#  Human-Robot Co-Creativity: A Scoping Review
Informing a Research Agenda for Human-Robot Co-Creativity with Older Adults

Marianne Bossema1,2, Somaya Ben Allouch3,4, Aske Plaat2 and Rob Saunders2 1
Amsterdam University of Applied Sciences, The Netherlands<EMAIL_ADDRESS>Leiden University, The Netherlands3 Amsterdam University of Applied Sciences,
The Netherlands4 University of Amsterdam, The Netherlands

###### Abstract

This review is the first step in a long-term research project exploring how
social robotics and AI-generated content can contribute to the creative
experiences of older adults, with a focus on collaborative drawing and
painting. We systematically searched and selected literature on human-robot
co-creativity, and analyzed articles to identify methods and strategies for
researching co-creative robotics. We found that none of the studies involved
older adults, which shows the gap in the literature for this often involved
participant group in robotics research. The analyzed literature provides
valuable insights into the design of human-robot co-creativity and informs a
research agenda to further investigate the topic with older adults. We argue
that future research should focus on ecological and developmental perspectives
on creativity, on how system behavior can be aligned with the values of older
adults, and on the system structures that support this best.

##  I INTRODUCTION

The world’s population is rapidly aging. According to the United Nations, the
number of people aged 60 years or older is expected to more than double by
2050, reaching approximately 2.1 billion [1]. This demographic shift is having
significant social, economic, and health implications. In 2019, the World
Health Organization published a review of 900 studies, concluding that
creative activities can promote health and well-being and help prevent and
slow age-related physical and cognitive decline [2]. Here, the term ‘creative
activities’ is referring to forms of personal, everyday creativity, such as
making music, drawing, dancing, or crafts. According to Cohen [3], such acts
of everyday creativity are fundamental to psychological development and well-
being in later life.

Creech et al. [4] present a systematic literature review into creativity and
the quality of later life, which highlights the benefits of the collaborative
and relational nature of creativity. Co-creativity is also linked to well-
being; Zeilig et al. [5] suggest that sharing agency in co-creative activities
can empower people with dementia. These studies share the view that co-
creativity can foster social connections and create a safe space that
facilitates involvement and sharing.

Social robots are playing a growing role in healthcare and well-being [6] [7].
There are few examples, however, of creative robot applications for older
adults. Social robots offer unique opportunities to support creativity through
assistance and social interaction. In addition, technological advancements in
generative AI bring new opportunities to suggest tailored content in creative
collaborations. There are unanswered questions, however, on how to design
appropriate human-robot co-creative systems. A scoping review was conducted,
to systematically map the research done in Human-Robot Co-Creativity (HRCC),
and to inform a research agenda for HRCC with older adults. We define HRCC as
“An interactive system for collaborative creativity between human and robot.
It involves the joint effort of two or more embodied and co-present agents,
where both take initiative in response to the other(s) and contribute to
creative outcomes”. This definition is based on the concepts of “Humbots”, as
described by Lubart et al., [8] and “Mixed-Initiative Creative Interfaces” as
introduced by Deterding et al. [9].

In the next section, we begin by presenting theories related to the value-
sensitive design of HRCC for older adults. In Section III, we describe the
methodology we used for this scoping review. Based on the analysis of selected
articles, we document the results in Section IV, followed by a discussion in
Section V. In Section VI we provide a conclusion, leading to a research agenda
outlined in Section VII.

##  II BACKGROUND

Investigating the prospective role of social robots in co-creative systems
bridges the fields of Human-Robot Interaction, Computational Creativity, and
Arts & Health. Here, we present theories from these fields in the context of
value-sensitive design of HRCC for older adults.

###  II-A Creativity and Values of Older Adults

Definitions of creativity generally share the common theme that it involves
generating something new, valuable, and surprising [10]. There are different
approaches, however, to understanding this complex concept. Glăveanu [11]
takes an ecological perspective, describing creativity as a phenomenon that
emerges through interaction in a social and material environment. Kaufman &
Beghetto [12] take a developmental perspective, looking at the individual.
Individuals are more likely to be creative when they are given challenging
tasks that require new solutions, have a degree of autonomy and control over
their work, and can collaborate and communicate effectively with others.

