# Designing Creative AI Partners with COFI: A Framework for Modeling
Interaction in Human-AI Co-Creative Systems

Jeba Rezwana<EMAIL_ADDRESS>and  Mary Lou Maher<EMAIL_ADDRESS>University of North Carolina at CharlotteUSA

###### Abstract.

Human-AI co-creativity involves both humans and AI collaborating on a shared
creative product as partners. In a creative collaboration, interaction
dynamics, such as turn-taking, contribution type, and communication, are the
driving forces of the co-creative process. Therefore the interaction model is
a critical and essential component for effective co-creative systems. There is
relatively little research about interaction design in the co-creativity
field, which is reflected in a lack of focus on interaction design in many
existing co-creative systems. The primary focus of co-creativity research has
been on the abilities of the AI. This paper focuses on the importance of
interaction design in co-creative systems with the development of the Co-
Creative Framework for Interaction design (COFI) that describes the broad
scope of possibilities for interaction design in co-creative systems.
Researchers can use COFI for modeling interaction in co-creative systems by
exploring alternatives in this design space of interaction. COFI can also be
beneficial while investigating and interpreting the interaction design of
existing co-creative systems. We coded a dataset of existing 92 co-creative
systems using COFI and analyzed the data to show how COFI provides a basis to
categorize the interaction models of existing co-creative systems. We identify
opportunities to shift the focus of interaction models in co-creativity to
enable more communication between the user and AI leading to human-AI
partnerships.

Human-AI Co-Creativity, Co-Creativity, Interaction Design, Framework

††copyright: acmcopyright††journal: TOCHI††journalyear: 2022††journalvolume:
1††journalnumber: 1††article: 1††publicationmonth: 1††price: 15.00††ccs:
Human-centered computing Interaction design process and methods††ccs: Human-
centered computing Collaborative interaction

##  1\. Introduction

Computational creativity is an interdisciplinary field that applies artificial
intelligence to develop computational systems capable of producing creative
artifacts, ideas and performances (Colton et al., 2012). Research in
computational creativity has lead to different types of creative systems that
can be categorized based on their purposes: systems that generate novel and
valuable creative products, systems that support human creativity, and systems
that collaborate with the user on a shared creative product combining the
creative ability of both the user and the AI (Davis et al., 2015a). Davis
introduces the term human-computer co-creativity, where humans and computers
can collaborate in a creative process as colleagues (Davis, 2013). In human-
computer co-creativity, both humans and AI agents are viewed as one system
through which creativity emerges. The creativity that emerges from a
collaboration is different from creativity emerging from an individual as
creative collaboration involves interaction among collaborators and the shared
creative product is more creative than each individual could achieve alone
(Sawyer and DeZutter, 2009). Stephen Sonnenburg demonstrated that
communication is the driving force of collaborative creativity (Sonnenberg,
1991). Interaction is a basic and essential component of co-creative systems
as both the human and the AI actively participate and interact in the co-
creation, unlike autonomous creative systems that generate creative artifacts
alone and creativity support tools that support human creativity.

Designing and evaluating co-creative systems has many challenges due to the
open-ended and improvisational nature of the interaction between the human and
the AI agent (Davis et al., 2016; Kantosalo et al., 2014). Humans utilize many
different creative strategies and reasoning processes throughout the creative
process, and ideas and the creative product develop dynamically through time.
This continual progression of ideas requires adaptability on the agent’s part.
Additionally, it is not always clear how the co-creative AI should contribute
and interact during the course of the co-creative process. For example,
sometimes the human may want to lead and have the AI assist with some tasks,
whereas other times the human may want the AI to lead to help find inspiration
or to work independently. Understanding the mechanics of co-creation is still
very much open questions in the young field of human-computer co-creativity.
Bown asserted that the success of a creative system’s collaborative role
should be further investigated as interaction plays a key role in the creative
process of co-creative systems (Bown, 2015). AI ability alone does not ensure
a positive collaborative experience of users with the AI (Louie et al., 2020)
and interaction is more critical than algorithms where interaction with the
users is essential (Wegner, 1997). In this paper we focus on the interaction
design space as an essential aspect of effective co-creative systems.

Interaction design is the creation of a dialogue between users and the system
(Kolko, 2010). Recently, interaction design in co-creative systems is being
addressed as a significant aspect of computational creativity. Kantosalo et
al. said that interaction design, specifically, interaction modality should be
the ground zero for designing co-creative systems (Kantosalo et al., 2020).
However, the interaction designs of many existing co-creative systems provide
only one-way interaction, where humans can interact with the AI but the system
is not designed for the AI to communicate back to humans. For example,
Collabdraw (Fan et al., 2019) is a co-creative sketching environment where
users draw with an AI. The user interface includes only one button that users
click to submit their artwork and indicate that their turn is complete. Other
than the button, there is no way for the user to communicate with the AI and
vice versa to provide information, suggestions, or feedback. Although the AI
algorithm is capable of providing intriguing contributions to the creative
process, the interaction design is inadequate for collaboration between human
and AI. Another example, Image to Image (Isola et al., 2017) is a co-creative
system that converts a line drawing of a particular object from the user into
a photo-realistic image. The user interface has only one button that users
click to tell the AI to convert the drawing. Interaction design can provide
more than the transfer of instructions from a user to an AI agent to generate
a creative artifact and can lead to a more engaging user experience. A recent
study showed increased user satisfaction with text-based instructions from the
AI rather than button-based instructions in a co-creation (Oh et al., 2018). A
starting point to investigate interaction models is the study of collaboration
among humans (Davis et al., 2015a). Understanding the factors in human
collaboration can build the foundation for the development of human-AI
collaboration in co-creative systems (Mamykina et al., 2002). Interaction
models developed for computer supported collaborative work is an important
source for identifying interaction models related to co-creative systems.

In this paper, we present Co-Creative Framework for Interaction design (COFI)
that describes interaction components as a space of possibilities for
interaction design in co-creative systems. These interaction components
represent various aspects of a co-creation, such as participation style,
contribution type, and communication between humans and the AI. COFI is
informed by the literature on human collaboration, CSCW, computational
creativity, and human-computer co-creativity. We adopted interaction
components based on a literature review and adapted the components to concepts
relevant to co-creativity. COFI can be used as a guide when designing the
interaction models in co-creative systems. COFI can also be beneficial for
investigating and interpreting the interaction design of existing co-creative
systems. We coded and analyzed the interaction models of a dataset of 92 co-
creative systems using COFI to evaluate the value and analytical power of the
framework. Three distinct interaction models for co-creative systems emerged
from this analysis: generative pleasing AI agents that follow along with the
user, improvisational AI agents that work alongside users on a shared product
spontaneously, and advisory AI agents that both generate and evaluate the
creative product. The analysis reveals that the co-creative systems in this
dataset lack communication channels between the user and AI agent. Finally,
this paper discusses the limitations in the existing interaction models in co-
creative systems, potential areas for further development, and the importance
of extending the scope of human-AI communication in co-creative systems.

##  2\. Related Work

###  2.1. Co-creative Systems

Creativity is defined as the exploration and production of novel and useful
ideas (Dietrich, 2004; Jung et al., 2013; Jennings et al., 2011). Wiggins
defined creative systems as ”A collection of processes, natural or automatic,
which are capable of achieving or simulating behavior which in humans would be
deemed creative (Wiggins, 2006).” Davis et al. discussed the three main
categories of creative systems based on their working processes and purposes
(Davis et al., 2015a): standalone generative systems, creativity support
tools, and co-creative systems. Standalone generative systems refer to fully
autonomous intelligent systems that work independently without any interaction
with humans in the creative process. Creative systems that support the user’s
creativity without contributing to the creative process are considered
creativity support tools (CST). In co-creative systems, humans and computer
both contribute as creative colleagues in the creative process(Davis, 2013).
Co-creative systems originated from the concept of combining standalone
generative systems with creativity support tools as computers and humans both
take the initiative in the creative process and interact as co-creators
(Kantosalo and Jordanous, 2020). Mixed initiative creative systems are often
used as a substitute term for co-creative systems in the literature
(Yannakakis et al., 2014).

