From Prompts to Preferences: An Open-Source Platform for Generative AI-Enhanced Conjoint Analysis

2026-06-11

AI Summary

This study addresses the lack of low-cost, end-to-end tools for jointly generating diverse textual and visual stimuli in behavioral research. To bridge this gap, we propose an open-source, self-hostable web platform that uniquely integrates large language models with text-to-image models within a unified analytical workflow. The platform enables researchers to generate customized stimuli through parameterized prompts, while offering attribute suggestions and real-time analytics to lower usability barriers. Designed with a structured wizard interface and modular architecture, it supports full export of data and prompts, thereby enhancing research transparency and reproducibility. Its efficacy has been empirically validated in a study on preferences for ambient assisted living care robots (N=55), demonstrating its capacity to streamline stimulus creation and analysis in experimental settings.

Abstract

Conjoint analysis is a widely used preference measurement method in marketing research, political science, healthcare, and human-computer interaction. Despite broad adoption, researchers without access to commercial platforms face significant barriers, as existing tools are either expensive or lack end-to-end survey infrastructure. This paper presents an open-source, self-hosted web application for designing, deploying, and analysing conjoint surveys. Beyond conventional tabular stimuli, the platform uses generative AI to produce integrated stimuli formats: textual scenario descriptions generated by a large language model, and visual stimuli by a text-to-image model. A researcher-defined base prompt is parameterised with the conjoint profile, and optional LLM-facing level annotations enrich the generation. A structured setup wizard, AI-assisted attribute suggestion, and live data analysis lower the technical barriers for researchers new to conjoint methodology. A full export bundle including all stimuli, their generating prompts, and response data facilitates transparency and reproducibility. The platform is demonstrated through a proof-of-concept study on care robot preferences for ambient assisted living (AAL, N=55) using AI-generated visual stimuli. The paper discusses the role of AI assistance in conjoint design, arguing that theoretical grounding must remain the researcher's responsibility, and outlining how genAI-generated stimuli can broaden the methodological repertoire for HCI and related fields.

Problem

• conjoint analysis
• open-source platform
• generative AI
• survey infrastructure
• preference measurement

Innovation

• generative AI
• conjoint analysis
• open-source platform
• AI-generated stimuli
• large language model