Development of Intelligent Chatbots Using Python and OpenAI for Messaging Automation and Contextual Response in Educational and Business Applications

Abstract

Abstract

Advancements in natural language processing (NLP) technologies and machine learning methods have enabled the development of tools that facilitate more sophisticated interactions between humans and machines (Adamopoulou & Moussiades, 2020b; Radford et al., 2018). This study focuses on integrating Selenium and OpenAI's GPT models in the creation of an intelligent chatbot, designed to streamline the automation of web messaging interactions while providing appropriate responses in educational and corporate applications (Koundinya et al., 2020; Lee et al., 2024).

The evaluated objective was to create a system capable of efficiently handling extensive interactions autonomously, reducing response times, and ensuring consistency in the generated replies, while overcoming the limitations of more traditional automation methods or standalone NLP techniques (Lavrinovics et al., 2024). The strategy was based on software engineering principles and agile development techniques, employing Python and Selenium for task automation, alongside GPT-3.5 and GPT-4 models for the NLP component (Radford et al., 2018; Freed, 2021).

Analyses conducted, which included validation through accuracy and loss metrics, incorporated response times and were performed under controlled conditions using both real and simulated data (Batani et al., 2024; Caballero Castellanos et al., 2023). The findings revealed an accuracy rate of 97.54% and automation of 85% in interactions on platforms such as WhatsApp Web, with an average response time of 3 seconds. This represents a significant improvement compared to current standards of efficiency and scalability (Lee, Bubeck, & Petro, 2023).

It can be concluded that the combination of these technologies has proven to be an effective and valuable solution with potential applications in personalized education, customer service, and automated support systems (Chiu et al., 2023; Kesarwani et al., 2023). This suggests opportunities for future research focused on multimodal capabilities or a more rigorous analysis of user experience (Hill, Randolph Ford, & Farreras, 2015; Vaswani et al., 2017).

Keywords: Automation, artificial intelligence, contextual interaction, digital education, virtual platforms