Enhancing creative writing with robot–LLM integration: The interplay of embodiment, AI creativity and user engagement

This study explores the impact of robot–LLM (Large Language Model) integration on collaborative creative writing, focusing on how embodiment and AI creativity influence various aspects of creative output. A total of 150 undergraduate students participated in a structured experimental design with five collaboration conditions: Human–Human (HH), Human–Computer with High-Creativity LLM (HC), Human–Robot with High-Creativity LLM (HR), Human–Robot with Low-Creativity LLM (RL) and Human–Computer with Low-Creativity LLM (CL). Creativity was assessed through expert ratings and computational analysis of originality, imagery, voice and semantic flow. The results revealed that while the Human–Robot (High-Creativity LLM) condition significantly enhanced originality, Human–Human and Human–LLM (text-based) collaborations excelled in imagery and voice. The study identified an ‘embodiment paradox’, where robot embodiment amplified creativity in high-creativity AI conditions, yet human collaboration remained superior in stylistic expression. Mediation analysis revealed that user engagement acted as a mediator, with embodiment compensating for low-creativity AI and amplifying the creative process with high-creativity AI. The findings have important implications for the design of collaborative AI systems, highlighting the need for a balanced integration of embodiment and AI creativity to optimize creative outcomes. This research contributes to our understanding of how human–robot–LLM collaborations can expand creative potential in writing, offering insights for future AI applications in educational and creative industries.