In a co-creative system, interaction between the human and AI agent make the
creative process complex and emergent. Maher explores issues related to _who_
is being creative when humans and AI collaborate in a co-creative system
(Maher, 2012). Antonios Liapis et al. argued that when creativity emerges from
human-computer interaction, it cannot be credited either to the human or to
the computer alone, and surpasses both contributors’ original intentions as
novel ideas arise in the process (Liapis et al., 2014). Designing interaction
in co-creative systems has unique challenges due to the spontaneity of the
interaction between the human and the AI (Davis et al., 2016). A co-creative
AI agent needs continual adjustment and adaptation to cope with human
strategies. A good starting point to investigate questions about modeling an
effective interaction design for co-creative systems can be studying creative
collaboration in humans (Davis et al., 2015a). Mamykina et al. argued that by
understanding the factors of human collaborative creativity, methods can be
devised to build the foundation for the development of computer-based systems
that can augment or enhance collaborative creativity (Mamykina et al., 2002).

###  2.2. Interaction Design in Co-creative Systems

Regarding interaction design in interactive artifacts, Fallman stated:
”interaction design takes a holistic view of the relationship between designed
artifacts, those that are exposed to these artifacts, and the socio-cultural
context in which the meeting takes place” (Fallman, 2008). In the field of co-
creativity, interaction design includes various parts and pieces of the
interaction dynamics between the human and the AI, for example - participation
style, communication between collaborators, and contribution type. Now the
question is how researchers and designers can explore the possible spaces of
interaction design in co-creative systems. For instance, turn-taking is the
ability for agents to lead or follow in the process of interaction (Winston
and Magerko, 2017). While designing a co-creative system, should the designer
consider turn-taking or concurrent participation style? Turn-taking models
work well in many co-creative systems but may not fit well for all co-creative
systems. Lauren and Magerko investigated whether the user experience is
improved with a turn-taking model applied to Lumin AI, a co-creative dance
partner, through an empirical study (Winston and Magerko, 2017). However,
their results showed negative user experience with a turn-taking model
compared to a non-turn taking model. The negative user experience resulted
from the dislike for the AI agent to take the lead.

Bown argued that the most practiced form of evaluating artificial creative
systems is mostly theoretical and is not empirically well-grounded and
suggested interaction design as a way to ground empirical evaluations of
computational creativity (Bown, 2014). Yee-King and d’Inverno also argued for
a stronger focus on the user experiences of creative systems, suggesting a
need for further integration of interaction design practice into co-creativity
research (Yee-King and d’Inverno, 2016). There is a lack of a holistic
framework for interaction design in co-creative systems. A framework for
interaction design is necessary to explain and explore the possible
interaction spaces and compare and evaluate the interaction design of existing
co-creative systems for improving the practice of interaction modeling in co-
creative systems.

There are recent developments in frameworks and strategies for interaction in
co-creative systems. Kantosalo et al. proposed a framework to describe three
aspects of interaction, interaction modalities, interaction style and
interaction strategies, in co-creative systems (Kantosalo et al., 2020). They
analyzed nine co-creative systems with their framework to compare different
systems’ creativity approaches even if they are within the same creative
domain (Kantosalo et al., 2020). Bown and Brown identified three interaction
strategies - operation-based interaction, request-based interaction and
ambient interaction in metacreation, the automation of creative tasks with
machines (Bown and Brown, 2018). Bown et al. explored the role of dialogue
between the human and the user in co-creation and argued that both linguistic
and non-linguistic dialogues of concepts and artifacts are essential to
maintain the quality of co-creation (Bown et al., 2020). Guzdial and Riedl
proposed an interaction framework for turn-based co-creative AI agents to
better understand the space of possible designs of co-creative systems
(Guzdial and Riedl, 2019). Their framework is limited to turn-based co-
creative agents and has a focus on contributions and turn-taking. In this
paper we present COFI, a description of a design space of possibilities for
interaction in co-creative systems that includes and extends these existing
frameworks and strategies.

###  2.3. Creative Collaboration among Humans

Sawyer asserted that the creativity that emerges from collaboration is
different from the creativity emerging from an individual where interaction
among the group is a vital component of creativity (Sawyer and DeZutter,
2009). He investigated the process of creativity when emerging from a group by
observing and analyzing improvisational theater performances by a theater
group (Sawyer and DeZutter, 2009) and argued that the shared product of
collaborative creativity is more creative than each individual alone could
achieve. Sonnenburg introduced a theoretical model for creative collaboration,
and this model presents communication among the group as the driving force of
collaborative creativity (Sonnenberg, 1991). Interaction among the individuals
in a collaboration makes the process emergent and complex. For investigating
human collaboration, many researchers stressed the importance of understanding
the process of interaction. Fantasia et al. proposed an embodied approach of
collaboration which considers collaboration as a property and intrinsic part
of interaction processes (Fantasia et al., 2014). They claimed that
interaction dynamics help in understanding and fostering our knowledge of
different ways of engaging with others and argued that it is crucial to
investigate the interaction context, the environment, and how collaborators
make sense of the whole process for gaining more knowledge and understanding
more about collaboration. In COFI, we include components that address the
interaction we observe in human to human collaboration as possibilities for
human-AI co-creativity.

Computer supported cooperative work (CSCW) is computer assisted coordinated
activity carried out by a group of collaborating individuals (Baecker, 1993).
K Schmidt defined CSCW as an endeavor to understand the nature and
characteristics of collaborative work to design adequate computer-based
technologies (Schmidt, 2008). A foundation of CSCW is sense-making and
understanding the nature of collaborative work for designing adequate computer
based technology to support human collaboration. CSCW systems are designed to
improve group communication while alleviating negative interactions that
reduce collaboration quality (Kamel and Davison, 1998). For building effective
CSCW systems for collaborative creative work, many CSCW researchers
investigated creative collaboration among humans to understand the mechanics
of collaboration. For this reason, the design space of CSCW is relevant for
interaction design in co-creative systems.

###  2.4. Sense-making in Collaboration

Sense-making is motivated by a continual urge to understand connections among
people, places, and events to anticipate their trajectories (Klein et al.,
2006). Russel et al. discussed the processes involved in a sense-making:
searching for representations, instantiate representations by encoding the
representations, and utilizing the encoding in task-specific information
(Russell et al., 1993). Davis argued that participatory sense-making is useful
to analyze, understand and model creative collaboration (Davis et al., 2015a).
De Jaegher and Di Paolo also proposed participatory sense-making as a starting
point of understanding social interaction (De Jaegher and Di Paolo, 2007). To
understand participatory sense-making, the definition of sense-making from
cognitive theory is crucial. Sense-making is the way cognitive agents
meaningfully connect with their world, based on their needs and goals as self-
organizing, self-maintaining, embodied agents (De Jaegher, 2013). Introducing
multiple agents in the environment makes the dynamics of sense-making more
complex and emergent as each agent is interacting with the environment as well
as with each other. Participatory sense-making evolves from this complex,
mutually interactive process (Davis et al., 2015a). Participatory sense-making
occurs where ”A co-regulated coupling exists between at least two autonomous
agents where the regulation itself is aimed at the aspects of the coupling
itself so that the domain of relational dynamics constitutes an emergent
autonomous organization without destroying the autonomy of the agents
involved.” (De Jaegher and Di Paolo, 2007). In this quote, De Jaegher and Di
Paolo outlines the process of participatory sense-making where meaning-making
of relational interaction dynamics such as the rhythm of turn-taking, manner
of action, interaction style, etc is necessary (Davis et al., 2016).

Figure 1. Interactional Sense-making in a Co-creation.

