Artificial intelligence in computer programming education: A systematic literature review
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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…
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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).”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Manorat et al., 2025
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Convolutional Neural Network Applied to Code Assignment Grading
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Using Programming Autograder Formative Data to Understand Student Growth
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Real-time Metacognition Feedback for Introductory Programming Using Machine Learning
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2018 Majan International Conference (MIC)
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Prediction of student academic progression: A case study on Vignan University
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Identifying typical approaches and errors in Prolog programming with argument-based machine learning
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Adaptive multilevel clustering model for the prediction of academic risk
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CLG clustering for dropout prediction using log-data clustering method
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Proceedings of the 21st International Conference on Computers in Education (ICCE 2013)
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Bayesian network analysis of computer science grade distributions
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Investigating the Applicability of the Normalized Programming State Model to BlueJ Programmers
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Model for Evaluating Student Performance Through Their Interaction With Version Control Systems.
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Determination of competency of programmers by classification and ranking using AHP
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Identifying an original copy of the source codes in programming assignments
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The First International Conference on AI-ML-Systems
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REFERENT: Transformer-based Feedback Generation using Assignment Information for Programming Course
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Explainable AI for Workflow Verification in VIPLE
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The Reform and Exploration of Intelligent PYTHON Language Teaching
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Speeding Up Automated Assessment of Programming Exercises
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A New Model for Semiautomatic Student Source Code Assessment
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Predicting Students’ Performance and Problem Solving Behavior from iList Log Data
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Source Code based Approaches to Automate Marking in Programming Assignments
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Analysis of Student Pair Teamwork Using GitHub Activities
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Finding an Effective Data Mining Algorithm for Automatic Detection of Learning Styles
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Evaluating Distance Measures for Program Repair
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Utilizing Automated Scaffolding Strategies to Improve Students' Reflections Writing Process
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Lobanov, A., Bryksin, T., & Shpilman, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23207-8_33 · Cited by 3 · see citing works
Machine Learning based Code Assessment Systems
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Clusters of Programming Exercises Difficulties Resolvers in a MOOC
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Automatic Generation of Matching Rules for Programming Exercise Assessment
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Plaza, L., Araujo, L., López-Ostenero, F., & Martínez-Romo, J. (2023) · Applied System Innovation · doi:10.3390/asi6050083 · Cited by 2 · see citing works
Liu, X., Castellanos, H., Wiese, L. J., & Magana, A. J. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10342972 · Cited by 2 · see citing works
Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming
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Poon, L. K. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14815-7_32 · Cited by 2 · see citing works
Zhao, X. (2022) · International Conference on Automation, Robotics and Computer Engineering (ICARCE) · doi:10.1109/icarce55724.2022.10046617 · Cited by 2 · see citing works
Monitoring Programming Styles in Massive Open Online Courses Using Source Embedding
Matsrostefano, S., & Sciarrone, F. (2022) · International Conference on Information Visualisation (IV) · doi:10.1109/iv56949.2022.00049 · Cited by 2 · see citing works
Personalized Programming Guidance Based on Deep Programming Learning Style Capturing
Liu, Y., Zhu, R., & Gao, M. Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-0730-0_20 · Cited by 2 · see citing works
Agile Teaching: Automated Student Support and Feedback Generation
Bahrehvar, M., & Moshirpour, M. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343212 · Cited by 1 · see citing works
DEMAx Tool Based on an Improved Model for Semiautomatic C/C++ Source Code Assessment
Angelovski, D., Stankov, E., & Jovanov, M. (2021) · International Conference on Information and Education Innovations (ICIEI) · doi:10.1145/3470716.3470728 · Cited by 1 · see citing works
YONA: An Intelligence Question-Answering System Based on Artificial Intelligence
Huang, Z. S. (2023) · International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) · doi:10.1109/ichci58871.2023.10277702 · Cited by 1 · see citing works
Programmer Performance Prediction with Cognitive Tests: A Granular Approach
Aboulmagd, A., & Elkafrawy, P. (2023) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc58260.2023.10323743 · Cited by 1 · see citing works
Arhandi, P. P., Firdausi, A. T., Andoko, B. S., & Masykuro, N. S. A. Q. (2023) · International Conference on Vocational Education and Electrical Engineering (ICVEE) · doi:10.1109/icvee59738.2023.10348365 · Cited by 1 · see citing works
Flagged first, then by citations
7 of the 207 references couldn’t be matched to a known paper.
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“Despite their growing adoption, the effects of LLMs on students’ programming processes and learning outcomes remain underexplored (Manorat et al., 2025; Slimi, 2023; Sun et al., 2024).”
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An AI-Driven System for Learning MQTT Communication Protocols with Python Programming
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Future-Ready Learning: Emerging Tech Shaping the Evolution of Computer Science Education
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AI cognitive laziness in programming learning among university students
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Integrating Formal Methods and Large Language Models in Computer Engineering Education
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Multi-Course Integration Framework Based on Subject Knowledge Graphs
Yu, D., & Zhuang, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462843
Knowledge Management in Open Distance E-Learning Programmes
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A Study of Enhancing C Programming Education through AIGC-Integrated Hybrid Learning
Wu, Z., & Du, H. (2026) · International Conference on Educational and Information Technology · doi:10.1109/iceit68991.2026.11521481
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İnşaat Sahalarında Güvenlik Kaskı Algılama için YOLO Algoritmalarının Performans Analizi
Sağlıyan, M., & Özcan, A. (2026) · Afyon Kocatepe University Journal of Sciences and Engineering · doi:10.35414/akufemubid.1799653
Large language models in software development learning
Hernández, S. C., Cruz, M. P. T., Sánchez, J. M. E., Luna-Gómez, A. A., Orozco, A. M. P., & Medina, R. H. (2026) · Discover Education · doi:10.1007/s44217-026-01750-0
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A Project-Based Learning Framework for Nonlinear Systems and Control
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AI-Powered Intelligent Programming Tutor: Adaptive Learning and Auto-Evaluation
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- 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
- gold
- doi.org
- Licence
- 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 ›
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