# “It’s Not a Replacement:” Enabling Parent-Robot Collaboration to Support In-
Home Learning Experiences of Young Children

Hui-Ru Ho<EMAIL_ADDRESS>0009-0000-3701-2521 Dept. of Educational Psychology
University of Wisconsin–MadisonDepartment of Educational Psychology,
University of Wisconsin–MadisonMadisonWIUSA ,  Edward Hubbard
<EMAIL_ADDRESS>0000-0002-2701-415X Dept. of Educational Psychology
University of Wisconsin–MadisonDepartment of Educational Psychology,
University of Wisconsin–MadisonMadisonWIUSA and  Bilge Mutlu
<EMAIL_ADDRESS>0000-0002-9456-1495 Dept. of Computer Sciences
University of Wisconsin–MadisonDepartment of Computer Sciences, University of
Wisconsin–MadisonMadisonWIUSA

(2024)

###### Abstract.

Learning companion robots for young children are increasingly adopted in
informal learning environments. Although parents play a pivotal role in their
children’s learning, very little is known about how parents prefer to
incorporate robots into their children’s learning activities. We developed
prototype capabilities for a learning companion robot to deliver educational
prompts and responses to parent-child pairs during reading sessions and
conducted in-home user studies involving 10 families with children aged 3–5.
Our data indicates that parents want to work with robots as collaborators to
augment parental activities to foster children’s learning, introducing the
notion of parent-robot collaboration. Our findings offer an empirical
understanding of the needs and challenges of parent-child interaction in
informal learning scenarios and design opportunities for integrating a
companion robot into these interactions. We offer insights into how robots
might be designed to facilitate parent-robot collaboration, including
parenting policies, collaboration patterns, and interaction paradigms.

Human-robot interaction, parent-robot collaboration, parent-child dyads, young
children, informal learning, home, field study

††journalyear: 2024††copyright: rightsretained††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642806††isbn:
979-8-4007-0330-0/24/05††submissionid: 7631††ccs: Human-centered computing
Collaborative interaction††ccs: Applied computing Interactive learning
environments Figure 1. We explored how parents used and envisioned a learning
companion robot to support young children’s learning at home and introduce the
notion of parent-robot collaboration as a mechanism for successful integration
of robots into the home.

##  1\. Introduction

As care robots have become more common and entered into formal and informal
learning environments, a substantial literature has discussed how social
robots could be designed to support children in school-based learning (Lopez-
Caudana et al., 2018; Davison et al., 2020; Kennedy et al., 2016; Gordon et
al., 2016), home-based learning (Michaelis and Mutlu, 2018, 2019; Ho et al.,
2021, 2023), as well as various other aspects of life such as well-being
(Henkel et al., 2020) and daily routines (Chan et al., 2017). Most of the
existing research has primarily concentrated on interaction design between a
robot and a single child (Kennedy et al., 2016; Tanaka and Kimura, 2009; Chen
et al., 2020; Ali et al., 2019) or a robot with multiple children (Escobar-
Planas et al., 2022), with little work starting to consider the potential
aspect of including a parent in the interaction (Chan et al., 2017; Chen et
al., 2022).

Recent field deployments of learning companion robots suggest that the use of
these robots in a home environment usually involves complex social dynamics.
Michaelis et al. (2023) conducted a four-week in-home deployment study and
found that a social robot naturally elicited involvement of multiple family
members who all engaged with the robot in their individual ways. They
recommended offering parents regular reports about children’s learning
activities with the robot. Moreover, established work in developmental
psychology has shown that parents or primary caregivers play critical roles in
promoting social and intellectual development, especially in young children
(Blevins-Knabe and Austin, 2016; Duncan et al., 2007; Peck et al., 1992). Not
only the frequency, but also the quality, of parental involvement in
children’s learning can significantly influence children’s life outcomes and
overall well being (Harris and Goodall, 2008; Sui-Chu and Willms, 1996;
Elliott et al., 2017). The quality of parental participation in children’s
learning activities also differ from family to family based on individual
characteristics of parents (Dowker, 2021; Elliott et al., 2017).