To understand interaction dynamics in an open-ended improvisational
collaboration, Kellas and Trees present a model of interactional sense-making
(Kellas and Trees, 2005). They describe two types of interaction in the sense-
making process: interaction between collaborators and interaction with the
shared product (Figure 1). We adapt and extend this model for COFI to ground
our space of possibilities for interaction design on the concept of
interactional sense-making. Interaction with the shared product, in the
context of a co-creative system, describes the ways in which the co-creator
can sense, contribute, and edit content of the emerging creative product.
Interaction between collaborators explains how the interaction between the co-
creators is unfolding through time which includes turn-taking, timing of
initiative, communication etc. Participatory sense-making occurs when there is
a mutual co-regulation of these two interactional sense-making processes
between the co-creators . For example, when both participants are adapting
their responses based on each other’s contribution while maintaining an
engaging interaction dynamic, participatory sense-making occurs.

##  3\. Co-Creative Framework for Interaction Design (COFI)

We develop and present Co-Creative Framework for Interaction Design (COFI) as
a space of possibilities for interaction design in co-creative systems. COFI
also provides a framework for analyzing the interaction design trends of
existing co-creative systems. This framework describes various aspects
involved in the interaction between the human and the AI. COFI is informed by
research on human collaboration, CSCW, computational creativity, and human-
computer co-creativity.

The primary categories of COFI are based on two types of interactional sense-
making of collaboration as described by Kellas and Trees (Figure 2) (Kellas
and Trees, 2005): interaction between collaborators and interaction with the
shared product. Interaction with the shared product, in the context of co-
creative systems, describes interaction aspects related to the creation of the
creative content. Interaction between collaborators explains how the
interaction between the human and the AI is unfolding through time which
includes turn-taking, timing of initiative, communication, etc. Thus, COFI
characterizes relational interaction dynamics between the collaborators (human
and AI) as well as functional aspects of interacting with the shared creative
product. Kellas and Trees’ framework was used for explaining and evaluating
the interaction dynamics in human creative collaboration in joint
storytelling. Understanding collaborative creativity among humans can be the
basis for designing effective co-creative systems where the AI agent acts as a
creative partner.

Each of the two categories of interaction is further divided into two
subcategories. Interaction between collaborators is divided into collaboration
style and communication style. On the other hand, interaction with the shared
product is divided into the creative process and creative product. CSCW
literature discusses collaboration mechanics among the collaborators to make
effective CSCW systems. Many frameworks about groupware and CSCW systems
discuss and emphasize both collaboration components and communication
components among collaborators. For example, Baker et al. proposed an
evaluation technique based on collaboration mechanics for groupware and
emphasized both coordination and communication components in a collaboration
(Baker et al., 2001). Creativity literature focuses more on creativity
emergence, which includes creative processes and the creative product. For
example, Rhodes’s famous 4P, which is one of the most acknowledged model,
includes creative process and product (Rhodes, 1961). Therefore, in COFI, the
literature regarding human collaboration and CSCW literature informs the
category ’interaction between the collaborators’, while the creativity and co-
creativity literature provides descriptions of the ’interaction with the
shared product’. In human-AI co-creativity, the focus should be on both
creativity and collaboration. As a result, both the CSCW and creativity
literature provide the basis for defining the interaction components of COFI
under the four subcategories.

Figure 2. Co-Creative Framework for Interaction Design (COFI): On the left (a)
Components of Interaction between the collaborators, On the right (b)
Components of Interaction with the Shared Product.

We performed a literature review to identify the components of COFI. We
identified a list of search databases for relevant academic publications: ACM
Library, arXiv, Elsevier, Springer, and ScienceDirect, and google scholar. We
used keywords based on the 4 Cs in COFI: Collaboration style, Communication
style, Creative process, Creative product. The total list of keywords are:
’human collaboration mechanics,’ ’creative collaboration among humans,’
’communication in collaboration,’ ’cooperation mechanics,’ ’Interaction in
joint action,’ ’groupware communication,’ ’interaction design in computational
creativity,’ ’interaction in co-creativity,’ ’creative process,’ ’group
interaction in computational creativity,’ ’interaction in human-computer co-
creation’. We considered documents published from 1990 until 2021. We did not
include papers that are a tutorial or poster, papers that are not in English,
papers that by title or abstract are outside the scope of the research, and
papers that do not describe the collaboration mechanics or group interaction.
We included papers describing strategies, mechanisms and components of
interaction in a natural collaboration, computer-mediated collaboration and
human-AI collaboration. COFI was developed in an iterative process of adding,
merging, and removing components based on the interaction components defined
in the literature. We refer to the specific publications that contributed to
each component of COFI in the sections below: for each interaction component
the first paragraph defines the component, and the second paragraph references
the relevant publications that provided the basis for that component.

###  3.1. Interaction between Collaborators (Human and AI)

This section presents components related to the relational interaction
dynamics between the human and the AI as co-creators. As shown in Figure 1(a),
interaction between collaborators is divided into two subcategories which are
_collaboration style_ and _communication style_.

####  3.1.1. Collaboration Style

Collaboration style is the manner of working together in a co-creation. In
COFI, collaboration style comprises participation style, task distribution,
timing of initiative and mimicry as interaction components. The following
subsections describe each interaction component in this category.

Participation Style: Participation style in COFI refers to whether the
collaborators can participate and contribute simultaneously, or one
collaborator has to wait until the partner finishes a turn. Therefore,
participation style in COFI is categorized as parallel and turn-taking. For
example, in a human-AI drawing co-creation, collaborators can take turns to
contribute to the final drawing or they can draw simultaneously.

Participation style in COFI is based on the categorization of interpersonal
interaction into two types: concurrent interaction and turn-based interaction
(Liu et al., 2015). In concurrent interaction, continuous parallel
participation from the collaborators occurs and in turn-based interaction,
participants take turns in contributing. In a parallel participation style,
both collaborators can contribute and interact simultaneously (Penichet et
al., 2007). In a turn-taking setting, simultaneous contribution can not occur
(Penichet et al., 2007). In CSCW research, there is a concept for interaction
referred to as synchronous and asynchronous. Synchronous interaction requires
the real time interaction where the presence of all collaborators is required.
Whereas asynchronous cooperation does not require simultaneous interaction of
all collaborators (Rodden and Blair, 1991; Reinhard et al., 1994; Cacciagrano
and Corradini, 2001). In CSCW, the distinction between synchronous and
asynchronous interaction is information exchange in terms of time. In COFI,
participation style describes the way collaborators participate when all are
present at the same time.

Task Distribution: Task distribution refers to the distribution of tasks among
the collaborators in a co-creative system. In COFI, there are two types of
task distribution, same task and task divided. When it is same task, there is
no division of tasks between collaborators and all the collaborators take part
in the same task. For example, in a human-AI co-creative drawing, both co-
creators do the same task, i.e. generating the drawing. In a task-divided
distribution, the main task is divided into specific sub-tasks and the sub-
tasks are distributed among the collaborators. For example, in co-creative
poetry, the user can define the conceptual space for the poetry and generate a
poem while the AI agent can evaluate the poetry.

Cahan and Fewell asserted that division of task is a key factor in the success
of social groups (Cahan and Fewell, 2004). According to Fischer and Mandl,
task division should be addressed for co-ordination in a computer-mediated
collaboration (Fischer and Mandl, 2003). This component of COFI emerged from
discussions of the two interaction modes presented by Kantosalo and Toivonen:
alternating co-creativity and task divided co-creativity (Kantosalo and
Toivonen, 2016). In alternating co-creativity, each party contributes to the
shared artifact while doing the same task by taking turns. Kantosalo and
Toivonen emphasized the turn-taking in alternating interaction mode. In COFI,
we renamed alternating co-creativity to be same task as we want to emphasize
the task distribution. Task divided in COFI is the same term used in Kantosalo
and Toivenen (Kantosalo and Toivonen, 2016).

Timing of Initiative: In a co-creative setting, the timing of collaborators’
initiative can be scheduled beforehand, or it can be spontaneous. If the
timing of the initiative is planned or fixed in advance, in COFI it will be
addressed as planned. If both agents initiate their contribution without any
prior plan or fixed rules, it will be addressed as spontaneous. Timing of the
initiative should be chosen based on the motivation behind designing a co-
creative system. Spontaneous timing is suitable for increased emergent
results, whereas planned timing is more suitable for systems where users want
inspiration or help in a specific way for a particular aspect of the creative
process.

