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AI-GENERATED ERRORS AS A LEARNING TOOL: IMPROVING PROGRAMMING EDUCATION THROUGH ERROR CORRECTION.

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Title: AI-GENERATED ERRORS AS A LEARNING TOOL: IMPROVING PROGRAMMING EDUCATION THROUGH ERROR CORRECTION.
Authors: Sakibayev, Spartak1, Sakibayeva, Bela2, Toybazarov, Darkhan1
Source: Journal of Educators Online. Jan2026, Vol. 23 Issue 1, p1-12. 12p.
Subject Terms: *Computer programming education, *Artificial intelligence, *Students, *Academic achievement, *Problem solving, *Intelligent tutoring systems
Abstract: This study examines the utilization of AI-generated errors in programming education, with a focus on students' ability to identify and correct errors intentionally introduced into AI-generated code. The primary objective is to evaluate the effectiveness of this approach in enhancing students' programming competence and academic performance. Participants engaged with AI-generated code containing predefined errors, and their error detection and correction skills were assessed. The findings indicate that this method supports the development of programming proficiency and contributes to improved academic outcomes. This study adds to the existing literature on AI in education, providing a basis for future research on integrating AI tools into programming instruction. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Educators Online is the property of Journal of Educators Online 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
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  Data: <searchLink fieldCode="AR" term="%22Sakibayev%2C+Spartak%22">Sakibayev, Spartak</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sakibayeva%2C+Bela%22">Sakibayeva, Bela</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Toybazarov%2C+Darkhan%22">Toybazarov, Darkhan</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educators+Online%22">Journal of Educators Online</searchLink>. Jan2026, Vol. 23 Issue 1, p1-12. 12p.
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  Data: *<searchLink fieldCode="DE" term="%22Computer+programming+education%22">Computer programming education</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Students%22">Students</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink>
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  Data: This study examines the utilization of AI-generated errors in programming education, with a focus on students' ability to identify and correct errors intentionally introduced into AI-generated code. The primary objective is to evaluate the effectiveness of this approach in enhancing students' programming competence and academic performance. Participants engaged with AI-generated code containing predefined errors, and their error detection and correction skills were assessed. The findings indicate that this method supports the development of programming proficiency and contributes to improved academic outcomes. This study adds to the existing literature on AI in education, providing a basis for future research on integrating AI tools into programming instruction. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Educators Online is the property of Journal of Educators Online 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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              Text: Jan2026
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