Cited by 49Teaching and Learning ProgrammingArtificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive Learning

Artificial intelligence in computer programming education: A systematic literature review

Open access
Manorat, Pisut, Tuarob, Suppawong, Pongpaichet, Siripen·2025·Computers and Education: Artificial Intelligence

Abstract

The demand for skilled programmers and the increasing complexity of coding skills have led to a rise in the adoption of artificial intelligence (AI) and machine learning (ML) technologies in computer programming education. Previous research has explored the potential of AI in aspects such as grading assignments, generating feedback, detecting plagiarism, and identifying at-risk students, but there is a lack of systematic reviews focused on AI-powered teaching processes in computer programming classes. To provide a more comprehensive understanding of AI and ML's role in computer programming education, this systematic review examines a wider range of applications across the entire pedagogical process. Analyzing 119 relevant research papers published between 2012 and 2024, this review offers an overview of AI and ML tools and techniques used in various educational contexts. Aligned with instructional design models, the reviewed literature is categorized into four key areas: course design, classroom implementation, assessment and feedback, and performance monitoring. This systematic review not only highlights the practical tools available to instructors but also identifies research trends and potential areas for future exploration in the field of computer programming education. • This SLR comprehensively analyzes how AI and ML enhance the pedagogical process in higher education computer programming courses. • After 2020, research has shifted from AI-powered performance monitoring…

Integrity signals

Registry

Crossref · 10.1016/j.caeai.2025.100403

Cites

200 of 200 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • When students lean too heavily on AI-generated code, their ability to develop core competencies such as logical reasoning, collaboration, and debugging may be compromised.

    “When students lean too heavily on AI-generated code, their ability to develop core competencies -such as logical reasoning, collaboration, and debugging -may be compromised (Groothuijsen et al., 2024;Manorat et al., 2025;Tedre et al., 2021).”

Cites

Flagged first, then by citations

7 of the 207 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Manorat, P., Tuarob, S., & Pongpaichet, S.
Affiliations
Mahidol University (TH)
Corresponding author
Pongpaichet, Siripen
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2025-06
Volume
8
Pages
100403
ISSN
2666-920X (ISSN-L)
OpenAlex
W4409150580
Cited by
27
References
207
Citations by year
2026: 21 · 2025: 6
FWCI
5.75
Type
journal article
Language
en
Open access
Locations
  • Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Mahidol University
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
artificial intelligence in education, programming education, machine learning in education, course design, classroom implementation, assessment and feedback, performance monitoring, deep learning, personalized learning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review