(untitled) (International Journal of Engineering and Computer Science, 2025)
This study explores generative syntactic analysis using TensorFlow, specifically applied to C++ programming education. Its dual objectives were to analyze the influence of varied learning rates on generative intelligent model performance and to evaluate the user experience of a developed chatbot system. Key findings indicate that learning rates play a critical role in model training, with a rate of 0.01 consistently yielding superior perplexity and BLEU scores compared to a rate of 0.1, while also acknowledging the importance of dataset and task specificity. User feedback revealed a largely positive reception (4.2/5) for the chatbot system, attributed to its intuitive interface and ease of use. However, opportunities for enhancement were identified, particularly in aligning terminology with industry standards and improving documentation. This research significantly contributes to AI-assisted education by offering insights into both technical advancements and user-centric refinements for intelligent generative models.