Both ecological and developmental perspectives align with the values that
older adults attribute to their creative experiences. In a Dutch study by
Groot et al. [13], older participants reported appreciating creative
activities for 1) offering an environment where they feel safe, accepted, and
free, 2) promoting personal and artistic growth, and 3) enabling meaningful
social interactions (Fig. 1). Based on the study of Groot et al., Liu et al.
[14] investigate the relationship between context, mechanisms, and outcomes,
and mention ‘a welcoming environment’ as a consistent underlying mechanism.
Liu et al. [14] recommend deepening our understanding of environments and
affective atmospheres in art activities with older adults. Groot et al. [13]
recommend Participatory Action Design as a research approach to capture the
essence of older participants’ creative experiences.

Figure 1: Values that older adults attribute to creative activities, based on
a Dutch nationwide study by Groot et al. [13]. Figure adapted with permission.

In “A Roadmap for Therapeutic Computational Creativity”, Pease et al. [15]
delve into the connection between Computational Creativity and mental health
and well-being. The authors discuss the benefits and risks associated with
this connection. They also highlight potential opportunities, such as _casual
creators_ [16]. Casual creators prioritize the pleasure of the creative
process over the end product and offer enjoyable and easily accessible
creative experiences that may be valuable for older adults. This kind of
experiences may promote “personal and artistic growth”, while contributing to
“an atmosphere in which people feel safe, accepted, and free” [13]. The
roadmap also discusses the concept of the ‘third hand’, a metaphor for the
therapist’s role in supporting and encouraging patients’ creative processes,
without imposing their own ideas or disrupting the patient’s autonomy [17].
The researchers recommend collaboration with health professionals, to
determine the limitations and possibilities of therapeutic computational
creativity [18].

###  II-B Interaction Design for Computational Creativity

Kaufman & Beghetto [12] mention two main requirements for people to be
creative 1) a degree of autonomy and control, and 2) effective communication
with others. These requirements present challenges for the design of co-
creative systems. While traditional creativity support tools focus on human
control, Gemeinboeck & Saunders [19] suggest embodied creative agents that
share the world with humans, and act autonomously, beyond their creator’s
intent. Mixed-Initiative Creative Interfaces [9] are in between, a form of AI-
enabled Creativity Support tools, where both humans and the system can take
initiative during creative collaboration. This raises questions, e.g., on how
agency can be shared and how initiative can be negotiated to support both
well-being and mutual creativity.

The requirement of effective communication also poses interaction design
challenges. Bray & Bown [20] argue that computational creativity systems are
often complex and opaque, limiting visibility and clarity of their conceptual
models. Understanding may be improved when users can clearly perceive the
system’s structure, and develop a mental model of how this structure leads to
behavior. This is crucial to facilitate a suitable level of autonomy and
control. Dialogues can be expected to contribute to understanding and common
ground, either language-based or through creative artifacts. A dialogic
approach, as suggested by Bown et al. [21], can enable both human and
artificial agents (e.g. social robots) to actively influence the creative
process and products, and adapt to the other’s behavior.

Social robots offer unique opportunities for embodied interaction, sharing
agency, and (non-)verbal communication. They can suggest tailored AI-generated
content and support creative exploration. The articles being reviewed shed
light on how interaction design challenges may be faced and how solutions may
be applied.

TABLE I:  Keywords searched in databases Actors | Activities | Applications
---|---|---
Human-machine | Co-creativ* | Creativ* support
Human-computer | Creative collaboration | Support* creativity
Human-robot | Art* collaboration | Stimulat* creativity
Human-AI | Collaborative creativity | Art therapy
Robot* | Collaborative art | Creativ* Robot*
Artificial Intelligence | Collaborative drawing |
AI | Collaborative painting |
Machine Learning | Collaborative sketching |

