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 |
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| Language: | English |
| Authors: | Spartak Sakibayev, Bela Sakibayeva, Darkhan Toybazarov |
| Source: | Journal of Educators Online. 2026 23(1). |
| Availability: | Journal of Educators Online. Grand Canyon University, 23300 West Camelback Road, Phoenix, AZ 85017. e-mail: CIRT@gcu.edu. Web site: https://www.thejeo.com |
| Peer Reviewed: | Y |
| Page Count: | 12 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Programming, Error Patterns, Error Correction, Coding, Instructional Effectiveness, Extracurricular Activities, College Students, Foreign Countries, Grades (Scholastic) |
| Geographic Terms: | Kazakhstan |
| ISSN: | 1547-500X |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1499130 |
| Database: | ERIC |
| 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. |
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| ISSN: | 1547-500X |