Salvador et al. discussed timing of initiative in their framework for
evaluating groupware for supporting collaboration (Salvador et al., 1996).
They defined two types of timing of initiative: spontaneous initiatives, where
participants take initiatives spontaneously and pre-planned initiatives, where
group interactions are scheduled in advance. Alam et. al divided interaction
among groups into planned and impromptu (Alam et al., 2013). For COFI, we
merged these ways of describing the timing of initiative into spontaneous and
planned.

Mimicry: COFI includes mimicry as a subcategory of collaboration style which
is used in co-creative systems as an intentional strategy for collaboration.
When mimicry is a strategy for the AI contribution, the co-creative AI mimics
the human user.

Drawing Apprentice (Davis et al., 2015b) is a co-creative web-based drawing
system that collaborates with users in real-time abstract drawing while
mimicking users. The authors demonstrated with their findings that even if the
Drawing Apprentice mimics the user in the creative process, the system engaged
users in the creative process that resulted in generating novel ideas. An
example of a non-mimic co-creative system is Viewpoints AI. Viewpoints AI is a
co-creative system where a human can engage in collaborative dance movement as
the system reads and interprets the movement for responding with an improvised
movement (Jacob et al., 2013).

####  3.1.2. Communication Style

In COFI, communication style refers to the ways humans and AI can communicate.
Communication is an essential component in any collaboration for the co-
regulation between the collaborators and helps the AI agent make decisions in
a creative process (Bown et al., 2020). Communication is critical for
achieving understanding and coordination between collaborators. A significant
challenge in human-AI collaboration is the development of common ground for
communication between humans and machines (Dafoe et al., 2021). Collaborators
communicate in different ways in a co-creation such as, communication through
the shared product and contributions, and communication through different
communication channels or modalities. In co-creative systems, collaborators
contribute to the shared product through the creative process and sense-making
of each others’ contributions during the process and act accordingly.
Communicating through the shared product is a prerequisite in a co-creation or
any collaborative system (Bown and Brown, 2018). Hence, COFI does not include
interaction through the shared product under _communication style_. In COFI,
_communication style_ includes different channels or modalities designed to
convey intentional and unintentional information between users and the AI.
Human to AI communication channels carry information from users to the AI. On
the other hand, AI to human communication channels carry information from the
AI to users.

Human to AI Intentional Communication: Human to AI intentional communication
channels represent the possible ways a human agent can intentionally and
purposefully communicate to the AI agent to provide feedback and convey
important information. In COFI, human to AI communication channel includes
direct manipulation, voice, text and embodied communication. The human agent
can directly manipulate the co-creative system by clicking buttons for giving
instructions, feedback, or input. It can also provide user preferences by
selecting from AI provided options. Using the whole body or gestures for
communicating with the computer will be referred to as embodied. Voice and
text can be also used as intentional communication channels from human to AI.

Gutwin and Greenburg proposed a framework that discusses the mechanics of
collaboration for groupware (Gutwin et al., 1996). Their framework includes
seven major elements and one of them is explicit or intentional communication.
Bard defined intentional communication as the ability to coordinate behavior
involving agents (Bard, 1992). Brink argued that the primary goal of
intentional communication is to establish joint attention (Brinck, 2008). In
the field of human-computer interaction, the communication channel between
humans and computers is described as a modality. The modalities for
intentional communication from human to AI include direct manipulation,
embodied/gesture, text, and voice (Nigay, 2004).

Human to AI Consequential Communication: In COFI, human to AI consequential
communication channels represent the ways the human user unintentionally or
unconsciously gives off information to the AI agent. In other words, this
channel represents the ways a co-creative AI agent can track and collect
unintentional or consequential information from the human user such as eye
tracking, facial expression tracking, biometric data tracking and embodied
movements. AI agents can track and collect various consequential details from
the human to perceive user preference, user agency and engagement. For
example, a posture or facial expression can indicate boredom or lack of
interest.

Gutwin and Greenburg reported consequential or unintentional communication as
a major element of collaboration mechanics, in addition to intentional
communication (Gutwin et al., 1996). Collaborators pick up important
information that is unintentionally ”given off” by others, which is considered
as consequential communication in a human collaboration. Unintentional
communication, such as embodied communication, gaze, biometric measurement and
facial expression are consequential communication (Gutwin et al., 1996).
Revealing the internal state of an individual is termed ’Nonverbal leakage’ by
Ekman and Freisen (Ekman and Friesen, 1969). Mutlu et al. argued that in a
human-AI interaction, unintentional cues have a significant impact on user
experience (Mutlu et al., 2009).

AI to Human Communication: AI to human communication represents the channels
through which AI can communicate to humans. Humans expect feedback, critique
and evaluation of our contribution from collaborators in teamwork. If the AI
agent could communicate their status, opinion, critique and feedback for a
specific contribution, it would make the co-creation more balanced as the
computational agent will be perceived as an intelligent entity and a co-equal
creative partner rather than a mere tool. This communication involves
intentional information from the AI to human. Because the interaction
abilities of a co-creative AI agent are programmed, all of the communication
from the AI is intentional. However, one may ask, can AI do anything
unintentional or unconscious beyond the programmed interaction? A co-creative
AI can have a body and can make a facial expression of boredom. However, can
we call it unintentional or it is also an intentional information designed to
be similar a human’s consequential communication? It can be an interesting
question to ask if consequential communication from the AI to the user is even
possible to design. Mutlu et al. investigated the impact of ’nonverbal
leakage’ in robots on human collaborators (Mutlu et al., 2009), however the
leakage was designed intentionally as part of the interaction design.

In a co-creative setting, the modalities for AI initiated communication can
include text, voice, visuals (icons, image, animation), haptic and embodied
communication (Nigay, 2004). There are some communication channels that work
for both human to AI and AI to human communication, such as text, voice, and
embodied communication. These communication channels are under both categories
to identify the possibilities based on the direction of information flow.

###  3.2. Interaction with the Shared Product

Interaction components related to the shared creative product in a co-creative
setting are discussed in this section and illustrated in Figure 1(b).
Interaction with the shared product is divided into two subcategories,
creative contribution to the product and creative process.

####  3.2.1. Creative Process

Creative process characterizes the sequence of actions that lead to a novel
and creative production (Lubart, 2001). In COFI, there are three types of
creative processes that describe the interaction with the shared product:
generate, evaluate, and define. A co-creative AI can play the role of a
generator, evaluator or a definer depending on the creative process. In the
generation creative process, the co-creative AI generates creative ideas or
artifacts. For example, a co-creative AI can generate a poem along with the
user or produce music with users. Co-creative AI agents evaluate the creative
contributions made by the user in a creative evaluation process. An example of
creative evaluation will be analyzing and assessing a creative story generated
by a user. And in a creative definition process, the AI agent will define the
creative concept or explore different creative concepts along with the user.
For example, a co-creative agent can define the attributes of a fictional
character before a writer starts to write about the character.

The basis of this categorization is the work of Kantosalo et al. that defines
the roles of the AI as generator, evaluator, and concept definer (Kantosalo
and Toivonen, 2016). COFI adopts the categorization of Kantosalo et al. as a
basis for understanding the range of potential creative processes: The
generator generates artifacts in a specific conceptual description, the
evaluator evaluates these concepts, and the concept definer defines the
conceptual space (Kantosalo and Toivonen, 2016). In the recent work of
Kantosalo and Jordanous, they compared their defined roles with the apprentice
framework of Negrete-Yankelevich’s and Morales-Zaragoza, where the roles are
generator, apprentice and master (Negrete-Yankelevich and Zaragoza, 2014).

####  3.2.2. Creative Product

The creative product is the idea or concept that is being created. Creative
product has two interaction components, contribution type and contribution
similarity. We identified these specific components as we focused on various
aspects of contribution making to the shared product as meaning emerges
through the contributions in a collaboration. These components are identified
from the literature and discussed in the following subsections.

