PyChatAI: Enhancing Python Programming Skills—An Empirical Study of a Smart Learning System
Abstract
This paper presents strategies for effectively integrating AI tools into programming education and provides recommendations for enhancing student learning outcomes through intelligent educational systems. Learning computer programming is a cognitively demanding task that requires dedication, logical reasoning, and persistence. Many beginners struggle with debugging and often lack effective problem-solving strategies. To address these issues, this study investigates PyChatAI—a bilingual, AI-powered chatbot designed to support novice Python programmers by providing real-time feedback, answering coding-related questions, and fostering independent problem-solving skills. PyChatAI offers continuous, personalised assistance and is particularly beneficial for students who prefer remote or low-pressure learning environments. An empirical evaluation employing a Solomon Four-Group design revealed significant improvements across all programming skill areas, with especially strong gains in theoretical understanding, code writing, and debugging proficiency.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080 · see citing works
Does ChatGPT Help Novice Programmers Write Better Code? Results From Static Code Analysis
Haindl, P., & Weinberger, G. (2024) · IEEE Access · doi:10.1109/access.2024.3445432 · Cited by 25 · see citing works
Exploring ChatGPT's Potential and Concerns in Higher Education
Tick, A. (2024) · 2024 IEEE 22nd Jubilee International Symposium on Intelligent Systems and Informatics (SISY) · doi:10.1109/sisy62279.2024.10737595 · Cited by 8 · see citing works
Flagged first, then by citations
- Authors
- Alanazi, M., Soh, B., Samra, H., & Li, A.
- Venue
- Computers
- Publisher
- MDPI AG
- Published
- 2025-04-23
- Volume
- 14
- Issue
- 5
- Pages
- 158
- ISSN
- 2073-431X
- Cited by
- 11
- References
- 3
- Language
- en
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