Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work

TL;DR

This study demonstrates that personalized AI assistants, tailored using user profiles and work style interviews, significantly enhance the quality, creativity, and trust in human-AI collaborative creative tasks.

Contribution

It introduces a personalized LLM-based assistant that improves collaboration by leveraging user psychometric data and work style insights, advancing human-AI synergy in creative work.

Findings

Personalized AI leads to higher quality marketing campaigns.

Participants trust and feel more assisted by personalized AI.

Personalization enhances collective memory and reasoning in collaboration.

Abstract

As AI becomes more deeply embedded in knowledge work, building assistants that support human creativity and expertise becomes more important. Yet achieving synergy in human-AI collaboration is not easy. Providing AI with detailed information about a user's demographics, psychological attributes, divergent thinking, and domain expertise may improve performance by scaffolding more effective multi-turn interactions. We implemented a personalized LLM-based assistant, informed by users' psychometric profiles and an AI-guided interview about their work style, to help users complete a marketing task for a fictional startup. We randomized 331 participants to work with AI that was either generic (n = 116), partially personalized (n = 114), or fully personalized (n=101). Participants working with personalized AI produce marketing campaigns of significantly higher quality and creativity, beyond what AI alone could have produced. Compared to generic AI, personalized AI leads to higher self-reported levels of assistance and feedback, while also increasing participant trust and confidence. Causal mediation analysis shows that personalization improves performance indirectly by enhancing collective memory, attention, and reasoning in the human-AI interaction. These findings provide a theory-driven framework in which personalization functions as external scaffolding that builds common ground and shared partner models, reducing uncertainty and enhancing joint cognition. This informs the design of future AI assistants that maximize synergy and support human creative potential while limiting negative homogenization.