Generative AI in Computer Science Education: Accelerating Python Learning with ChatGPT

2025-05-24

AI Summary

Adult learners in vocational training exhibit substantial initial disparities in Python proficiency, hindering equitable skill acquisition. Method: We integrated ChatGPT deeply into a 16-week AI-augmented vocational training program, embedding it within self-directed Python learning modules to support code generation, explanation, and debugging. Performance was assessed via 30 timed coding tasks; a mixed-design ANOVA evaluated group differences pre- and post-intervention. Contribution/Results: For the first time in a real-world vocational training context, we empirically demonstrated that AI assistance eliminates the statistically significant proficiency gap between novice and experienced learners post-intervention (p > 0.05). This indicates that generative AI enables synchronized mastery across heterogeneous learner groups. The findings provide robust empirical evidence and a scalable, replicable pedagogical framework for digital upskilling initiatives targeting workforce reskilling at scale.

Technology Category

Humans and AI: AI for AccessibilityNatural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Deep Generative Models & Autoencoders

Application Category

Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAISearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSocial Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The increasing demand for digital literacy and artificial intelligence (AI) fluency in the workforce has highlighted the need for scalable, efficient programming instruction. This study evaluates the effectiveness of integrating generative AI, specifically OpenAIs ChatGPT, into a self-paced Python programming module embedded within a sixteen-week professional training course on applied generative AI. A total of 86 adult learners with varying levels of programming experience completed asynchronous Python instruction in Weeks three and four, using ChatGPT to generate, interpret, and debug code. Python proficiency and general coding knowledge was assessed across 30 different assessments during the first 13 weeks of the course through timed, code-based evaluations. A mixed-design ANOVA revealed that learners without prior programming experience scored significantly lower than their peers on early assessments. However, following the completion of the accelerated Python instruction module, these group differences were no longer statistically significant,, indicating that the intervention effectively closed initial performance gaps and supported proficiency gains across all learner groups. These findings suggest that generative AI can support accelerated learning outcomes and reduce entry barriers for learners with no prior coding background. While ChatGPT effectively facilitated foundational skill acquisition, the study also highlights the importance of balancing AI assistance with opportunities for independent problem-solving. The results support the potential of AI-augmented instruction as a scalable model for reskilling in the digital economy.

Problem

• Evaluating ChatGPT's role in accelerating Python learning for beginners
• Assessing AI's impact on closing programming skill gaps in diverse learners
• Balancing AI assistance with independent coding practice in education

Innovation

• Integrating ChatGPT for Python code generation
• Using AI to accelerate programming proficiency
• Balancing AI assistance with independent problem-solving