Prompt Coaching for Inclusiveness: A Media Literacy Approach to Increase Users' Awareness of Algorithmic Bias and Prompting Efficacy
Cheng Chen, Mengqi Liao, Aditya Anand Phadnis, Yao Li, Andrew High, Saeed Abdullah, S. Shyam Sundar
Abstract
Large language models often produce biased or stereotypical outputs. One way to reduce this possibility is to be more inclusive in our prompts, but doing so may not come naturally to most users. Therefore, we designed a tool that coaches users to write more inclusive prompts—a strategy that leverages design friction to provide a media literacy intervention. Data from a user study (N=344) show that compared to no coaching, inclusive prompt coaching directly increased users’ awareness of algorithmic bias and their perceived prompting efficacy. It also indirectly enhanced their trust in the system and perceived trust calibration through cognitive elaboration. However, inclusive prompt coaching resulted in a less satisfying user experience. These findings have implications for ethical interventions in prompting for better communicating and combating algorithmic bias. We discuss the benefits and limitations of inclusive prompt coaching, as well as ways to balance usability for long-term adoption of generative AI systems.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get a892ccf9-0161-4b10-84c4-e13b1d155482Related papers
- Why Johnny Can't Prompt: How Non-AI Experts Try (and Fail) to Design LLM PromptsJ. D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, Qian YangCHI 2023 · 892 citations
- Interface Support for Evaluating Disability Bias in AI-Generated ImagesKelly Avery Mack, Lucy Jiang, Lotus Zhang, Leah FindlaterCHI 2026 · 1 citation
- Farsight: Fostering Responsible AI Awareness During AI Application PrototypingZijie J. Wang, Chinmay Kulkarni, Lauren Wilcox, Michael Terry et al.CHI 2024 · 55 citations
- Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM BehaviorMinjae Lee, Minsuk KahngCHI 2026 · 1 citation
- Who Controls the Conversation? User Perspectives on Generative AI (LLM) System PromptsAnna Neumann, Yulu Pi, Jatinder SinghCHI 2026 · 3 citations
