REGULATORY EFFECTS OF GENERATIVE AI ON SELF-REGULATED LEARNING AND AI LITERACY: A MIXED-METHODS STUDY.
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| Title: | REGULATORY EFFECTS OF GENERATIVE AI ON SELF-REGULATED LEARNING AND AI LITERACY: A MIXED-METHODS STUDY. |
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| Authors: | AKPINAR, Yavuz1 akpinar@bogazici.edu.tr, KUTBAY, Ekrem1 ekrem.kutbay@boun.edu.tr |
| Source: | Turkish Online Journal of Distance Education (TOJDE). Jul2026, Vol. 27 Issue 3, p1-24. 24p. |
| Subject Terms: | *Self-regulated learning, *Generative artificial intelligence, *Mixed methods research, *Cognitive psychology, *Educational technology, *Lesson planning, Prompt engineering, Artificial intelligence in education |
| Abstract: | This study aimed to explicitly examine cognitive patterns of prompt engineering, the role of pre-service teachers' artificial intelligence literacy (AIL) and online self-regulated learning (SRL) abilities in their use of prompting skills to develop lesson plans. The study was designed as a convergent parallel exploratory mixed-methods study involving quantitative analysis of students' data on achievement in lesson planning, SRL and AIL, and qualitative interpretative exploration of students' prompt sets based on cognitive domain and argumentation levels. The quantitative data was collected from 63 sophomore college students in a Faculty of Education through a quasi-experimental study with pre and post achievement tests, an AIL scale and an online learning self-regulation scale. The participants attended the experimental procedure with AIPRO. ORG in a computer laboratory within two hours. Two raters analyzed a total of 1070 prompts on the basis of hierarchical level of taxonomy of cognitive domain, and prompt sets on the basis of argumentative characteristics in three levels. The thematic analysis of the prompts revealed that the prompts were categorized into seven different themes. Sets of statistical tests, including general linear model 2X2 ANOVA tests and t tests, were conducted with the participants' quantitative data revealed that the study was effective in helping participants, regardless of level of SRL and AIL, to develop knowledge of lesson planning. The study then merged quantitative and qualitative results revealing implications for generative AI based college learning environments, and perspectives to inform new studies of learning and teaching with AI applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Online Journal of Distance Education (TOJDE) is the property of Turkish Online Journal of Distance Education 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: 195172438 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: REGULATORY EFFECTS OF GENERATIVE AI ON SELF-REGULATED LEARNING AND AI LITERACY: A MIXED-METHODS STUDY. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22AKPINAR%2C+Yavuz%22">AKPINAR, Yavuz</searchLink><relatesTo>1</relatesTo><i> akpinar@bogazici.edu.tr</i><br /><searchLink fieldCode="AR" term="%22KUTBAY%2C+Ekrem%22">KUTBAY, Ekrem</searchLink><relatesTo>1</relatesTo><i> ekrem.kutbay@boun.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Online+Journal+of+Distance+Education+%28TOJDE%29%22">Turkish Online Journal of Distance Education (TOJDE)</searchLink>. Jul2026, Vol. 27 Issue 3, p1-24. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Self-regulated+learning%22">Self-regulated learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Mixed+methods+research%22">Mixed methods research</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognitive+psychology%22">Cognitive psychology</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Lesson+planning%22">Lesson planning</searchLink><br /><searchLink fieldCode="DE" term="%22Prompt+engineering%22">Prompt engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+in+education%22">Artificial intelligence in education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study aimed to explicitly examine cognitive patterns of prompt engineering, the role of pre-service teachers' artificial intelligence literacy (AIL) and online self-regulated learning (SRL) abilities in their use of prompting skills to develop lesson plans. The study was designed as a convergent parallel exploratory mixed-methods study involving quantitative analysis of students' data on achievement in lesson planning, SRL and AIL, and qualitative interpretative exploration of students' prompt sets based on cognitive domain and argumentation levels. The quantitative data was collected from 63 sophomore college students in a Faculty of Education through a quasi-experimental study with pre and post achievement tests, an AIL scale and an online learning self-regulation scale. The participants attended the experimental procedure with AIPRO. ORG in a computer laboratory within two hours. Two raters analyzed a total of 1070 prompts on the basis of hierarchical level of taxonomy of cognitive domain, and prompt sets on the basis of argumentative characteristics in three levels. The thematic analysis of the prompts revealed that the prompts were categorized into seven different themes. Sets of statistical tests, including general linear model 2X2 ANOVA tests and t tests, were conducted with the participants' quantitative data revealed that the study was effective in helping participants, regardless of level of SRL and AIL, to develop knowledge of lesson planning. The study then merged quantitative and qualitative results revealing implications for generative AI based college learning environments, and perspectives to inform new studies of learning and teaching with AI applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Online Journal of Distance Education (TOJDE) is the property of Turkish Online Journal of Distance Education 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.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1 Subjects: – SubjectFull: Self-regulated learning Type: general – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Mixed methods research Type: general – SubjectFull: Cognitive psychology Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Lesson planning Type: general – SubjectFull: Prompt engineering Type: general – SubjectFull: Artificial intelligence in education Type: general Titles: – TitleFull: REGULATORY EFFECTS OF GENERATIVE AI ON SELF-REGULATED LEARNING AND AI LITERACY: A MIXED-METHODS STUDY. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: AKPINAR, Yavuz – PersonEntity: Name: NameFull: KUTBAY, Ekrem IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13026488 Numbering: – Type: volume Value: 27 – Type: issue Value: 3 Titles: – TitleFull: Turkish Online Journal of Distance Education (TOJDE) Type: main |
| ResultId | 1 |
