Designing intelligent chatbots with ChatGPT: a framework for development and implementation
Abstract
Background: The rapid evolution of interactive AI has reshaped human-computer interaction, with ChatGPT emerging as a key tool for chatbot development. Industries such as healthcare, customer service, and education increasingly integrate chatbots, highlighting the need for a structured development framework.
Purpose: This study proposes a framework for designing intelligent chatbots using ChatGPT, focusing on user experience, hybrid design models, prompt engineering, and system limitations. The framework aims to bridge the gap between technical innovation and real-world application.
Methods: A systematic literature review (SLR) was conducted, analyzing 40 relevant studies. The research was structured around three key questions: (1) How do user experience and engagement influence chatbot performance? (2) How do hybrid design models improve chatbot performance? (3) What are the limitations of using ChatGPT, and how does prompt engineering affect responses?
Results: The findings emphasize that well-designed user interactions enhance engagement and trust. Hybrid models integrating rule-based and machine learning techniques improve chatbot functionality. However, challenges such as response inconsistencies, ethical concerns, and prompt sensitivity require careful consideration. A framework for design, development, and implementation of effective Chatbots with ChatGPT has been proposed in this study.
Conclusion: This study provides a structured framework for chatbot development…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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