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 chatbots 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 chatbots clarity, accessibility, and confidence-building impact, though critiques included occasional latency and restrictive code sanitization. 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.