Contribution Type: In a co-creation, an individual can contribute in different
ways to the shared product. Co-creators can generate new elements for the
shared product, extend the existing contribution, and modify or refine the
existing contribution. How a co-creator is contributing depends on their
interaction with the shared product and their interpretation of the
interaction. The primary contribution types according to COFI are: ’create
new’, ’extend’, ’transform’ and ’refine’. ’Extend’ refers to extending or
adding on to a previous contribution made by any of the collaborators.
Generating something new or creating new objects is represented by ’create
new’, whereas ’transform’ conveys turning a contribution into something
totally different. ’Refine’ is evaluating and correcting a contribution with
similar type of contribution. For example, in a co-creative drawing, drawing a
tree will be considered ’create new’. Extend is when the collaborator adds a
branch to the tree or extends the roots of the tree. Turning a tree branch
into something else, such as a flower, will be considered a ’transformation’,
different from ’create new’ as it is performed on a previous contribution to
turn it into a new object. ’Refine’ is when the collaborator polishes the
branch of the tree to give more detail.

Contribution types are adopted and adapted from Boden’s categories of
computational creativity based on different types of contribution:
combinatorial, exploratory, and transformational (Boden, 1998). Combinatorial
creativity involves novel (improbable) combinations of similar ideas to the
existing ideas. We adapted ’expand’ and ’refine’ from combinatorial creativity
as ’expand’ is extending the existing contribution and ’refine’ is about
correcting or emphasizing the contribution with similar ideas. Exploratory
creativity involves the generation of novel ideas by the exploration of
defined conceptual spaces and ’creating new’ is adapted from this as users use
explores the conceptual space when creating something new. Transformational
creativity involves the transformation of some dimension of the space so that
new structures can be generated, which could not have arisen before and
’transform’ is adapted from this.

Contribution Similarity: In COFI, similarity refers to the degree of
similarity or association between a new contribution compared to the
contribution of the partner. Near refers to high similarity with the partner’s
contribution and far means less similarity with the partner’s contribution. In
this paper, AI agents that use ‘near’ will be referred to as pleasing agents,
and agents that use ‘far’ will be referred to as provoking agents.

Miura and Hida demonstrated that high similarity and low similarity in
contributions and ideas among collaborators are both essential for greater
gains in creative performance (Miura and Hida, 2004). Both convergent and
divergent exploration have their own value in a creative process. Divergent
thinking is ”thinking that moves away in diverging directions to involve a
variety of aspects”, whereas convergent thinking is demarcated as ”thinking
that brings together information focused on something specific” (the, [n.d.]).
Basadur et al. asserted that divergent thinking is related to the ideation
phase and convergent thinking is related to the evaluation phase (Basadur and
Hausdorf, 1996). Kantosalo et al. defined pleasing and provoking AI agents,
based on how similar their contributions are (Kantosalo and Toivonen, 2016). A
pleasing computational agent follows the human user and complies with the
human contribution and preference. Provoking computational agents provoke the
human by challenging the human-provided concepts with divergent ideas and
dissimilar contribution.

##  4\. Analysis of Interaction Models in Co-creative Systems Using COFI

###  4.1. Data

We used COFI to analyze a corpus of co-creative systems to demonstrate COFI’s
value in describing the interaction designs of co-creative systems. We
initiated our corpus of co-creative systems using the archival website called
the “Library of Mixed-Initiative Creative Interfaces” (LMICI), which archives
many of the existing co-creative systems from the literature (Lib, [n.d.]).
Mixed initiative creative systems are often used as an alternative term for
co-creative systems (Yannakakis et al., 2014). Angie Spoto and Natalia Oleynik
created this archive after a workshop on mixed-initiative creative interfaces
led by Deterding et al. in 2017 (Deterding et al., 2017; Lib, [n.d.]). The
archive provides the corresponding literature and other relevant information
for each of the systems. LMICI archive consists of 74 co-creative systems from
1996 to 2017. However, we used 73 systems from the LMICI archive due to the
lack of information regarding one system. We added 19 co-creative systems to
our dataset to include recent co-creative systems (after 2017). We used the
keywords ’co-creativity’ and ’human-AI creative collaboration’ to search for
existing co-creative systems from 2017 to 2021 in the ACM digital library and
Google scholar. Thus, we have 92 co-creative systems in the corpus that we
used to analyze the interaction designs using COFI. Table 1 shows all the co-
creative systems that we analyzed with corresponding years and references.
Figure LABEL: shows the count of the co-creative systems in our dataset each
year.

Figure 3. Counts of Co-creative Systems in the Dataset per Year.

Table 1. List of Co-creative Systems in the Dataset Sorted by Year. Year | Co-creative Systems
---|---
1996 | Improv (Perlin and Goldberg, 1996)
1999 | GeNotator (Thywissen, 1999)
2000 | NEvAr (Machado and Cardoso, 2000)
2001 | Metasynth (Dahlstedt, 2001)
2003 | Facade (Mateas and Stern, 2003), continuator (Pachet, 2003)
2005 | LOGTELL (Ciarlini et al., 2005)
2008 | CombinFormation(Kerne et al., 2008), REQUEST (Riedl et al., 2008), miCollage(Xiao et al., 2008), BeatBender (Levisohn and Pasquier, 2008), WEVVA (Nelson et al., 2017)
2009 | Terrain Sketching (Gain et al., 2009), JNETIC (Bergen, 2009), Synthetic Audience (O’Neill and Riedl, 2011), The Poetry Machine (Widows and Sandilands, 2009)
2010 | SKETCHAWORLD (Smelik et al., 2010), Tanagra (Smith et al., 2010), Realtime Generation of Harmonic Progressions (Eigenfeldt and Pasquier, 2010),
| JamBot. (Brown et al., 2010), Filter Trouve (Colton et al., 2010), Clap-
along (Young and Bown, 2010), EDME (Lopez et al., 2010), LEMu (Lopez et al.,
2010),
2011 | Shimon (Hoffman and Weinberg, 2011), Stella (León, 2011), Party Quirks (Magerko et al., 2011), Generation of Tracks in a High-end Racing Game (Cardamone et al., 2011),
| ELVIRA (Colton et al., 2011), Creating Choreography with Interactive
Evolutionary Algorithms (Eisenmann et al., 2011)
2012 | Spaceship Generator (Liapis et al., 2012), MaestroGenesis (Szerlip et al., 2012), PINTER (Gilroy et al., 2012), Co-PoeTryMe (Oliveira et al., 2014),
| A formal Architecture of Shared Mental Models (Hodhod and Magerko, 2016),
Impro-Visor (Keller, 2012)
2013 | Sentient Sketchbook (Liapis et al., 2013), Dysphagia (Shaker et al., 2013), Viewpoints AI (Jacob et al., 2013),
| Ropossum (DiPaola et al., 2013), COCO Sketch(Davis, 2013), Sentient
World(Liapis et al., 2013)
2014 | Chef Watson (Pinel and Varshney, 2014), Kill the Dragon and Rescue the Princess (Laclaustra et al., 2014), Nehovah (Smith et al., 2014), Autodesk Dreamcatcher(Oliveira et al., 2014)
2015 | CAHOOTS (Wen et al., 2015), Funky Ikebana (Compton and Mateas, 2015), StyleMachine (met, 2015), Drawing Apprentice (Davis et al., 2015b)
2016 | Improvised Ensemble Music Making on Touch Screen (Martin et al., 2016), AceTalk (Trinh et al., 2016), Chor-rnn (Crnkovic-Friis and Crnkovic-Friis, 2016), Cochoreo (Carlson et al., 2016),
| Evolutionary Procedural 2D Map Generation (Scheibenpflug et al., 2016),
Danesh (Cook et al., 2016), Plecto (Ianigro and Bown, 2016),
| Image-to-Image (Isola et al., 2017), Robodanza (Infantino et al., 2016),
SpeakeSystem (Yee-King and d’Inverno, 2016), TaleBox(Castaño Pérez et al.,
2016),
| ChordRipple (Samuel et al., 2016), Robovie (Kahn et al., 2016), Creative
Assistant for Harmonic Blending (Kaliakatsos-Papakostas et al., 2016),
| Writing Buddy (Samuel et al., 2016), Recommender for Game Mechanics (Machado
et al., 2016)
2017 | TOPOSKETCH (White and Loh, 2017), Trussfab(Kovacs et al., 2017), Chimney (Morreale et al., 2017), FabMachine (Kim et al., 2017),
| LuminAI (Long et al., 2017), GAIA (Goel and Rugaber, 2017), 3Buddy (Lucas
and Martinho, 2017), Deeptingle (Khalifa et al., 2017)
2018 | The Image Artist (Zoric and Gambäck, 2018), DuetDraw (Oh et al., 2018), Robocinni (Ackerman et al., 2018)
2019 | In a silent way (McCormack et al., 2019), Metaphoria (Gero and Chilton, 2019), collabDraw (Fan et al., 2019), DrawMyPhoto (Williford et al., 2019)
2020 | Shimon the Rapper (Savery et al., 2020), ALYSIA (Cheatley et al., 2020), Cobbie (Lin et al., 2020), WeMonet (Li et al., 2020),
| Co-cuild (Deshpande, 2020), IEC (Parente et al., 2020), Creative Sketching
Partner (Karimi et al., 2020)
2021 | BunCho (Osone et al., 2021), CharacterChat (Schmitt and Buschek, 2021), StoryDrawer (Zhang et al., 2021), FashionQ (Jeon et al., 2021)

