A Day in Their Shoes: Using LLM-Based Perspective-Taking Interactive Fiction to Reduce Stigma Toward Dirty Work

2025-05-09

AI Summary

Occupational stigma against “dirty work” professions (e.g., sanitation workers, funeral directors) adversely affects practitioners’ psychological well-being. Method: This study introduces an LLM-driven interactive fiction intervention system featuring a novel perspective-shifting narrative framework that enables embodied role-playing—transforming conventional one-way education into an emotion-immersive, cognitively reconstructive anti-stigma approach. A mixed-methods evaluation (N=100 quantitative experiment + 15 in-depth interviews) was conducted. Contribution/Results: Findings demonstrate significant improvements in participants’ occupational understanding and empathic capacity. Qualitative analysis confirms the system’s efficacy in enhancing narrative immersion, emotional resonance, and identification with professional value. This work establishes a scalable, psychologically effective, and technology-augmented paradigm for occupational stigma intervention.

Technology Category

• Humans and AI: Emotional Intelligence
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

Occupations referred to as"dirty work"often face entrenched social stigma, which adversely affects the mental health of workers in these fields and impedes occupational equity. In this study, we propose a novel Interactive Fiction (IF) framework powered by Large Language Models (LLMs) to encourage perspective-taking and reduce biases against these stigmatized yet essential roles. Through an experiment with participants (n = 100) across four such occupations, we observed a significant increase in participants' understanding of these occupations, as well as a high level of empathy and a strong sense of connection to individuals in these roles. Additionally, qualitative interviews with participants (n = 15) revealed that the LLM-based perspective-taking IF enhanced immersion, deepened emotional resonance and empathy toward"dirty work,"and allowed participants to experience a sense of professional fulfillment in these occupations. However, participants also highlighted ongoing challenges, such as limited contextual details generated by the LLM and the unintentional reinforcement of existing stereotypes. Overall, our findings underscore that an LLM-based perspective-taking IF framework offers a promising and scalable strategy for mitigating stigma and promoting social equity in marginalized professions.

Problem

• Reducing social stigma toward dirty work occupations
• Enhancing empathy through LLM-based interactive fiction
• Addressing biases and promoting equity in marginalized professions

Innovation

• LLM-based Interactive Fiction for perspective-taking
• Enhances empathy through immersive storytelling
• Scalable strategy to reduce occupational stigma