了一个基于人工智能支持的基于建模的教师教育计划的设计和实施,该计划将基于模型的学习(MBL)与5E学习周期相结合。该项目由14名在职化学教师在24节课上实施,包括前访谈、三个建模周期和后访谈。在建模过程中,教师通过与生成式人工智能(Claude)的对话互动来开发动态模型。通过六个领域的七个问题来探索元建模知识水平:模型的性质、模型的目的、建模过程、可变性、多样性和评估标准。从客观主义和主观主义两方面分析了反应。教师们发现了电子海模型在解释光泽(n = 14)、导热性(n = 7)、延展性和延展性(n = 6)以及导电性(n = 3)方面的局限性。为了解决这些问题,教师们开发了包含电子波动性质、晶格振动(声子)和电子云再分配的教育模型。分析表明,在前评估中,对模型的客观理解占主导地位,而在后评估中,对模型的主观理解占主导地位。在建模过程、可变性和模型多样性方面观察到显著的改进。这些变化是在建模活动的背景下观察到的,这些活动涉及到识别模型局限性和开发教育模型,其中生成人工智能作为支持对话交互和迭代修订的交互元素,作为更广泛的教学计划的一部分。教师们不再把模型理解为固定的知识,而是试探性的、可修改的探究工具。研究结果为科学教育中加强建模知识的教师教育提供了方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.80
自引率
26.70%
发文量
64
审稿时长
6-12 weeks
期刊介绍: The journal for teachers, researchers and other practitioners in chemistry education.
期刊最新文献
From prompt engineering to modeling: secondary science teachers' use of generative AI to engineer sweetener molecules. Correction: Students’ epistemological resources and framing of stoichiometry assessment items across linguistic backgrounds: insights for equitable design To be or not to be a model? Exploring chemistry students’ ideas about general and specific meta-modeling knowledge regarding reaction mechanisms in organic chemistry An AI-supported modeling approach for teaching metal bonding: design and implementation of a teacher education program integrating model-based learning and the 5E learning cycle Students’ meaning-making of nonverbal communication as representational scaffolding in UK chemistry education: Chinese students’ cross-cultural perspectives
wanfang
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1