Figure 4. Count of Co-Creative Systems in Different Creative Domains.

We grouped the systems into 13 categories describing their creative domains.
The categories are Painting/Drawing/Art, Culinary, Dance, Music,
Storytelling/Narrative/Writing, Game Design, Theatre/Performance,
Video/Animation, Photography, Poetry, Industrial and Product Design, Graphic
Design and Humor/Comic. In Figure 4, the count of the systems in each category
is provided. We see the most common creative domains in the corpus are music,
storytelling/narrative/writing, Game design and Painting/Drawing/art. The
distribution shows that some creative domains are are not well represented in
this dataset or rarely used in developing co-creative systems, for example,
culinary, humor, and graphic design.

###  4.2. Coding Scheme

To analyze the interaction design of the existing co-creative systems, we
coded the interaction designs of 92 systems using COFI. Two coders from our
research team independently coded 25% of the systems following COFI. They then
achieved consensus through discussing the disagreements in the codes (Kappa
Inter-rater reliability 0.79). The rest of the systems were coded by a single
coder according to the consensus. For each system, the coding shows all
interaction design components according to COFI. All the interaction
components of the systems were coded according to the information provided in
the corresponding literature. For a specific interaction component, when none
of the subcategories are present in the interaction design, we coded it as
‘None’.

###  4.3. Interaction Design Models among Co-creative Systems

For identifying different interaction models utilized by the co-creative
systems in the dataset, we clustered all the systems using their interaction
components. We used K-modes clustering (Huang, 1997; Cao et al., 2012) for
identifying clusters as the K-modes algorithm is suitable for categorical
data. K-modes clustering is an extension of K-means, but instead of means,
this algorithm uses modes. For demonstrating the cluster centroids, this
algorithm uses modes of all the features. We used all the interaction
components according to COFI as features. We found three clusters of the
systems based on their interaction design (Figure 5). The first cluster
includes 67 co-creative systems and thus indicating a dominant interaction
model. The second cluster includes 9 systems and the third one includes 16
systems. We used chi-square for determining interaction components that
contribute significantly to forming the clusters and found that all of the
interaction components are significant factors for the clusters (all P values
¡ 0.05). Figure 5 shows the three major interaction models, including all the
interaction components (cluster centroids represented by feature modes).

Figure 5. Interaction Designs for the Three Clusters of Co-creative Systems.

####  4.3.1. Cluster 1 - Interaction Design Model for Generative Pleasing AI
Agents

The interaction model of the first cluster is the most prevalent as there are
67 systems in this cluster sharing the same or similar model. This dominant
interaction model shows that most of the co-creative systems in the dataset
utilize turn-taking as the participation style. Therefore, each of the
collaborators must wait until the partner finishes their turn. This
interaction model uses ’planned’ timing of initiative which is an indication
of non-improvisational co-creativity. Hence, most of the systems in the
dataset do not support improvisational creativity. This interaction model uses
direct manipulation for human to AI intentional communication. However, this
model does not incorporate any human to AI consequential communication or AI
to human communication. The main task is divided between the collaborators,
and the AI agent uses generation as the creative process in most of the
systems in this cluster and creates something new without mimicking the user.
The degree of similarity in contribution is high. In other words, the AI agent
pleases the human by generating contributions that follow along with the
contributions made by the human. Mostly, this interaction model is used by
non-improvisational systems that generate creative products to please the
users.

An example of a system that uses this interaction design is Emotion Driven
Music Engine (EDME) (Lopez et al., 2010). EDME generates music based on the
emotions of the user. The user selects an emotion, and EDME plays music to
match that emotion. This system works in a turn-taking way with the user. The
timing of initiative-taking is planned as the system will always respond after
the human finishes selecting their emotion. The task is divided between the
collaborators as the user defines the conceptual space by choosing an emotion
from the interface and the system generates the music according to that
emotion. The system contributes to the collaboration by creating something new
and without mimicking the user. The system creates music that is associated
with and similar to the user-defined emotion. The biggest challenge here is
the human can not give any feedback or communicate with the system regarding
the generated music. The system can not track any consequential information
from the human such as facial expression, eye gaze and embodied gestures.
Also, the system can not communicate any relevant information to the user such
as providing additional information regarding the contribution or visual cues.

####  4.3.2. Cluster 2 - Interaction Design Model for Improvisational AI
Agents

The interaction design for the systems in cluster 2 uses parallel
participation style where both agents can contribute simultaneously. The task
distribution for these systems is usually ‘same task’ and most of the systems
contribute by generating in the creative process. Most of the systems in this
cluster contribute to the collaboration by creating something new and these
systems can do both mimicry and non-mimicry. The degree of similarity in terms
of users’ contribution can be both high and low. This interaction model
employs spontaneous initiative-taking while both co-creators contribute to the
same task with parallel participation style, indicating improvisational co-
creativity. Systems in this cluster do not have any way of communication
between the user and the system, and a lack of communication in
improvisational co-creativity can reduce the collaboration quality and
engagement (Hoffman and Weinberg, 2011).

An example system for this cluster is LuminAI, where human users improvise
with virtual AI agents in real time to create a dance performance (Long et
al., 2017). Users move their body and the AI agent will respond with an
improvised movement of its own. Both the AI agent and users can dance
simultaneously and they take initiatives spontaneously. Collaborators
contribute to only a single task, generating dance movements. The AI can
create new movements and transform user movements while it can do both mimicry
and non-mimicry. The dance movements can be similar or different from the
user. There is no way the user can deliberately communicate with the system or
the system can communicate with the user. Here, the creative product itself is
an embodied product but the system can not collect any consequential
information from the user such as eye gaze, facial expression or additional
gestures other than dance moves.

####  4.3.3. Cluster 3 - Interaction Design Model for Advisory AI Agents

The third cluster includes systems that work in a turn taking manner and the
task is divided into subtasks between the collaborators. The initiative taking
is planned prior to the collaboration. Users can communicate to the system
through direct manipulation, but there is no human to AI consequential
communication channel or AI to human communication channel. The most notable
attribute for this interaction model is both the generation and evaluation
ability of the AI agent unlike the other two interaction models where the AI
agent can only contribute by generating. Systems with this interaction model
can act as an adviser to the user by evaluating the contribution of the user.
Most of these systems in this cluster contribute by refining the contribution
of the user. These systems do not mimic the contribution of the user and the
degree of contribution similarity can be both high and low.

