Abstract

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.

Keywords

  • Generative Syntactic Analysis
  • TensorFlow
  • Learning Rates
  • Chatbot System
  • C++ Programming Education

References

  1. 1. Buczak, Philip & Groll, Andreas & Pauly, Markus & Rehof, Jakob & Horn, Daniel. (2024). Using sequential statistical tests for efficient hyperparameter tuning. AStA Advances in Statistical Analysis. 108. 10.1007/s10182-024-00495-1.
  2. 2. Chaves, A. P., & Gerosa, M. A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human-Computer Interaction, 37, 729–758.
  3. 3. Coskun-Setirek, A., & Mardikyan, S. (2017). Understanding the adoption of voice-activated personal assistants. International Journal of E-Services and Mobile Applications, 9(3), 1–21.
  4. 4. Gaczek, P., Leszczyński, G., & Zieliński, M. (2022). Is AI Augmenting or Substituting Humans?: An Eye-Tracking Study of Visual Attention Toward Health Application. International Journal of Technology and Human Interaction, 18(1), 1-14.
  5. 5. Goldberg, Yoav. (2017). Neural Network Methods for Natural Language Processing. Synthesis Lectures on Human Language Technologies. 10. 1-309. 10.2200/S00762ED1V01Y201703HLT037.
  6. 6. Khan, R., & Das, A. (2018). Build Better Chatbots. A complete guide to getting started with chatbots. Apress.
  7. 7. Lewis, James & Sauro, Jeff. (2021). Usability and User Experience: Design and Evaluation. 10.1002/9781119636113.ch38.
  8. 8. Leipzig J, Nüst D, Hoyt CT, Ram K, Greenberg J. The role of metadata in reproducible computational research. Patterns (N Y). 2021 Sep 10;2(9):100322. doi: 10.1016/j.patter.2021.100322. PMID: 34553169; PMCID: PMC8441584.
  9. 9. Ling, E. C., Tussyadiah, I., & Stienmetz, J. (2023). Perceived Intelligence of Artificially Intelligent Assistants for Travel: Scale Development and Validation. Journal Title, OnlineFirst. https://doi.org/10.1177/00472875231217899
  10. 10. Nirala, K. K., Singh, N. K., & Purani, V. S. (2022). A survey on providing customer and
  11. 11. public administration-based services using AI: Chatbot. Multimedia Tools and Applications, 81(16), 22215-22246. https://doi.org/10.1007/s11042-021-11191-5
  12. 12. Pawar, V. V., & Vanjare, C. (2024). AI-powered chatbots reshaping dentistry: Opportunities, challenges, and future directions. Oral Oncology Reports, 9, Article 100156. https://doi.org/10.1016/j.oor.2024.100156
  13. 13. Ranieri, A., Bernardo, I., Mele, C. Serving customers through chatbots: positive and negative effects on customer experience. Journal of Service Theory and Practice 13 March 2024; 34 (2): 191–215. https://doi.org/10.1108/JSTP-01-2023-0015
  14. 14. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict European Journal of Marketing, 53(11), 2322–2347.
  15. 15. Smith, E. M., Williamson, M., Shuster, K., Weston, J., & Boureau, Y. L. (2020). Can you put it all together: Evaluating conversational agents' ability to blend skills. arXiv preprint arXiv:2004.08449.
  16. 16. Uzoka, Abel & Cadet, Emmanuel & Ojukwu, Pascal. (2024). Leveraging AI-Powered chatbots to enhance customer service efficiency and future opportunities in automated support. Computer Science & IT Research Journal. 5. 2485-2510. 10.51594/csitrj.v5i10.1676.