@article{10.11648/j.se.20251101.11,
  author = {Sayed Mahbub Hasan Amiri and Md Mainul Islam},
  title = {Enhancing Python Programming Education with an AI-Powered Code Helper: Design, Implementation, and Impact
},
  journal = {Software Engineering},
  volume = {11},
  number = {1},
  pages = {1-17},
  doi = {10.11648/j.se.20251101.11},
  url = {https://doi.org/10.11648/j.se.20251101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20251101.11},
  abstract = {This is the study that presents an AI-Python-based chatbot that helps students to learn programming by demonstrating solutions to such problems as debugging errors, solving syntax problems or converting abstract theoretical concepts to practical implementations. Traditional coding tools like Integrated Development Environments (IDEs) and static analyzers do not give robotic help while AI-driven code assistants such as GitHub Copilot focus on getting things done. To close this gap, our chatbot combines static code analysis, dynamic execution tracing, and large language models (LLMs) to provide the students with relevant and practical advice, hence promoting the learning process. The chatbot’s hybrid architecture employs CodeLlama for code embedding, GPT-4 for natural language interactions, and Docker-based sandboxing for secure execution. Evaluated through a mixed-methods approach involving 1,500 student submissions, the system demonstrated an 85% error resolution success rate, outperforming standalone tools like pylint (62%) and GPT-4 (73%). Quantitative results revealed a 59.3% reduction in debugging time among users, with pre- and post-test assessments showing a 34% improvement in coding proficiency, particularly in recursion and exception handling. Qualitative feedback from 120 students highlighted the chatbot’s clarity, accessibility, and confidence-building impact, though critiques included occasional latency and restrictive code sanitization. Emphasizing the ethical aspects of the project, the bias principle led to the discrimination of gendered reasons for 83% and the GDPR-iPad-like procedures to anonymity were followed. The chatbot's productivity points to its ability to make coding education available to everyone and to give 24/7 aid to students in some not well-funded schools. Future work will expand multilingual support through localized datasets and culturally adapted examples, integrate gamification to enhance engagement, and develop collaborative learning features. By balancing technical innovation with pedagogical empathy, this research provides a blueprint for AI tools that prioritize educational equity and long-term skill retention over mere code completion. The chatbot exemplifies how AI can augment human instruction, fostering deeper conceptual understanding in programming education.
},
 year = {2025}
}

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TY  - JOUR
T1  - Enhancing Python Programming Education with an AI-Powered Code Helper: Design, Implementation, and Impact

AU  - Sayed Mahbub Hasan Amiri
AU  - Md Mainul Islam
Y1  - 2025/04/28
PY  - 2025
N1  - https://doi.org/10.11648/j.se.20251101.11
DO  - 10.11648/j.se.20251101.11
T2  - Software Engineering
JF  - Software Engineering
JO  - Software Engineering
SP  - 1
EP  - 17
PB  - Science Publishing Group
SN  - 2376-8037
UR  - https://doi.org/10.11648/j.se.20251101.11
AB  - This is the study that presents an AI-Python-based chatbot that helps students to learn programming by demonstrating solutions to such problems as debugging errors, solving syntax problems or converting abstract theoretical concepts to practical implementations. Traditional coding tools like Integrated Development Environments (IDEs) and static analyzers do not give robotic help while AI-driven code assistants such as GitHub Copilot focus on getting things done. To close this gap, our chatbot combines static code analysis, dynamic execution tracing, and large language models (LLMs) to provide the students with relevant and practical advice, hence promoting the learning process. The chatbot’s hybrid architecture employs CodeLlama for code embedding, GPT-4 for natural language interactions, and Docker-based sandboxing for secure execution. Evaluated through a mixed-methods approach involving 1,500 student submissions, the system demonstrated an 85% error resolution success rate, outperforming standalone tools like pylint (62%) and GPT-4 (73%). Quantitative results revealed a 59.3% reduction in debugging time among users, with pre- and post-test assessments showing a 34% improvement in coding proficiency, particularly in recursion and exception handling. Qualitative feedback from 120 students highlighted the chatbot’s clarity, accessibility, and confidence-building impact, though critiques included occasional latency and restrictive code sanitization. Emphasizing the ethical aspects of the project, the bias principle led to the discrimination of gendered reasons for 83% and the GDPR-iPad-like procedures to anonymity were followed. The chatbot's productivity points to its ability to make coding education available to everyone and to give 24/7 aid to students in some not well-funded schools. Future work will expand multilingual support through localized datasets and culturally adapted examples, integrate gamification to enhance engagement, and develop collaborative learning features. By balancing technical innovation with pedagogical empathy, this research provides a blueprint for AI tools that prioritize educational equity and long-term skill retention over mere code completion. The chatbot exemplifies how AI can augment human instruction, fostering deeper conceptual understanding in programming education.

VL  - 11
IS  - 1
ER  - 

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