PerspectiveCoach: Exploring LLMs for Developer Reflection

2026-01-05

arXiv.org

AI Summary

This work proposes PerspectiveCoach, the first large language model (LLM)-based conversational tool designed to help software developers systematically reflect on the ethical implications of their designs for marginalized groups. By guiding developers through structured perspective-taking exercises, PerspectiveCoach fosters deeper understanding of end users’ lived experiences and promotes ethical self-reflection. The system introduces two novel design principles—“enhanced adaptivity” and “pluralistic centrality”—to operationalize an end-user-centered perspective-taking mechanism. Empirical evaluation demonstrates that the tool significantly enhances developers’ self-awareness, breadth of perspective, and nuance in ethical reasoning. Furthermore, it exhibits strong performance in usability, relevance, and accuracy in restating user concerns across multi-turn interactions.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Despite growing awareness of ethical challenges in software development, practitioners still lack structured tools that help them critically engage with the lived experiences of marginalized users. This paper presents PerspectiveCoach, a large language model (LLM)-powered conversational tool designed to guide developers through structured perspective-taking exercises and deepen critical reflection on how software design decisions affect marginalized communities. Through a controlled study with 18 front-end developers (balanced by sex), who interacted with the tool using a real case of online gender-based harassment, we examine how PerspectiveCoach supports ethical reasoning and engagement with user perspectives. Qualitative analysis revealed increased self-awareness, broadened perspectives, and more nuanced ethical articulation, while a complementary human-human study contextualized these findings. Text similarity analyses demonstrated that participants in the human-PerspectiveCoach study improved the fidelity of their restatements over multiple attempts, capturing both surface-level and semantic aspects of user concerns. However, human-PerspectiveCoach's restatements had a lower baseline than the human-human conversations, highlighting contextual differences in impersonal and interpersonal perspective-taking. Across the study, participants rated the tool highly for usability and relevance. This work contributes an exploratory design for LLM-powered end-user perspective-taking that supports critical, ethical self-reflection and offers empirical insights (i.e., enhancing adaptivity, centering plurality) into how such tools can help practitioners build more inclusive and socially responsive technologies.

Problem

• ethical reflection
• marginalized users
• perspective-taking
• inclusive design
• software ethics

Innovation

• LLM-powered reflection
• perspective-taking
• ethical reasoning
• inclusive design
• developer ethics