##  III METHOD

Six databases were used to conduct the scoping review: ACM, IEEE, Google
Scholar, PsycINFO, Pubmed, and Scopus. Keywords were chosen for ‘Actors’ (e.g.
human-robot), ‘Activities’ (e.g. co-creativity), and ‘Application’ (e.g.
Creativity Support), see Table I. Only conference and journal articles,
published in English were included. The search results (n=827) were collected
in February 2023 and imported in Rayyan [22], where duplicates (n=100) were
removed, and labels were assigned. Searching and selecting articles was done
systematically using PRISMA guidelines [23], currently with a single reviewer
(the first author). Based on a first screening of titles and abstracts,
articles (n=432) were excluded when they a) did not involve human subjects in
evaluating a robotic system, b) described a distinctive context (e.g.,
business, innovation, teaching, or product development) when they were
conference workshop calls or proposals, or c) were found to be duplicates. In
a second screening, papers (n=157) were removed that did not show evidence of
a ‘co-creative agent’, here defined as a computational actor involved in
building shared creative artifacts, in co-presence with one or more human
collaborators [8]. In the last step of the screening, studies that did not
involve an embodied intelligent agent were removed (n=113). Doing the
exclusion selection in separate steps allowed for acquiring a broader view,
and offered the opportunity to also keep studies with non-robotic agents in
mind. Two papers were added through forward and backward citation searches,
and a final set of 27 articles was used for analysis.

Figure 2: PRISMA flow diagram for the scoping review.

###  III-A Analysis of Design Research

Studies in HRCC are a form of design research, focused on understanding
specific interaction design problems. We used the Function-Behavior-Structure
(FBS) ontology, as described by Gero & Kannengiesser [24], for analyzing and
comparing this kind of study. The ontology is based on the notion that all
designs can be represented in a uniform way, and that design systems can be
conceptualized in three ontological categories. Function (F) is about ‘what
the system is for’, Behavior (B) covers ‘what it does’, and Structure (S)
describes the components and their relationships, or ‘what it consists of’. In
addition, we applied a layered framework for interactions between creative
collaborators, proposed by Kantosalo et al. [25], to the ‘Behavior’ of co-
creative systems into interaction layers of modalities, styles, and strategies
to provide a finer-grained view of ‘what a co-creative system does’.

TABLE II: Reviewed papers, structured using the FBS ontology [24] and the
Interaction Framework for Human-Computer Co-Creativity [25].

