PromptHelper: A Prompt Recommender System for Encouraging Creativity in AI Chatbot Interactions

2026-01-22

AI Summary

This work proposes the first prompt recommendation system (PRS) integrated directly into a chat interface to address limitations users face when interacting with AI chatbots—namely, difficulties in exploring diverse conversational directions, articulating creative intent, and understanding the effects of prompt variations. The system employs a context-aware semantic diversity algorithm to generate real-time, varied follow-up prompt suggestions that encourage exploratory interaction while preserving user agency. A user study (N=32) demonstrates that the PRS significantly enhances users’ sense of exploration and expressive capacity, effectively alleviates prompting bottlenecks, and does so without imposing additional cognitive load.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Prompt Engineering / Prompting
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• prompting
• creativity
• AI chatbot
• user interaction
• exploratory interaction

Innovation

• prompt recommender system
• creative AI interaction
• semantic diversity
• exploratory prompting
• user agency