Systematic analysis of communicative efficiency between rule-based chatbots and natural language models
Article Sidebar
Main Article Content
Abstract
The study was grounded in a systematic literature review on the communicative efficiencies of rule-based conversational agents and those powered by natural language models with artificial intelligence. A total of 175 documents were analyzed as the basis for this review. Additionally, a historical analysis of the first recorded conversational agent, ELIZA, developed in 1966, was included, highlighting its pivotal
role in the emergence of rule-based systems. The study also delved into the arguments underpinning the
significant differences between rule-based conversational agents and those leveraging natural language
models. These differences revealed that rule-based systems are simple and cost-effective tools, ideal for
repetitive and structured tasks, yet constrained in managing complex interactions. Conversely, agents
powered by natural language models enable more adaptive and personalized interactions, albeit requiring substantial investment in data and development. According to the findings, the choice between these approaches depends on the application context, available resources, and the specific needs of the organization. Furthermore, the research underscored the evolution of conversational agents and their transformative impact across various sectors. In this regard, the results open pathways to explore how emerging technological trends, such as advanced natural
language processing models, can enhance the efficiency and applicability of these systems while addressing the ethical and technical challenges associated with their implementation in diverse industries.
Article Details
Universitas-XXI Journal aligns with the principles of open access and academic collaboration by adopting the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). This license ensures that published content can be used and shared widely under the following conditions:
Attribution: Authors and the journal must be appropriately credited as the original creators of the content, with full reference and a link to the publication provided.
Non-commercial use: Content may not be used for commercial purposes, preserving its academic and educational intent.
ShareAlike: Derivative works must be distributed under the same terms as this license, promoting the creation of accessible and equitable knowledge.
By implementing this license, the Universitas-XXI Journal strengthens its commitment to disseminating high-quality research, providing free access to knowledge, and fostering a collaborative environment among researchers, educators, and students worldwide.
This decision reflects core values of responsibility, ethics, and transparency in academia, ensuring that intellectual contributions serve as a foundation for new research and projects while respecting the rights of authors and the scientific community.
For more information about this license, please visit the following link: https://creativecommons.org/licenses/by-nc-sa/4.0/"
References
Abu Shawar, B., & Atwell, E. (2007). Chatbots: Are They Really Useful? LOAIT 2007 - International Workshop on Legal Ontologies and Artificial Intelligence Techniques, 29-40.
Amalia, R. S., & Suprayogi, M. (2019). Ensuring data correctness and bias-free AI functionalities in chatbots. Emerald Insight. Recuperado de https://www.emerald.com/insight/content/doi/10.1108/JHTT-02-2019-0021/full/html
AirDroid. (2024). Rule-Based vs. AI Chatbot: Which One is Better? Recuperado de www.airdroid.com
Badr, Y. (2024). Evolution of large language models and their role in shaping general artificial intelligence. Digital Transformation and Society, 3(1), 1-2. https://doi.org/10.1108/DTS-02-2024-088
Bhattacharyya, S.S. (2024). Study of adoption of artificial intelligence technology-driven natural large language model-based chatbots by firms for customer service interaction. Journal of Science and Technology Policy Management. https://doi.org/10.1108/JSTPM-11-2023-0201
Buhalis, D., & Yen, C. L. (2020). Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies. Emerald Insight. Recuperado de https://www.emerald.com/insight/content/doi/10.1108/JHTT-03-2020-0039/full/html
Caldarini, G., Jaf, S., & McGarry, K. (2022). A Literature Survey of Recent Advances in Chatbots. Information, 13(1), 41. https://doi.org/10.3390/info13010041
Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). Increasing customer service efficiency through artificial intelligence chatbot. Emerald Insight.
Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Human-Chatbot Interaction Design. International Journal of Human-Computer Interaction, 37(8), 729-758.
Dale, R. (2020). The return of the chatbots: The latest advances and challenges in chatbot technology. Natural Language Engineering, 26(6), 811-832.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186. https://doi.org/10.18653/v1/N19-1423
El-Ateif, S., Idri, A., & Fernández-Alemán, J.L. (2024). On the differences between CNNs and vision transformers for COVID-19 diagnosis using CT and chest x-ray mono- and multimodality. Data Technologies and Applications, ahead-of-print. https://doi.org/10.1108/DTA-01-2023-0005
Emma Goldman. (2017, septiembre 29). Antes de Siri y Alexa, estaba ELIZA [Video]. YouTube. https://www.youtube.com/watch?v=RMK9AphfLco
Gnewuch, U., Morana, S., & Maedche, A. (2021). Designing Conversational Agents for Services: Reviewing Design Principles and Evaluating Their Effectiveness. Journal of the Association for Information Systems, 22(1), 163-204.