Function | Behavior | Structure |
---|---|---|---
| Interaction layers |  |
Focus | Participants | Domain | Strategies | Styles | Modalities | Robots, devices | Refs
Creativity Support | Children | Storytelling | Stimulate human creativity | Demonstrate verbal creativity | Game-based | Speech, GUI | Jibo, tablet |  [26][27]
| Children | Storytelling | Stimulate human creativity | Mirror/contrast user input | Open-ended | TUI | Robotic object (YOLO) |  [28][29][30]
| Children | Storytelling | Stimulate human creativity | Promotional behavior | Open-ended | Speech, GUI | EMYS, tablet | [31]
| Children | Storytelling | Stimulate human creativity | Demonstrate verbal creativity | Open-ended | Speech, GUI | Furhat, tablet |  [32][33]
| Children | Storytelling | Stimulate human creativity | Scaffold creativity/reflection | Open-ended | TUI | Stuffed animal, physical tools | [34]
| Children | Drawing | Stimulate human creativity | Embodied presence | Game-based | Speech, GUI | Jibo, tablet | [26]
| Children | Drawing | Stimulate human creativity | Demonstrate figural creativity | Game-based | Speech, GUI | Jibo, tablet | [27]
| Children | Construction | Stimulate human creativity | Scaffold creativity/reflection | Game-based | Speech, GUI | Jibo, tablet | [27]
| Adults | Storytelling | Stimulate human creativity | Demonstrate verbal creativity | Game-based | Speech, GUI | Robovie, display | [35]
| Adults | Drawing | Stimulate human creativity | Embodied presence | Game-based | Speech, GUI | Nao, tablet | [36]
| Adults | Performance | Stimulate human creativity | Mirror/contrast user input | Open-ended | TUI | Robotic object, tablet | [37]
Creative collaboration | Adults | Drawing | Non-verbal communication | Expressive robot movement | Open-ended | TUI, GUI | Cobot, physical tools | [38]
| Designers | Drawing | Conceptual sketching | Suggesting conceptual shifts | Open-ended | TUI | Drawbot, physical tools | [39]
| Adults | Drawing | Non-verbal communication | Expressive robot movement | Open-ended | TUI | Cobot, physical tools | [40]
| Artists | Performance | Arts-led, process-led | Dialogue through improvisation | Open-ended | TUI, GUI | Cobot, tablet, dance floor | [41]
| Children | Drawing | Human/machine learning | User-specific training data | Open-ended | TUI | Cobot, physical tools | [42]
Art Therapy | Adults | Painting | Responsive art approach | Express matching emotions | Open-ended | Speech, TUI | Baxter, BMI, physical tools | [43]
| Adults | Painting | Balance contingency/artistry | Suggesting visual metaphors | Open-ended | Speech, TUI | Baxter, physical tools | [44]
| Adults | Painting | Connect to personal art | Personalized visual metaphors | Open-ended | Speech, TUI | Baxter, physical tools | [45]
| Children | Drawing | Personalization | Speech-assisted co-drawing | Open-ended | Speech, GUI | Cobot, tablet | [46]
Artistic Work | Artist | Paint/perform | Human-machine symbiosis | Mimicry, memory a.o. | Open-ended | TUI, ambient | Cobots a.o., art installation | [47]
| Audience | Performance | Embodied Creative AI | Shared creative spaces | Open-ended | Ambient | Robotic objects, art installation | [48]
| Audience | Performance | Embodied Creative AI | Performance Body Mapping | Open-ended | Ambient | Robotic objects, art installation | [49]
| Audience | Paint/perform | Robotic art of audience input | Speech-to-AI-art | Open-ended | Speech, ambient | Kuka robot, art installation | [50]

##  IV RESULTS

The FBS ontology [24] and Kantosalo’s Interaction Framework for Human-Computer
Co-Creativity [25] were used for the analysis of reviewed articles. An
overview is presented in Table II.

###  IV-A Function

When looking at the reviewed articles presented in Table II, we can
distinguish four categories based on their research focus: 1) Creativity
Support 2) Creative Collaboration 3) Art Therapy, and 4) Artistic Work.
_Creativity Support_ forms the largest group, with studies investigating
factors of social robot behavior that affect human creativity. Studies of
_Creative Collaboration_ explored the interaction dynamics, and how the
process of collaboration can be facilitated. In the category of _Art Therapy_
, the focus was on specific therapeutic requirements, and how to design
responsive systems for affective and assistive collaborative painting and
drawing. In the category of _Artistic Work_ , the focus is on creative
encounters between humans and machines, and studies are carried out in the
context of the researchers’ own artistic practice, mostly in performances
involving the audience. Regarding participants and target groups, we found
that studies on _Creativity Support_ involved mostly children, while adults
and professional artists and designers participated in the _Creative
Collaboration_ studies. In the category of _Artistic Work_ , the artists
themselves also played an important role, as well as the audience.

###  IV-B Behavior: Strategies, Styles, Modalities

All studies in the category of _Creativity Support_ propose the strategy of
stimulating human creativity, through various social behaviors of a robot.
Creativity demonstration was used to stimulate creativity with children (n=4)
and with adults (n=1). The robot demonstrated verbal creativity in
storytelling applications and figural creativity in a drawing game. It was
found that creativity demonstrations and scaffolding (e.g. asking questions,
prompting, and suggestions), as well as the promotion behavior of the robot
can contribute to higher levels of human creativity. When mirroring or
contrasting robot movements were congruent with user input, this positively
affected creativity [28][37]. The studies mostly compared conditions of robot
behavior, using pre-defined, validated content. For example, in multiple
studies the robot demonstrated creativity by selecting pre-defined suggestions
with a validated creativity score, dependent on the condition [26][27][35].