An example of co-creative systems that utilize this model is Sentient World
which assists video game designers in creating maps (Liapis et al., 2013). The
designer creates a rough terrain sketch, and Sentient World evaluates the map
created by the designer and then generates several refined maps as
suggestions. This system works in a turn-taking manner with the user, and the
initiative taking is planned. The AI agent uses both generation and evaluation
as creative processes by generating maps and evaluating maps created by the
user. The user can communicate with the system minimally with direct
manipulation (clicking buttons) for providing user preference for the maps.
The AI agent can not communicate any explicit information to the human and can
not collect any consequential information from the user such as facial
expression, eye gaze and embodied information. Sentient World can both create
new maps and refine the map created by the user. The system does not mimic the
user contribution and the similarity with user contribution is high.

###  4.4. Adoption Rate of the Interaction Components used in the Systems

Figure 6 shows the adoption rate of each of the interaction components in COFI
used in the systems. The first section of the table comprises interaction
components under _collaboration style_. Turn-taking is the most common
participation style in the dataset (89.1%), while just 10.9% of the systems
use parallel participation. Parallel participation is used by the systems that
engage in performative co-creation. Most of the co-creative systems in the
dataset use task-divided distribution of tasks (75%) as they work on separate
creative subtasks. 25% systems use _same task_ as their task distribution as
both the user and the AI work on the same creative task/s. Timing of
initiative is planned in 86.8% of the systems and the rest of the systems take
spontaneous initiatives without any fixed plan. For mimicry, 90.2% of the
systems employ non-mimicry, 8.7% systems use both mimicry and non-mimicry, and
only one system (1.1%) uses mimicry.

The second category, _communication style_ , is concerned with different
communication channels used by the co-creative systems. 69.6% systems use
direct manipulation as the human to AI communication channel. Voice, embodied
and text is used rarely by the systems. 3.3% of the systems use embodied
communication as human to AI consequential communication and most of the
systems (95.7%) do not track and collect any consequential information from
the user. For AI to human communication, most systems do not have any
channels. In the next section, we talk about the trend in communication
channels in co-creative systems.

In the creative process category, it is noticeable that the majority of the
systems (79.3%) employ generation as the creative process and 15.2% of the
systems use both generation and evaluation as the creative processes.
Definition as a creative process is rarely used in the co-creative systems.

In the creative product category, contribution type is the first interaction
component and most co-creative systems use _create new_ (59.8%). 10.9% of the
systems use both _create new_ and _refine_ as the contribution type. 8.7% of
the systems use both _create new_ and _extend_ as the contribution type.

Figure 6. Adoption Rate of Each Interaction Component used in the Co-creative
Systems in the Dataset.

###  4.5. Communication in Interaction Models

Our analysis identifies a significant gap in the use of the components of
interaction in the co-creative systems in this dataset: a lack of
communication channels between humans and AI (Figure 3). In co-creative
systems, subtle communication happens during the creative process through
contributions. For example, in a collaborative drawing co-creative system
where no communication channel exists between the user and the AI, subtle
interaction happens through the shared product as co-creators make sense of
each other’s contribution and then make a new contribution. Designing
different modalities for communication between the user and the AI has the
potential to improve the coordination and quality of collaboration. However,
82.6% of the systems cannot communicate any feedback or information directly
to the human collaborator other than communicating through the shared product.
The rest of the systems communicate with the users through text, embodied
communication, voice, or visuals (image and animation). For human to AI
consequential communication, 95.7% of the systems can not capture any
consequential information from the human user such as facial expression,
biometric data, gaze and postures. However, consequential communication can
increase user engagement in collaboration. For the intentional communication
from human to AI, most of the systems use direct manipulation (clicking
buttons or selecting options) to communicate (69.6%). In other words, in most
of the systems, users can only minimally communicate with the AI or provide
instructions to the AI directly, for example, through clicking buttons or
using sliders. 21.7% of the systems have no way for the user to communicate
with the AI intentionally. The rest of the systems use other intentional
communication methods, like embodied communication or voice or text.

Figure 7. Distribution of Different Kinds of Communication between Humans and
AI in the Co-Creative Systems in the Dataset.

Some of the systems in our dataset utilize multiple communication channels.
Shimon is a robot that plays the marimba alongside a human musician (Hoffman
and Weinberg, 2011). Using embodied gestures as visual cues to anticipate each
other’s musical input, Shimon and the musician play an improvised song,
responding to each other in real-time. The robot and the human both use
intentional embodied gestures as visual cues to communicate turn-taking and
musical beats. Therefore, this system includes human to AI intentional
communication and AI to human communication. Findings from a user study using
Shimon demonstrate that visual cues aid synchronization during improvisational
co-creativity. Another system with interesting communication channels is
Robodanza, a humanoid robot that dances with humans (Infantino et al., 2016).
Human dancers use intentional communication by intentionally touching the
robot’s head in order to awaken it and the robot tracks human faces to detect
consequential information. The robot is able to detect the noise and rhythm of
hands clapping and tapping on a table. The robot can move its head in the
direction of the perceived rhythms and move its hand following the perceived
tempo for communicating its status to the human users.

##  5\. Discussion

We develop and describe COFI to provide a framework for analyzing, comparing,
and designing interaction in co-creative systems. Researchers can use COFI to
explore the possible spaces of interaction for choosing an appropriate
interaction design for a specific system. COFI can be beneficial while
investigating and interpreting the interaction design of existing co-creative
systems. As a framework, COFI is expandable as other interaction components
are added in the future. We analyzed the interaction models of 92 existing co-
creative systems using COFI to demonstrate its value in investigating the
trends and gaps in the existing interaction designs in co-creative systems. We
identified three major clusters of interaction models utilized by these
systems. In the following paragraphs, we explain the interaction models and
discuss the potential for further research in specific interaction components.
These interaction models can be useful when designing a co-creative system
since they can help identify appropriate interaction components and determine
if interaction components should be modified for the corresponding type of co-
creative AI agent.

The most common interaction model in our dataset is suitable for generative
co-creative AI agents that follow and comply with human contributions and
ideas by generating similar contributions. Provoking agents are rare in the
literature, and in fact, such a stance seems to be opposed by some in the
literature. For example, Tanagra’s creators ensured ”that Tanagra does not
push its own agenda on the designer” (Smith et al., 2010). However, both
pleasing and provoking agents have use-cases within co-creative systems
(Kantosalo and Toivonen, 2016). For example, if a user is trying to produce
concepts or ideas that convey their specific style, a pleasing agent that
contributes similar ideas is more desirable. However, if a user is searching
for varied ideas, a provoking agent with different contributions is an ideal
creative partner as it will provide more divergent ideas. This model can be
improved with consequential communication tracking from users and AI to human
communication.

The second interaction model is suitable for improvisational AI agents as it
uses spontaneous initiative-taking and both agents work on the same task in
parallel. Additionally, this model includes both mimicry and non-mimicry,
unlike the other models which direct the AI to take proper action in an
improvisational performance. This model can be utilized as a guide while
designing interaction in an improvisational co-creative system. However, this
model does not include any intentional or consequential communication channels
from humans to AI or AI to humans, which can negatively impact the
collaboration quality and user experience, especially in improvisational co-
creativity where communication is the key. Hoffman et al. asserted that
communication aids synchronization and coordination in improvisational co-
creativity (Hoffman and Weinberg, 2011). Further research can extend this
model by including or extending human-AI communication channels.

The third interaction model is used by co-creative AI agents that work as an
advisor by evaluating user’s contributions and contributing to the shared
product as a generator. In product-based co-creation, AI agents that can both
generate and evaluate help the user generate precise creative ideas and
artifacts. For example, in industrial design, the co-creative AI agent can
help in creative ideation by evaluating the user-provided concept for a robust
and error-free design and also help in the generation of the artifact with
divergent or convergent ideas (Kovacs et al., 2017). AI agents that use this
model can refine the user’s contributions in contrast to the other models. The
limitations of this model include the absence of human to AI consequential
communication and AI to human communication.

