Work

Can LLM-driven synthetic participants help user research? A case study in designing augmented reality for education

Year: 2026

Type: article

Abstract: ABSTRACT: Conventional user research with human participants faces significant challenges, including substantial time and resource requirements, and limited scalability. In response, this study presents an efficient, cost-effective workflow driven by large language models (LLMs) for simulating user research with synthetic participants (SPs) at scale. In a case study in design augmented reality for education, SPs’ open-ended answers were plausible and comprehensive, yet semi-open and closed items diverged from those of humans. SPs can augment early qualitative work, but cannot replace human studies.

Source: Proceedings of the Design Society

DOI: 10.1017/pds.2026.10618

Authors: Suyi Xu, Rafael Roberto, Manoela Silva, Ziwei Gao, Qi Liu

Institutions: University of Bath, Serviço Nacional de Aprendizagem Industrial, Universidade de Pernambuco, University of Oxford

Language: English

Topic: Innovative Human-Technology Interaction

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid:

OpenAlex ID: W7167034652