Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base

Abstract: This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX) and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD guidelines for LLMs. Following this, a preliminary experiment is presented as an example to demonstrate how HCD principles can be employed to enhance user experience within GPT by using a single document input to GPT’s Knowledge base as new knowledge resource to control the interactions between GPT and users, aiming to meet the diverse needs of hypothetical software learners as much as possible. The experimental results demonstrate the effect of different elements’ forms and organizational methods in the document, as well as GPT’s relevant configurations, on the interaction effectiveness between GPT and software learners. A series of trials are conducted to explore better methods to realize text and image displaying, and jump action. Two template documents are compared in the aspects of the performances of the four interaction modes.

Introduction. Since the emergence of Large Language Models (LLMs) and blowout development from 2022, their integration with Human-computer interaction (HCI) marks the beginning of a new chapter in this interaction. This shift heralds a shift away from traditional HCI, which primarily relied on graphical user interfaces and command-line inputs, toward more sophisticated AI-driven interfaces and models. As Gokul [1] points out, LLMs are reshaping the Artificial Intelligence (AI) landscape with their advanced capabilities in processing and generating human-like language. Their applications extend into various creative domains, including music, art, and storytelling. However, in the aspect of user experience (UX), the LLMs and their applications still present challenges.

Discussion / Conclusion. Some findings in the series of experiments include: (1) Changing interaction modes while using a shared knowledge part is not an easy task. The more users’ needs integrated, the more difficult the organization the (5) GPT tends to use NLP to give responses, unless there are explicit steps with high weight to force it give original content from the document. Even within “print ( )” function, sometimes GPT refuse to give irrelevant contents and use NLP to change them. This study concludes HCD guidelines in LLMs and tries to integrate them into an experiment of using a single document as new knowledge in GPT to meet user’s diverse software learning needs. It is found that without high-level code, it is not easy to integrate all diverse needs perfectly into one GPT. The natural language characteristic of GPT is generally a merit for comprehensively understand the document and user’s input, while in some cases, becomes an interference of proceeding mixed steps to generate preset content and creative content together, which may need preset components and workflow inside GPT with higher control level.