A notable finding from the analysis of this dataset is the lack of AI agents
defining the conceptual space as the creative process (only 4 out of 92). Most
of the systems in the corpus contribute by generating and some contribute by
evaluating the human contributions. In the context of co-creativity, defining
the conceptual space is an essential task. An AI agent can define the
conceptual space without any guidance from the user. For example, the Poetry
Machine is a poetry generator that prompts the user with images that users
respond to with a line of poetry (Lib, [n.d.]; Widows and Sandilands, 2009)
and then organizes the lines of poetry into a poem. An AI agent can also
suggest multiple ideas for the conceptual space while the user can select
their preferred one. TopoSketch (White and Loh, 2017) generates animations
based on a photo of a face provided by the human and displays various facial
expressions as ideas for the final animation. CharacterChat inspires writers
to create fictional characters through a conversation. The bot converses with
the user to guide the user to define different attributes of the fictional
character. Humans may desire inspiration for creative concepts and ideas at
the beginning of a creative journey. Creative brainstorming and defining
creative concepts can be potential research areas for co-creative systems.
There is potential for designing new co-creative systems that both define the
creative conceptual space and explore it with the user.

The most significant area of improvement in all of the interaction models
identified is communication, the key to coordination between two agents.
Providing feedback, instructions or conveying information about the
contribution is essential for creative collaboration. Without any
communication channel between the co-creators, the creation becomes a silent
game (Sørensen, 2016, 2017) as collaborators can not express any concerns and
provide feedback about their contributions. Communication through the creative
product is subtle communication and may not be enough to maintain the
coordination and collaboration quality. Most of the existing co-creative
systems in our dataset have minimal communication channels, and this hinders
the collaboration ability of the AI agent and the interactive experience. Most
of the systems in the dataset utilize only direct manipulation for
communicating intentional information from the users. Direct manipulations
include clicking buttons and using sliders for rating AI contribution,
providing simple instructions and collecting user preferences. For most
systems, direct manipulation provides a way for minimal communication and does
not provide users with a way to communicate more broadly. Very few systems in
the dataset use other communication channels other than direct manipulation
for human to AI intentional communication. For example, AFAOSMM (2012) (Hodhod
and Magerko, 2016) is a theatre-based system that uses gestures as intentional
communication and Robodanza (2016) (Infantino et al., 2016) uses embodied
movements along with direct manipulation for intentional communication. Human
to AI consequential communication is rarely used in the systems but an
effective way to improve creative collaboration. It has been demonstrated that
humans, during an interaction, can reason about others’ ideas, goals,
intentions and predict partners’ behaviors, a capability called Theory of Mind
(ToM) (Premack and Woodruff, 1978; Yoshida et al., 2008; Baker et al., 2017).
Having a Theory of Mind allows us to infer the mental states of others that
are not directly observable, enabling us to engage in daily interaction. The
ability to intuit what others think or want from brief nonverbal interactions
is crucial to our social lives as we see others’ behavior not just as motions
but as an intentional action. In a collaboration, Theory of Mind is essential
to observe and interpret the behavior of a partner, maintain coordination and
act accordingly. Collecting unintentional information from the human partner
has the potential to improve the collaboration and user experience in a human-
AI co-creation, and may lead to enabling AI to mimic the Theory of Mind
ability of humans. The technology for collecting consequential information
from the user includes eye trackers, facial expression trackers, gesture
recognition devices, and cognitive signal tracking devices.

AI to human communication channels are also rarely utilized in the identified
interaction models. However, it is essential to understand the AI partner by
the users to build an engaging and trustworthy partnership. Many intelligent
systems lack the core interaction design principles such as transparency and
explainability and it makes them hard to understand and use (Eiband et al.,
2021). To address the challenge of transparency of AI interaction should be
designed to support users in understanding and dealing with intelligent
systems despite their complex black-box nature. When AI can communicate its
decision making process to users and explain its contribution, the system
becomes more comprehensible and transparent to build a partnership. So, AI to
human communication is critical for interaction design in co-creative systems.
Visuals, text, voice, embodied, and haptic feedback can be used to convey
information, suggestions, and feedback to the users. There is a distinction
between AI to human communication and AI steerability. For example, LuminAI is
a co-creative AI that dances with humans (Long et al., 2017). Here the
generated creative product is dance, an embodied product created by gestures
and embodied movements. However, AI can only communicate by contributing to
the product and does not directly communicate to humans. Humans can steer the
AI by contributing different embodied contributions to the final product and
the AI generates contributions based on the user movements. This is different
from embodied communication that intentionally communicates that the
collaborator is doing great with a thumbs up. The gap in interaction design in
terms of communication is an area of future research for the field of co-
creativity. User experiments with different interaction models can help
identify effective interaction design for different types of co-creative
systems (Rezwanaa et al., 2021). COFI provides a common framework for
analyzing the interaction designs in existing co-creative systems to identify
trends and gaps in existing interaction designs for designing improved
interaction in a co-creative system.

AI is being used increasingly in collaborative spaces, for example,
recommender systems, self-driving vehicles, and health care. Much AI research
has focused on improving the intelligence or ability of agents and algorithms
(Ashktorab et al., 2020). As AI technology shifts from computers to everyday
devices, AI needs social understanding and cooperative intelligence to
integrate into society and our daily lives. AI is, however, a novice when it
comes to collaborating with humans (Dafoe et al., 2021). The term ’human-AI
collaboration’ has emerged in recent work studying user interaction with AI
systems (Wang et al., 2019; Oh et al., 2018; Cai et al., 2019; Arous et al.,
2020). This marks both a shift to a collaborative from an automated
perspective of AI, and the advancement of AI capabilities to be a
collaborative partner in some domains. Ashktorab et al. asserted that human-AI
co-creation could be a starting point of designing and developing AI that can
cooperate with humans (Ashktorab et al., 2020). Human-AI interaction has many
challenges and is difficult to design (Yang et al., 2020). HCI deals with
complex technologies, including research to mitigate unexpected consequences.
A critical first step in designing valuable human-AI interactions is to
identify technical challenges, articulate the unique qualities of AI that make
it difficult to design, and then develop insights for future research (Yang et
al., 2020). Building a fair and effective AI application is considered
difficult due to the complexity both in defining the goals and algorithmically
achieving the defined goals. Prior research has addressed these challenges by
promoting interaction design guidelines (Mitchell et al., 2019; Amershi et
al., 2019). In this paper, we provide COFI as a framework to describe the
possible interaction spaces in human-AI creative collaboration and identify
existing trends and gaps in existing interaction designs. COFI can also be
useful in AI research and HCI research to design cooperative AI in different
domains. COFI will expand as we learn and identify more aspects of human-AI
collaboration.

##  6\. Limitations

The identification of clusters of interaction models in human-AI co-creative
systems is limited to the specific dataset that we used for the analysis.
Although we believe this sample contains a large population, the systems in
the dataset are limited by the expectations and technologies at the time of
publication. We expect the clusters and descriptions of interaction models for
co-creative systems will change over time.

##  7\. Conclusions

This paper develops and describes the COFI as a framework for modeling
interaction in co-creative systems. COFI was used to analyze the interaction
design of 92 co-creative systems from the literature. Three interaction models
for co-creative systems were identified: generative pleasing agents,
improvisational agents, and advisory agents. When developing a co-creative
system, these interaction models can be useful to choose suitable interaction
components for corresponding co-creative systems. COFI is broader than the
interaction designs utilized in any specific co-creative system in the data
set. The findings show that the space of possibilities is underutilized. While
the analysis is limited to the data set, it demonstrates that COFI can be a
tool for identifying research directions and research gaps in the current
space of co-creativity. COFI revealed a general lack of communication in co-
creative systems within the dataset. In particular, very few systems
incorporate AI to human communication, communication channels other than
direct manipulation for collecting intentional information from humans and
gathering consequential communication data, such as eye gaze, biometric data,
gesture, and emotion. This gap demonstrates an area of future research for the
field of co-creativity. We argue that COFI will provide useful guidelines for
interaction modeling while developing co-creative systems. As a framework,
COFI is expandable as other interaction components can be added to it in the
future. User experiments with different interaction models can help identify
effective interaction design for different types of co-creative systems and
lead to insights into factors that affect user engagement.

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