Análisis sistemático sobre la eficiencia comunicativa entre chatbots basados en reglas y modelos de lenguaje natural

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 pivotalrole in the emergence of rule-based systems. The study also delved into the arguments underpinning thesignificant differences between rule-based conversational agents and those leveraging natural languagemodels. These differences revealed that rule-based systems are simple and cost-effective tools, ideal forrepetitive and structured tasks, yet constrained in managing complex interactions. Conversely, agentspowered 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 naturallanguage 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.