Scaffolding Creativity: How Divergent and Convergent LLM Personas Shape Human Machine Creative Problem-Solving

TL;DR

This study introduces a coach-like LLM environment with divergent and convergent personas to enhance human creative problem-solving, revealing how personalized AI support influences user behavior and perceptions.

Contribution

It presents a novel persona-based LLM framework that balances exploration and convergence, with insights into user interaction and adaptive personalization for creative tasks.

Findings

User perceptions often differ from objective performance.

Individual differences predict persona preference.

Interaction patterns align with design thinking models.

Abstract

Large language models (LLMs) are increasingly shaping creative work and problem-solving; however, prior research suggests that they may diminish unassisted creativity. To address this tension, a coach-like LLM environment was developed that embodies divergent and convergent thinking personas as two complementary processes. Effectiveness and user behavior were assessed through a controlled experiment in which participants interacted with either persona, while a control group engaged with a standard LLM providing direct answers. Notably, users' perceptions of which persona best supported their creativity often diverged from objective performance measures. Trait-based analyses revealed that individual differences predict when people utilize divergent versus convergent personas, suggesting opportunities for adaptive sequencing. Furthermore, interaction patterns reflected the design thinking model, demonstrating how persona-guided support shapes creative problem-solving. Our findings provide design principles for creativity support systems that strike a balance between exploration and convergence through persona-based guidance and personalization. These insights advance human-AI collaboration tools that scaffold rather than overshadow human creativity.