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

Abstract

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.

Practitioner notes

What is already known about this topic?

• Previous studies have explored the impact of AI in creative collaborations, with a focus on text-based models like LLMs enhancing writing quality.
• Embodiment in AI systems, such as humanoid robots, has been shown to affect user engagement and emotional responses, influencing creativity.
• Human collaboration has traditionally been seen as superior in generating stylistic elements like imagery and voice, while AI excels in originality and idea generation.

What this paper adds?

• This research demonstrates that robot–LLM collaboration significantly boosts originality, particularly when high-creativity AI is used.
• The study uncovers the ‘embodiment paradox’, where embodied robots enhance creativity in high-creativity AI conditions but human collaboration remains superior in stylistic expression.
• The mediation role of user engagement is explored, showing how embodiment can enhance creative outcomes when AI creativity is low and amplify them when AI creativity is high.

Implications for practice and/or policy

• Educators and trainers can utilize embodied AI systems in creative tasks to increase student or participant engagement and foster more original outputs.
• Training programmes can be structured to leverage the strengths of both human collaboration and AI, tailoring tasks based on AI's creativity levels for optimal outcomes.
• Policy around the integration of AI in educational and creative settings should encourage balanced AI systems that combine embodiment and creativity for enhanced collaborative work.