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

TL;DR

This study explores how LLM-enhanced robots can support creative drawing activities with older adults, revealing preferences for curator roles and highlighting challenges in contextual understanding.

Contribution

It introduces a participatory course involving human-robot drawing activities with older adults and evaluates the impact of LLM enhancements on robot interaction quality.

Findings

Participants preferred acting as curators evaluating robot suggestions.

Enhanced robots with LLMs improved spoken dialogue interactions.

Participants noted limitations in robot understanding of artistic context.

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.