Academic Journal
AI Code Assistants in Programming Education: A Narrative Literature Review.
| Τίτλος: | AI Code Assistants in Programming Education: A Narrative Literature Review. |
|---|---|
| Συγγραφείς: | Farooq, Umer, Morganti, Dianna, Anwar, Saira |
| Πηγή: | Education Sciences; Jun2026, Vol. 16 Issue 6, p961, 14p |
| Θεματικοί όροι: | Computer programming education, Code generators, Intelligent agents, Psychology of students, Debugging, Thematic analysis, Psychological feedback, Educators' attitudes |
| Περίληψη: | Prior research suggests that programming is a fundamental competency for all students. Due to its importance, programming education is integrated across many disciplines beyond computer science (e.g., humanities, social sciences, and engineering). Also, many existing courses report increasing enrollment trends. However, these changes have also introduced instructional challenges, particularly in supporting students with diverse backgrounds at scale. In this context, many studies have explored the use of AI code assistants as tools that may support instruction and learning. In these studies, while examining the use of AI code assistants, researchers have reported variation in educational contexts, implementation approaches, and outcomes. With this paper, we argue that synthesized information of such variations could help in understanding the effective use of such tools in programming education. To create a synthesized resource on AI code assistants, in this paper, we present a narrative review that synthesizes existing research. We reviewed 29 peer-reviewed studies identified through searches across three databases. The studies were analyzed to identify reported patterns of use, student and instructor perceptions, limitations in existing research, and suggested directions for future research. Across the reviewed studies, AI code assistants were commonly discussed for tasks such as code generation, debugging support, and real-time feedback, with ChatGPT reported most often (16 mentions), followed by GitHub Copilot (6 mentions). Disciplinary information was available in 24 studies, which helped identify the academic settings where AI code assistants were reported. Students generally describe these tools as useful, while also expressing concerns related to over-reliance and accuracy. Student perceptions were reported in 10 studies, while instructor perceptions were reported in 4 studies. Common reported limitations include small sample sizes, short intervention durations, reliance on self-reported data, and limited examination of long-term learning outcomes. Overall, this review consolidates current evidence on how AI code assistants are used and perceived in programming education and identifies areas where more research is needed. [ABSTRACT FROM AUTHOR] |
| Copyright of Education Sciences is the property of MDPI 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.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: AI Code Assistants in Programming Education: A Narrative Literature Review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Farooq%2C+Umer%22">Farooq, Umer</searchLink><br /><searchLink fieldCode="AR" term="%22Morganti%2C+Dianna%22">Morganti, Dianna</searchLink><br /><searchLink fieldCode="AR" term="%22Anwar%2C+Saira%22">Anwar, Saira</searchLink> – Name: TitleSource Label: Source Group: Src Data: Education Sciences; Jun2026, Vol. 16 Issue 6, p961, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+programming+education%22">Computer programming education</searchLink><br /><searchLink fieldCode="DE" term="%22Code+generators%22">Code generators</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+agents%22">Intelligent agents</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology+of+students%22">Psychology of students</searchLink><br /><searchLink fieldCode="DE" term="%22Debugging%22">Debugging</searchLink><br /><searchLink fieldCode="DE" term="%22Thematic+analysis%22">Thematic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+feedback%22">Psychological feedback</searchLink><br /><searchLink fieldCode="DE" term="%22Educators'+attitudes%22">Educators' attitudes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Prior research suggests that programming is a fundamental competency for all students. Due to its importance, programming education is integrated across many disciplines beyond computer science (e.g., humanities, social sciences, and engineering). Also, many existing courses report increasing enrollment trends. However, these changes have also introduced instructional challenges, particularly in supporting students with diverse backgrounds at scale. In this context, many studies have explored the use of AI code assistants as tools that may support instruction and learning. In these studies, while examining the use of AI code assistants, researchers have reported variation in educational contexts, implementation approaches, and outcomes. With this paper, we argue that synthesized information of such variations could help in understanding the effective use of such tools in programming education. To create a synthesized resource on AI code assistants, in this paper, we present a narrative review that synthesizes existing research. We reviewed 29 peer-reviewed studies identified through searches across three databases. The studies were analyzed to identify reported patterns of use, student and instructor perceptions, limitations in existing research, and suggested directions for future research. Across the reviewed studies, AI code assistants were commonly discussed for tasks such as code generation, debugging support, and real-time feedback, with ChatGPT reported most often (16 mentions), followed by GitHub Copilot (6 mentions). Disciplinary information was available in 24 studies, which helped identify the academic settings where AI code assistants were reported. Students generally describe these tools as useful, while also expressing concerns related to over-reliance and accuracy. Student perceptions were reported in 10 studies, while instructor perceptions were reported in 4 studies. Common reported limitations include small sample sizes, short intervention durations, reliance on self-reported data, and limited examination of long-term learning outcomes. Overall, this review consolidates current evidence on how AI code assistants are used and perceived in programming education and identifies areas where more research is needed. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Education Sciences is the property of MDPI 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: Identifiers: – Type: doi Value: 10.3390/educsci16060961 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 961 Subjects: – SubjectFull: Computer programming education Type: general – SubjectFull: Code generators Type: general – SubjectFull: Intelligent agents Type: general – SubjectFull: Psychology of students Type: general – SubjectFull: Debugging Type: general – SubjectFull: Thematic analysis Type: general – SubjectFull: Psychological feedback Type: general – SubjectFull: Educators' attitudes Type: general Titles: – TitleFull: AI Code Assistants in Programming Education: A Narrative Literature Review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Farooq, Umer – PersonEntity: Name: NameFull: Morganti, Dianna – PersonEntity: Name: NameFull: Anwar, Saira IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22277102 Numbering: – Type: volume Value: 16 – Type: issue Value: 6 Titles: – TitleFull: Education Sciences Type: main |
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