We would therefore like to know what kind of social dynamics might emerge in
interactions between a learning companion robot and parent-child pairs, as
well as how parents perceive and envision their use of a learning companion
robot to support their children’s learning activities at home. Without a
thorough understanding of how parents make use of a learning companion robot
at home, there is a dearth of design choices for integrating parents into
existing child-robot interaction paradigms. It is therefore crucial to
understand how social robots could be designed to work together with
individual parents or caregivers, and thereby support young children’s
learning by empowering parents in their strengths, supplementing their
weaknesses with assistance from the robot, and minimizing parental concerns
via ethical design processes.

In this work, which is part of a larger project on designing learning
companion robots, we conducted field studies and interviews with families in
their homes using a learning companion robot as a technology probe designed to
facilitate reading and math learning. We discussed with parents how they
perceive their lived experience interacting with a social robot and their
child, and how they envision such technology being designed to support their
implementation of parenting strategies within an educational context.

We found a significant interest in managing parental involvement in children’s
learning activities to the extent that parents saw the robot as a collaborator
with them. In addition, we present themes related to parents’ preferences and
vision of their collaboration with the robot, including factors related to
their own parenting policies, factors related to robots’ interaction
management, and determining factors for the distribution of work between the
parent and the robot. We later explain how these themes interconnect and the
resulting design considerations for future robots that support parent-child
pairs for in-home learning experiences.

##  2\. Related Work

###  2.1. Learning Companion Robots

Social robots represent a growing technology that could provide social
interactions and physically embodied presence. These affordances have the
potential to convert an individual learning experience into a socially-
enriched one (Michaelis and Mutlu, 2018). Compared to virtual agents,
physically embodied robots can similarly offer adaptive assistance
(Ramachandran et al., 2019; Leyzberg et al., 2014; Schodde et al., 2017) and
verbal communications (Brown and Howard, 2014), yet distinctly support
learners’ engagement with the physical world and elicit higher-level social
behaviors during learning (Belpaeme et al., 2018), thereby promote better
learning outcomes (Leyzberg et al., 2012). Prior work has shown that social
robots can effectively support school-based learning in mathematics (Lopez-
Caudana et al., 2018), science (Davison et al., 2020), and literacy (Kennedy
et al., 2016; Gordon et al., 2016). Similarly, they can enhance children’s
learning during home-based activities such as reading (Michaelis and Mutlu,
2018, 2019), number board games (Ho et al., 2021), and math-oriented
conversations with parents (Ho et al., 2023).

Prior work has demonstrated how social robots could support children’s
learning within varying social dynamics and stakeholders. We can categorize
these studies based on the number and type of agents that are involved into
interactions involving (1) a robot and a single child, (2) a robot and
multiple children, (3) a robot, multiple children, and an adult, (4) a robot,
a child, and an adult. First, a substantial body of work has focused on how a
social robot could support a single child. For example, Kennedy et al. (2016)
demonstrated how children learn elements of a second language from the robot
in a short-term dyadic interaction context; on the other hand, Tanaka and
Kimura (2009) showed how a child can take the role of a teacher to instruct
the robot as a novice, gaining self-confidence and improving learning
outcomes. Chen et al. (2020) suggested an adaptive role-switching model to
flexibly accommodate a child’s personalized needs for being a tutor or a tutee
dynamically in a one-on-one interaction with a social robot during vocabulary
learning. In addition, beyond the context of structured learning, Ali et al.
(2019) demonstrated how an autonomous social robot can foster a child’s
creativity using verbal and non-verbal behaviors.

Secondly, some studies have addressed how a social robot interacts with
multiple children in educational contexts. For instance, Escobar-Planas et al.
(2022) investigated how children interact with a social robot in pairs during
a problem solving task and found individual differences in the perceived role
of the robot based on varying degrees of robot expressivity, suggesting the
importance of considering individual perception when designing the robot to
support children’s learning in groups. In addition, Kanda et al. (2012)
uncovered how social behavior exhibited by a robot supported the initial
building of relationships among children for collaborative learning in a
classroom setting.