In the category of _Creative Collaboration_ , two studies explored expressive
robot movements to improve non-verbal communication [38][40]. In the context
of collaborative drawing, the effects of direct versus indirect motion paths
on collaborative interaction were compared, but the results were inconclusive.
The researchers recommend further in-the-field experiments, combining
qualitative and quantitative methodologies. An arts-led, process-led approach
is proposed by Gomez Cubero et al. [41], to explore how co-creativity emerges
through human-robot dialogue and improvisation. The researchers developed
custom tools to support collaborative drawing with an industrial robot and put
these into practice. In a study involving designers, a mobile robot was
introduced for collaborative sketching and generating ideas through
‘conceptual shifts’ [39]. Using the Sketch-RNN model and the Google Quick,
Draw! API, input sketches were mapped to suggestions with visual and semantic
similarity. Results showed that the mobile, embodied agent performed better in
provoking exploratory thinking and collaborative ideation, compared to a web-
based agent. The alignment of human, robot, and machine learning is suggested
by Twomey [42] in a study where the robotic system is trained on audience-
specific content in the form of children’s drawings.

With _Art Therapy_ , the focus is on investigating how a robot can learn to
understand and adapt to the creative and emotional expressions of a human
interaction partner. Cooney & Menezes [43] propose to generate responsive art
for emotion regulation, through robot expressions of either matching or
positive emotions. Using wireless electroencephalography (EEG), brain signals
were captured and classified based on Russell’s valence/arousal model, and
then translated into visual features for paintings. Affective image databases
were used to train the system and Deep Convolutional Generative Adversarial
Networks (DC-GANs) for synthesizing compositions. To balance contingency and
artistry, Cooney & Berck [44] made use of visual metaphors that are responsive
to perceived emotions. In a follow-up study, Cooney [45] proposes metaphors
that connect more to emotional artistic expressions. Personalization was also
facilitated, through the robot’s open questions, letting users add their own
tags to describe the content. Another implementation of personalization is
suggested by Shaik et al. [46], by letting the system adapt sketches based on
verbal feedback and explicit directions from users, which were disabled
children.

Figure 3: Per column: 1) Jibo, scaffolding creativity in a construction task
[27]; YOLO, a robot toy for storytelling [28]; 2) The robot is present, an
arts-led, process-led approach for investigating human-robot dialogues in
improvisation [41]; Cobbie, a drawbot for conceptual sketching with designers
[39]; 3) Baxter robot used in Art Therapy; The valence/arousal model for
expressing matching emotions; 4) Accomplice - Creative robotics and embodied
computational creativity [48]; Dream Painter - Bridging Audience Interaction,
Robotics, and Creative AI [50]

In the category of _Artistic Work_ , Sougwen Chung [47] explores human-machine
symbiosis, studying concepts of mimicry, memory, collectivity, and
spectrality. For example, mimicry is explored with the robot mimicking the
artists drawing gestures and for memory, the machine learns the artist’s
drawing style, with neural nets trained on the artists drawing collection.
Saunders & Gemeinboeck [48] investigate how embodied, creative AI can act as a
performer by embedding a group of autonomous robots into the walls of a
gallery. The robots are programmed as curious agents, driven to explore their
world. By punching on the walls and making holes, they make changes to the
environment, communicate their presence, and involve the audience. In another
study by Saunders & Gemeinboeck [49], professional dancers and non-humanlike
robots were brought together in co-embodied explorations of forms and
movements. Sola et al. [50] suggest speech-to-AI-art transformations and
created an interface that allowed the audience to tell a co-creative system
about their dreams. Based on prompts from the input text, the AI-system
generated a drawing through latent space navigation using the CLIP model [51].
The industrial robot arm captured the audience’s stories in a collective
painting that hang down into the atrium of a museum as a cascade of dreams.

When looking at game-based versus open-ended interaction styles, the studies
that compared robot behavior in different conditions generally used game-based
interaction, e.g., in drawing and storytelling games. This facilitated
experimental control when comparing and measuring the effects of social robot
behavior. In other studies, open-ended forms of interaction were used, which
allows for investigating processes and dynamics, and contributes to ecological
validity when researching creative collaboration.