Hartmann, J., & Netzer, O. (2023). Natural Language Processing in Marketing. En K. Sudhir & O. Toubia (Eds.), Artificial Intelligence in Marketing (Vol. 20, pp. 191-215). Emerald Publishing Limited.
Hill, J., Randolph Ford, W., & Farreras, I. G. (2015). Real Conversations with Artificial Intelligence: A Comparison between Human–Human Online Conversations and Human–Chatbot Conversations. Computers in Human Behavior, 49, 245-250.
Hoyer, W.D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Service Research, 23(1), 3-24.
Hu, X., et al. (2018). Comparative study on the effectiveness of rule-based vs. PLN-based chatbots in tourism. Emerald Insight. Recuperado de https://www.emerald.com/insight/content/doi/10.1108/JHTT-04-2020-0050/full/html
Huang, Y., & Gursoy, D. (2024). Customers’ online service encounter satisfaction with chatbots: interaction effects of language style and decision-making journey stage. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-11-2023-1800
Hubtype. (2023). Rule-Based Chatbots vs. AI Chatbots: Key Differences. Recuperado de www.hubtype.com
Jindal, P., et al. (2020). Business impacts of NLP-based chatbot implementation in customer service. Emerald Insight. Recuperado de https://www.emerald.com/insight/content/doi/10.1108/JHTT-05-2019-0033/full/html
Kwartler, T. (2021). Text Analytics and Natural Language Processing. En M. Einhorn, M. Löffler, E. de Bellis, A. Herrmann, & P. Burghartz (Eds.), The Machine Age of Customer Insight (pp. 119-128). Emerald Publishing Limited.
Laranjo, L., Dunn, A. G., Tong, H. L., & Bashir, R. (2018). Conversational Agents in Healthcare: A Systematic Review. Journal of the American Medical Informatics Association, 25(9), 1248-1258.
Lara, D. C. P., & Gómez, V. J. G. (2020). Metodologías activas para la enseñanza y aprendizaje de los estudios sociales. Sociedad & Tecnología, 3(2), 2-10.
Ledro, C., Nosella, A. and Vinelli, A. (2022), "Artificial intelligence in customer relationship management: literature review and future research directions", Journal of Business & Industrial Marketing, Vol. 37 No. 13, pp. 48-63. https://doi.org/10.1108/JBIM-07-2021-0332
Ling, C., Nasir, M.H., & Pitra, Z. (2021). Understanding customer experience with AI-driven chatbots: a dual-process theory perspective. Journal of Business Research, 129, 170-183.
Liu, B., & Lane, I. (2016). Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling. arXiv preprint arXiv:1609.01454.
López, N. V., González, N. A., & Rodríguez, C. C. (2021). Implementation of Chatbot in Online Commerce, and Open Innovation. Journal of Open
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact
Nicolescu, L., & Tudorache, M. T. (2022). Human-computer interaction in customer service: the experience with AI chatbots—a systematic literature review. Electronics, 11(10), 1579.
Paschek, D., Luminosu, C.T., & Draghici, A. (2017). Automating customer service through chatbots. Procedia Computer Science, 121, 350-358.
Pradella, C. (2013). Evaluating chatbot usability for improving business processes. International Journal of Business Management, 8(4), 45-60.
Pucci, V. R., Machado, R. A., Cardano, M., Kantorski, L. P., & Weiller, T. H. (2020). Técnica da entrevista discursiva em pesquisa qualitativa: relato de experiência. Rev. enferm. UFSM, e97-e97.
Silverio, M. (2024, mayo 17). ChatGPT: número de usuarios y estadísticas. PrimeWeb. Recuperado de https://www.primeweb.com.mx/chatgpt-usuarios-estadisticas
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30.
Wang, X., & Jiang, Z. (2020). Recent Advances in Neural Chatbots. International Journal of Automation and Computing, 17(2), 251-266.
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.
Xue, J., Wang, Y.-C., Wei, C., Liu, X., Woo, J., & Kuo, C.-C. J. (2024). Bias and fairness in chatbots: An overview. APSIPA Transactions on Signal and Information Processing, 13(2), e102. http://dx.doi.org/10.1561/116.00000064