Earth Science Simulations with Generative Artificial Intelligence (GenAI).
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| Title: | Earth Science Simulations with Generative Artificial Intelligence (GenAI). |
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| Authors: | Yoon-Sung Choi1 |
| Source: | Journal of University Teaching & Learning Practice. 2025, Vol. 22 Issue 1, p1-24. 24p. |
| Subject Terms: | *Generative artificial intelligence, *Earth science education, *Student teachers, *Educational outcomes, *Lesson planning |
| Abstract: | This study investigates the practical characteristics of Earth science mock lessons utilising generative artificial intelligence (GenAI). To accomplish this, the researcher developed a one-session Earth science mock lesson employing GenAI, following a five-week preparation phase. Three pre-service teachers from the Earth Science Education Department at University A's College of Education participated in the study. Data collection included all written materials related to the GenAI-integrated instructional plan (lesson plans, instructional resources, activity sheets, all texts used in interactions with the GenAI, and pre-service teachers' selfassessments following the mock lesson), as well as video footage and audio recordings of the mock lesson, and semi-structured interviews conducted post-lesson. The GenAI-enhanced Earth science lesson plans were analysed using the TIAR evaluation rubric to explore the strengths, considerations, and potential of GenAI-integrated instruction. Furthermore, anticipated learning outcomes were examined using an AI literacy framework. Findings indicate that the Earth science mock lessons demonstrated intentional GenAI utilization regarding learning objectives, instructional models and strategies, and assessment. While the lessons revealed instrumental advantages of GenAI in an instructional context, the need for a critical approach to avoid over-reliance on the technology was emphasized. Anticipated learning outcomes from the mock lessons were found to encompass the affective, behavioural, cognitive, and ethical domains of AI literacy. This study empirically applies GenAI in Earth science education, examining GenAI-related learning outcomes and is anticipated to positively impact pre-service teachers' pedagogical competencies through technologyenhanced instruction. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of University Teaching & Learning Practice is the property of Open Access Publishing Association 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 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 183964731 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Earth Science Simulations with Generative Artificial Intelligence (GenAI). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yoon-Sung+Choi%22">Yoon-Sung Choi</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+University+Teaching+%26+Learning+Practice%22">Journal of University Teaching & Learning Practice</searchLink>. 2025, Vol. 22 Issue 1, p1-24. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Earth+science+education%22">Earth science education</searchLink><br />*<searchLink fieldCode="DE" term="%22Student+teachers%22">Student teachers</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+outcomes%22">Educational outcomes</searchLink><br />*<searchLink fieldCode="DE" term="%22Lesson+planning%22">Lesson planning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigates the practical characteristics of Earth science mock lessons utilising generative artificial intelligence (GenAI). To accomplish this, the researcher developed a one-session Earth science mock lesson employing GenAI, following a five-week preparation phase. Three pre-service teachers from the Earth Science Education Department at University A's College of Education participated in the study. Data collection included all written materials related to the GenAI-integrated instructional plan (lesson plans, instructional resources, activity sheets, all texts used in interactions with the GenAI, and pre-service teachers' selfassessments following the mock lesson), as well as video footage and audio recordings of the mock lesson, and semi-structured interviews conducted post-lesson. The GenAI-enhanced Earth science lesson plans were analysed using the TIAR evaluation rubric to explore the strengths, considerations, and potential of GenAI-integrated instruction. Furthermore, anticipated learning outcomes were examined using an AI literacy framework. Findings indicate that the Earth science mock lessons demonstrated intentional GenAI utilization regarding learning objectives, instructional models and strategies, and assessment. While the lessons revealed instrumental advantages of GenAI in an instructional context, the need for a critical approach to avoid over-reliance on the technology was emphasized. Anticipated learning outcomes from the mock lessons were found to encompass the affective, behavioural, cognitive, and ethical domains of AI literacy. This study empirically applies GenAI in Earth science education, examining GenAI-related learning outcomes and is anticipated to positively impact pre-service teachers' pedagogical competencies through technologyenhanced instruction. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of University Teaching & Learning Practice is the property of Open Access Publishing Association 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=183964731 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.53761/nf1yqr46 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1 Subjects: – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Earth science education Type: general – SubjectFull: Student teachers Type: general – SubjectFull: Educational outcomes Type: general – SubjectFull: Lesson planning Type: general Titles: – TitleFull: Earth Science Simulations with Generative Artificial Intelligence (GenAI). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yoon-Sung Choi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14499789 Numbering: – Type: volume Value: 22 – Type: issue Value: 1 Titles: – TitleFull: Journal of University Teaching & Learning Practice Type: main |
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