PromptHelper: A Prompt Recommender System for Encouraging Creativity in AI Chatbot Interactions
This addresses the challenge for users of AI chatbots in enhancing creativity and interaction efficiency, though it is incremental as it builds on existing prompt-based interaction approaches.
The authors tackled the problem of users struggling to explore creative directions and articulate intent when interacting with AI chatbots through prompting, by introducing PromptHelper, a prompt recommender system that significantly increased users' perceived exploration and expressiveness without raising cognitive workload in a study with 32 participants.
Prompting is central to interaction with AI systems, yet many users struggle to explore alternative directions, articulate creative intent, or understand how variations in prompts shape model outputs. We introduce prompt recommender systems (PRS) as an interaction approach that supports exploration, suggesting contextually relevant follow-up prompts. We present PromptHelper, a PRS prototype integrated into an AI chatbot that surfaces semantically diverse prompt suggestions while users work on real writing tasks. We evaluate PromptHelper in a 2x2 fully within-subjects study (N=32) across creative and academic writing tasks. Results show that PromptHelper significantly increases users' perceived exploration and expressiveness without increasing cognitive workload. Qualitative findings illustrate how prompt recommendations help users branch into new directions, overcome uncertainty about what to ask next, and better articulate their intent. We discuss implications for designing AI interfaces that scaffold exploratory interaction while preserving user agency, and release open-source resources to support research on prompt recommendation.