What stands out when looking at the interaction modalities used in the four
categories, is that speech is mostly used in the categories _Creativity
Support_ and _Art Therapy_. Robot speech is used for demonstrating verbal
creativity, and for scaffolding creativity, and prompting creative reflection
[27][34]. For art therapy robots, speech is found to be useful as well. It
allows complex information to be conveyed, in a familiar and intuitive way,
without requiring a person to look away from art-making and possibly lose
concentration [43]. Speech also enables users to give explicit feedback or
directions or ask for assistance, as suggested by Shaik et al. [46]. In the
categories _Creative Collaboration_ and _Artistic Work_ , human-machine
dialogues are more often based on non-verbal communication such as expressive
movement, or through the work itself. With _Artistic Work_ , we see most
examples of ambient interfaces, when exploring new forms of human-machine
encounters in a spatial setting (Fig. 3).

###  IV-C Structure

Different types of robots and embodiments were used in the reviewed studies
(Fig. 3). Human-like robots were used in almost all studies in using a
Stimulating creativity strategy, combined with a tablet or a computer screen.
With the drawing activities in this category, the robot (Jibo, Nao) was not
drawing physically, but virtually on the tablet, with separate canvases on the
same tablet [26][52][36]. In studies employing game-based interaction styles,
screens were used to present the game world. Alves-Oliveira et al.
[28][29][30] used a non-anthropomorphic robot object to stimulate creativity
in children; YOLO serves as a toy character in a storytelling game. The robot
interacts through lights, colors, and movements, while the shape of the robot
set realistic expectations for the robot’s capabilities. In the category of
_Creative Collaboration_ , collaborative robot (cobot) arms were mostly used,
together with physical drawing tools. Physical drawing tools were also used
with art therapy robots. The Baxter robot used for art therapy [43] can be
considered a human-like cobot, with two arms and a screen that can display a
face and facial expressions, facilitating non-verbal social communication.
With Artistic Work, robot arms were used next to custom-made robotic objects,
mostly in multi-agent settings. The stage is shared between humans and robots,
mostly in performances. In an art installation by Sola et al. [50], the
industrial robot arm is behind glass, while the audience can communicate with
the robot through a speech interface. In Accomplice, Saunders & Gemeinboeck
[48] install robots in their own space behind a wall in a gallery, which they
breaking through as they use the wall as their canvas. Saunders & Gemeinboeck
[49] used robotic cubes to explore how human and non-human forms of embodiment
can be mapped through movement, and how non-humanlike robotic objects can be
perceived as affective agents.

Figure 4: Values that older adults attributed to their creative activities in
a nationwide Dutch study [13], connected to robot behaviors suggested in the
reviewed studies, with the corresponding categories (Table II). These values
(e.g. meaningful connections) were attributed in the context of human-human
interactions. We suggest to investigate if and how human-robot co-creative
interactions can be valuable to older adults as well. We propose this mapping
of values and behaviors as part of our research agenda (Section VII).

##  V DISCUSSION

This review has several limitations. An important limitation is the fact that
the review selection was carried out by a single reviewer, due to time
constraints. In addition, there are opportunities to further work out the
analysis, for example to explore how machine learning techniques, as part of
the structure of a system, align with behavior and function. It turned out
that our search results did not include any studies involving the elderly.
This shows that future research on HRCC with older adults is important.
However, it is also a limitation, as we cannot learn from previous findings in
HRCC with the target group. We are planning an extended version of this
review, with a search query that does include the target group, f.e., looking
at gerontechnology for creativity support. This allows us to involve more
reviewers and to address aspects that have been underexposed so far.

##  VI CONCLUSION

Selected articles were structured using the FBS ontology [24] and the
Interaction Framework for Human-Computer Co-creativity [25]. The search and
selection process (see Section III) resulted in a heterogeneous set of
studies, describing robotic systems with various functions, behaviors, and
structures.

