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RETRACTED: Performance motivation and emotion regulation as drivers of academic competence and problem‐solving skills in AI‐enhanced preschool education: A SEM study.

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Title: RETRACTED: Performance motivation and emotion regulation as drivers of academic competence and problem‐solving skills in AI‐enhanced preschool education: A SEM study.
Authors: Zhao, Huiling (AUTHOR), Zhang, Huaichuan (AUTHOR), Li, Jinxin (AUTHOR), Liu, Hai (AUTHOR)
Source: British Educational Research Journal. Jun2026, Vol. 52 Issue 3, pe245-e266. 22p.
Subjects: Emotion regulation, Academic achievement, Early childhood education, Individualized instruction, Achievement motivation, Structural equation modeling, Intelligent tutoring systems, Problem solving
Geographic Terms: China
Abstract: The swift adoption of artificial intelligence (AI) in preschool education has sparked widespread interest, with emerging evidence suggesting that tools powered by AI can significantly improve early learning experiences by personalising instruction and supporting cognitive growth. Despite these advancements, the interplay between psychological factors (e.g., performance motivation and emotion regulation) and crucial educational outcomes (e.g., academic competence and problem‐solving skills) has remained relatively unexamined in AI‐enhanced preschool contexts. To bridge this gap, this study investigated the relationships among performance motivation, emotion regulation, academic competence and problem‐solving skills within AI‐enhanced preschool education in China. Adopting a quantitative design, the study included 464 preschool‐aged children (4–6 years), specifically selected within this developmental range. Data were gathered using age‐appropriate and validated tools and analysed through structural equation modelling (SEM). The findings indicated that performance motivation was a robust predictor of both academic competence and problem‐solving skills. Similarly, emotion regulation demonstrated significant correlations with academic competence and problem‐solving skills. The study proposes that incorporating strategies to bolster performance motivation and emotion regulation into AI‐enhanced preschool programmes can substantially elevate educational outcomes. These insights have practical implications for curriculum designers, instructors and technology developers seeking to harness AI's potential in early childhood education. [ABSTRACT FROM AUTHOR]
Copyright of British Educational Research Journal is the property of Wiley-Blackwell 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: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Huiling%22">Zhao, Huiling</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huaichuan%22">Zhang, Huaichuan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jinxin%22">Li, Jinxin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Hai%22">Liu, Hai</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Emotion+regulation%22">Emotion regulation</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Early+childhood+education%22">Early childhood education</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+motivation%22">Achievement motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink>
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  Data: The swift adoption of artificial intelligence (AI) in preschool education has sparked widespread interest, with emerging evidence suggesting that tools powered by AI can significantly improve early learning experiences by personalising instruction and supporting cognitive growth. Despite these advancements, the interplay between psychological factors (e.g., performance motivation and emotion regulation) and crucial educational outcomes (e.g., academic competence and problem‐solving skills) has remained relatively unexamined in AI‐enhanced preschool contexts. To bridge this gap, this study investigated the relationships among performance motivation, emotion regulation, academic competence and problem‐solving skills within AI‐enhanced preschool education in China. Adopting a quantitative design, the study included 464 preschool‐aged children (4–6 years), specifically selected within this developmental range. Data were gathered using age‐appropriate and validated tools and analysed through structural equation modelling (SEM). The findings indicated that performance motivation was a robust predictor of both academic competence and problem‐solving skills. Similarly, emotion regulation demonstrated significant correlations with academic competence and problem‐solving skills. The study proposes that incorporating strategies to bolster performance motivation and emotion regulation into AI‐enhanced preschool programmes can substantially elevate educational outcomes. These insights have practical implications for curriculum designers, instructors and technology developers seeking to harness AI's potential in early childhood education. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of British Educational Research Journal is the property of Wiley-Blackwell 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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        Value: 10.1002/berj.4196
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      – SubjectFull: Early childhood education
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      – SubjectFull: Individualized instruction
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      – SubjectFull: Achievement motivation
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      – SubjectFull: Structural equation modeling
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      – SubjectFull: Intelligent tutoring systems
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      – SubjectFull: Problem solving
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      – SubjectFull: China
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      – TitleFull: RETRACTED: Performance motivation and emotion regulation as drivers of academic competence and problem‐solving skills in AI‐enhanced preschool education: A SEM study.
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            NameFull: Zhao, Huiling
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            NameFull: Zhang, Huaichuan
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            NameFull: Li, Jinxin
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            – D: 01
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              Text: Jun2026
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              Y: 2026
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