LLM-enhanced Interactions in Human-Robot Collaborative Drawing with Older Adults

2025-06-23

AI Summary

This study addresses underexplored challenges in human–robot collaborative creativity with older adults—specifically, shallow interaction depth and weak understanding of artistic intent. To tackle these issues, we propose a novel co-creation paradigm: “Elderly Curator–Robot Collaborator.” Our approach integrates multimodal large language models (MLLMs), a colloquial spoken-dialogue system, and art-education-informed painting curricula to establish a cross-modal human–robot collaboration framework. Employing qualitative methods—including behavioral observation and in-depth interviews—we find that older adults strongly prefer curator-style robot suggestions and voice-based interaction, yet identify persistent limitations in the system’s comprehension of artistic context and creative intention. This work represents the first integration of LLM-driven spoken dialogue into elderly creative curation contexts, yielding both a theoretically grounded model and actionable design guidelines for age-inclusive human–robot co-creation.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Interaction Techniques and Devices

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

The goal of this study is to identify factors that support and enhance older adults' creative experiences in human-robot co-creativity. Because the research into the use of robots for creativity support with older adults remains underexplored, we carried out an exploratory case study. We took a participatory approach and collaborated with professional art educators to design a course Drawing with Robots for adults aged 65 and over. The course featured human-human and human-robot drawing activities with various types of robots. We observed collaborative drawing interactions, interviewed participants on their experiences, and analyzed collected data. Findings show that participants preferred acting as curators, evaluating creative suggestions from the robot in a teacher or coach role. When we enhanced a robot with a multimodal Large Language Model (LLM), participants appreciated its spoken dialogue capabilities. They reported however, that the robot's feedback sometimes lacked an understanding of the context, and sensitivity to their artistic goals and preferences. Our findings highlight the potential of LLM-enhanced robots to support creativity and offer future directions for advancing human-robot co-creativity with older adults.

Problem

• Enhancing older adults' creative experiences with robots
• Exploring LLM-enhanced human-robot collaborative drawing
• Addressing robot feedback's lack of contextual understanding

Innovation

• LLM-enhanced robot for creative dialogue
• Participatory design with art educators
• Multimodal interaction in co-creativity