###  VI-A Function

Studies in the categories of _Creativity Support_ and _Art Therapy_ take a
developmental perspective, with the goal to a) stimulate human creativity and
b) support art therapy through responsiveness and personalization. Studies in
the categories _Creative Collaboration_ and _Artistic Work_ take an ecological
perspective, investigating how creativity emerges through interaction. This is
a more process-led approach, involving end users and taking into account the
social and material environment. As set out in Section II, both ecological and
developmental perspectives align with values that older adults attribute to
their creative experience, and must be taken into account when defining the
functions of HRCC for older adults. An important finding regarding
participants is that older adults did not engage in any of the reviewed
studies. While Cooney & Menezes [43] thank older adults in their
acknowledgments for providing input, they evaluated their system with younger
adults. It is not clear why older adults have not yet been involved in HRCC
research. Robots and AI-generated content offer opportunities that can be
beneficial for this specific target group, which is growing worldwide, and
there are specific needs and wishes to be taken into account. That is why we
are making a case for investigating HRCC for, and with, the target group of
older adults.

###  VI-B Behavior

Evidence shows that robots are capable of demonstrating creativity, and that
this social behavior can be designed to stimulate human creativity. Other
social behaviors are found to be effective as well, such as mirroring and
contrasting user input to promote divergent and convergent thinking. Studies
on robots in _Art Therapy_ provide valuable insights into the importance of
recognizing, modeling, and synthesizing emotions in drawings and paintings.
Here, the emphasis is on tailoring and balancing content to user needs e.g.,
using personalized visual metaphors. These ideas on how an art therapy robot
could behave as a ‘third hand’ also inform future research in HRCC for older
adults. Studies in the category of _Creativity Support_ often used games to
structure the interaction, which contributes to experimental control when
measuring the effects of robot behavior. However, the majority of studies used
open-ended forms of interaction, investigating how dialogues and
collaborations develop.

The modality of speech is considered an important channel for transparency and
effective communication, promoting autonomy and control. This is emphasized in
the categories _Creativity Support_ and _Art Therapy_. Robot speech is used,
e.g., to demonstrate verbal creativity, scaffold creativity, and promote
creative reflection. User speech input is suggested as a means for explicit
feedback, requesting assistance, and personalizing suggested content. The
research projects in the category of _Artistic Work_ place creative robots in
spatial settings, sometimes with multiple agents, and letting artists and
audiences contribute to a physical shared space that fosters creativity. Both
speech and embodied, spatial interactions are of interest for HRCC with older
adults, to contribute to an environment where people feel free and safe.

###  VI-C Structure

Results show that in the categories _Creativity Support_ and _Art Therapy_ ,
mostly human-like robots were used. The robot YOLO is an exception, an
abstract robotic object that serves as a toy, while the shape of the robot
sets realistic expectations for the robot’s capabilities [29]. A shared stage
for humans and robots, as explored in studies on _Artistic Work_ , could be
interesting for older adults as well, when designed as an environment
fostering creativity, and where people feel free and safe.

##  VII RESEARCH AGENDA

We propose a participatory, value-sensitive design approach for investigating
HRCC with older adults. Older adults must be involved throughout the entire
process, in identifying opportunities and requirements, developing HRCC
activities, and testing hypotheses in both controlled experiments and in-the-
field settings. When investigating the design of the system, we propose
considering of the following aspects, aligned with the the FBS framework:

Function: Consider both ecological and developmental perspectives on
creativity when defining functional requirements for the target group.
---
Behavior: Align values that older adults attribute to creative activities with
the opportunities of HRCC (Fig. 4) to investigate how:  1. A robot’s social
behavior can support and enhance creative experiences for older adults; 2. AI-
generated content can be tailored and responsive to specific needs and
desires; and, 3. Intuitive dialogues (verbal, non-verbal, through artifacts)
can support co-creativity.
Structure: Investigate what types of robot and devices fit best and provide
opportunities for:  1. Social interaction with older adults; 2. Creative
support and exploration; and, 3. Shared creative experiences and spaces where
older adults feel free and safe.

## ACKNOWLEDGMENT

This publication is part of the project ‘Social robotics and generative AI to
support and enhance creative experiences for older adults’, with project
number 023.019.021 of the research program Doctoral Grant for Teachers which
is financed by the Dutch Research Council (NWO).

## References

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