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Generative Artificial Intelligence (GenAI) Meets Assessment: Experimental Insights into Teacher Candidates' Attitudes and Acceptance

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Title: Generative Artificial Intelligence (GenAI) Meets Assessment: Experimental Insights into Teacher Candidates' Attitudes and Acceptance
Language: English
Authors: Kübra Karakaya Özyer (ORCID 0000-0002-0208-7870), Betül Aydin (ORCID 0000-0002-4739-6503)
Source: International Journal of Education in Mathematics, Science and Technology. 2026 14(1):21-45.
Availability: International Journal of Education in Mathematics, Science and Technology. Necmettin Erbakan University, Ahmet Kelesoglu Education Faculty, Meram, Konya, 42090, Turkey. e-mail: ijermst@gmail.com; Web site: https://www.ijemst.net/index.php/ijemst/index
Peer Reviewed: Y
Page Count: 25
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Preservice Teachers, Student Attitudes, Computer Uses in Education, Adoption (Ideas), Performance Based Assessment
ISSN: 2147-611X
Abstract: This experimental study examined the impact of pre-service teachers' development of performance tasks utilizing differentiated generative artificial intelligence tools--textual, visual, and video-based--on their attitudes towards generative artificial intelligence (AI) and their acceptance levels of AI. A quasi-experimental approach was utilized to address the research issues, incorporating one control group and two randomly allocated experimental groups. Ninety-four pre-service teachers voluntarily participated in the study. The groups were categorized based on three distinct generative artificial intelligence tools utilized by the students in the development of a performance task. As a results of analyses, a significant increase in both positive attitude and acceptance level was reported in the Control group using text-based GenAI; an increase only in positive attitude in the Experiment 1 group; and a decrease in negative attitude in the Experiment 2 group. Engagement with differentiated generative AI tools resulted in notable alterations in in-group attitudes and levels of acceptability. This study underscores the essential requirement for pre-service teachers to implement a nuanced and diversified strategy for AI integration, acknowledging the advantages and limitations of different AI tools and their capacity to affect pre-service teachers' attitudes and acceptance in various manners.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1494399
Database: ERIC
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  Data: Generative Artificial Intelligence (GenAI) Meets Assessment: Experimental Insights into Teacher Candidates' Attitudes and Acceptance
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  Data: <searchLink fieldCode="AR" term="%22Kübra+Karakaya+Özyer%22">Kübra Karakaya Özyer</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0208-7870">0000-0002-0208-7870</externalLink>)<br /><searchLink fieldCode="AR" term="%22Betül+Aydin%22">Betül Aydin</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4739-6503">0000-0002-4739-6503</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Education+in+Mathematics%2C+Science+and+Technology%22"><i>International Journal of Education in Mathematics, Science and Technology</i></searchLink>. 2026 14(1):21-45.
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  Data: International Journal of Education in Mathematics, Science and Technology. Necmettin Erbakan University, Ahmet Kelesoglu Education Faculty, Meram, Konya, 42090, Turkey. e-mail: ijermst@gmail.com; Web site: https://www.ijemst.net/index.php/ijemst/index
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  Data: This experimental study examined the impact of pre-service teachers' development of performance tasks utilizing differentiated generative artificial intelligence tools--textual, visual, and video-based--on their attitudes towards generative artificial intelligence (AI) and their acceptance levels of AI. A quasi-experimental approach was utilized to address the research issues, incorporating one control group and two randomly allocated experimental groups. Ninety-four pre-service teachers voluntarily participated in the study. The groups were categorized based on three distinct generative artificial intelligence tools utilized by the students in the development of a performance task. As a results of analyses, a significant increase in both positive attitude and acceptance level was reported in the Control group using text-based GenAI; an increase only in positive attitude in the Experiment 1 group; and a decrease in negative attitude in the Experiment 2 group. Engagement with differentiated generative AI tools resulted in notable alterations in in-group attitudes and levels of acceptability. This study underscores the essential requirement for pre-service teachers to implement a nuanced and diversified strategy for AI integration, acknowledging the advantages and limitations of different AI tools and their capacity to affect pre-service teachers' attitudes and acceptance in various manners.
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      – Text: English
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        PageCount: 25
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      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Preservice Teachers
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      – SubjectFull: Student Attitudes
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      – SubjectFull: Computer Uses in Education
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      – SubjectFull: Adoption (Ideas)
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      – SubjectFull: Performance Based Assessment
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      – TitleFull: Generative Artificial Intelligence (GenAI) Meets Assessment: Experimental Insights into Teacher Candidates' Attitudes and Acceptance
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              Y: 2026
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