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Updated: Jul 4, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Leveraging Large Language Models in User eXperience (UX) Education when Real Users Are Unavailable.

Panos Bonotis1, Pantelis Angelidis1

  • 1Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

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This study explored using Large Language Models (LLMs) like ChatGPT to simulate user perspectives in User eXperience (UX) design workshops when real users are unavailable. LLMs can aid UX education by offering practice in human-centered methods, especially in healthcare.

Area of Science:

  • Human-Computer Interaction
  • Educational Technology
  • Healthcare Design

Background:

  • User eXperience (UX) design necessitates direct user engagement, particularly in healthcare, to understand patient and clinician needs.
  • Ethical and logistical challenges frequently impede early-stage user access in research.
  • Traditional UX methods face limitations when real user interaction is not feasible.

Purpose of the Study:

  • To investigate the utility of Large Language Models (LLMs) as a tool for simulating user perspectives in UX design education.
  • To assess the effectiveness of LLM-generated simulated user data in developing UX artifacts like proto-personas and empathy maps.
  • To evaluate the potential of LLMs to enhance UX design education, especially in resource-constrained or access-limited environments like healthcare.

Main Methods:

Keywords:
ChatGPTHuman-Centered DesignLarge Language Models (LLMs)UX Education

Related Experiment Videos

Last Updated: Jul 4, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • A UX workshop was conducted with undergraduate engineering students.
  • A Large Language Model (ChatGPT) was employed to simulate patient and clinician user perspectives.
  • Students developed prompts to guide the LLM in generating realistic user responses.
  • LLM outputs were utilized to create proto-personas, empathy maps, and initial design concepts.

Main Results:

  • Students successfully used LLM outputs to create foundational UX design artifacts.
  • The simulation provided a practical environment for students to practice human-centered design methods.
  • The approach offered a viable alternative for UX education when direct user access was limited.
  • Potential biases inherent in LLMs were acknowledged as a limitation.

Conclusions:

  • Large Language Models can serve as a valuable supplementary tool in UX design education, particularly for simulating user perspectives.
  • Thoughtful integration of LLMs can enhance student learning of human-centered design principles and prepare them for real-world challenges.
  • While LLMs cannot substitute for authentic user research, they offer a pragmatic solution for overcoming access barriers in educational settings.
  • Further research is warranted to explore the mitigation of LLM-induced biases in UX design.