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

Yuchen  Wang; Yin-Shan  Lin; Ruixin  Huang; Jinyin  Wang; Sensen  Liu

doi:10.59400/cai.v2i1.535

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

Yuchen Wang School of Architecture, University of Hawaii at Manoa, 2410 Campus Road, Honolulu, HI 96822, United States
Yin-Shan Lin Khoury College of Computer Science, Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States
Ruixin Huang Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, United States
Jinyin Wang Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, United States
Sensen Liu Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis, MO 63130, United States

Ariticle ID: 535

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DOI: https://doi.org/10.59400/cai.v2i1.535

Keywords: Large Language Models (LLMs); Human-Computer Interaction (HCI); User Experience (UX); Human-Centered Design (HCD); GPTs; knowledge base; user needs

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. Through continuous optimization, an improved version of the document was obtained to serve as a template for future use and research.

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Published

2024-04-19

How to Cite

Wang, Y., Lin, Y.-S., Huang, R., Wang, J., & Liu, S. (2024). 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. Computing and Artificial Intelligence, 2(1), 535. https://doi.org/10.59400/cai.v2i1.535

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