Adding an adult to each of the dynamics mentioned above results in the third
and the fourth forms of social dynamics with a social robot. The third form of
interaction with a robot involves multiple children and an adult who could be
a teacher using the robot in a classroom setting (You et al., 2006). For
instance, You et al. (2006) introduced a humanoid robot into a English class
as a teaching assistant to support the teacher motivating a group of children.
The fourth form of interaction with a robot involves a child and an adult who
could be a parent (Gvirsman et al., 2020; Chan et al., 2017; Cagiltay et al.,
2020; Chen et al., 2022). Although several studies describe parental
perspectives on incorporating a social robot in their child’s learning, most
of them have not integrated a situated interaction experience with a social
robot into their methods, relying instead on surveys or interviews facilitated
by pictures of social robots. These studies suggested that parents hold a
positive attitude towards the potential of integrating a social robot in their
children’s learning either at home or school (Tolksdorf and Rohlfing, 2020;
Lee et al., 2008; Oros et al., 2014; Louie et al., 2021). However, they also
reported several concerns including technical challenges (Tolksdorf and
Rohlfing, 2020), undesirable replacement of the teacher’s role (Lee et al.,
2008), and disruption of children’s school-based learning (Louie et al.,
2021). A few studies we found incorporated a physical robot in study sessions
with a parent and a child to induce insight into this interaction dynamic
(Gvirsman et al., 2020; Chan et al., 2017; Chen et al., 2022). Gvirsman et al.
(2020) developed a robotic system called Patricc that promoted parent-toddler-
robot (toddlers aged 1–3 years old) triadic interaction more than a tablet in
a 8-minute learning session, measured by video-coded gaze. Another study
designed a technology-based intervention, called WAKEY, for facilitating
morning routines of preschoolers (aged 3–6 years old). In a week-long
evaluation study, WAKEY was found to mitigate parental frustration about
communication regarding morning routines with children and make positive
impact on parents’ attitude in parenting and communication (Chan et al.,
2017). In addition, Chen et al. (2022) designed a platform that integrated a
Jibo robot and an Android tablet to facilitate parent-child co-reading
activities in a 3–6 week long deployment study, targeting families with
children aged 3–7 years old. They uncovered that social robots could improve
various aspects of parent-child interaction, recommending that the robot’s
role in a group setting should be explored from individual group members’
perspective and that these opinions should be further integrated in the design
of the robot.

###  2.2. Parent Involvement in Children’s Learning

Parents are one of the most crucial socialization agents that influence the
acquisition and development of a broad spectrum of cognitive and social skills
in children (Blevins-Knabe and Austin, 2016; Duncan et al., 2007; Peck et al.,
1992). Extensive literature in the field of educational psychology has
consistently demonstrated the positive impact of parental engagement in
children’s learning on their academic achievements (Fehrmann et al., 1987;
Hill et al., 2004). Moreover, active parental involvement within the home
environment holds particular promise for enhancing learning outcomes,
surpassing the effects observed in a school setting (Harris and Goodall, 2008;
Fantuzzo et al., 2004; Sui-Chu and Willms, 1996). Furthermore, Hoffner and
Buchanan (2002) identified a correlation between the extent of parental
guidance and children’s age, with younger children eliciting more parental
mediation compared to their older counterparts. These findings underscore the
importance of promoting parental involvement, especially within the home, in
the education of young children.

###  2.3. Parent-mediated Technology Use

As various forms of technology and media become increasingly prevalent,
parents are increasingly taking an active role in managing their children’s
use of these technologies. Parental mediation is critical for young children,
as it further determines how children’s learning and development is induced by
the media they are interacting with. Warren (2001) defined parental mediation
as “strategies parents use to control, supervise or interpret media content
for children.” Parents mediate children’s use of technology using different
strategies depending on various factors such as their confidence in using
technology (Dias et al., 2016).

Parental mediation theory has been continuously expanded by the Human-Computer
Interaction (HCI) community to explain the involvement of parents in
children’s technology and media usage in a broader context. The foundational
version of parental mediation theory originated from the patterns of watching
television, encompassing three primary strategies: restrictive mediation,
active mediation, and co-use (Valkenburg et al., 1999). Restrictive mediation
refers to how parents make rules to guide or prohibit the viewing of certain
content; active mediation (sometimes called “instructive mediation” or
“evaluative mediation”) refers to how parents discuss the content with
children during or after viewing; co-viewing refers to situations when parents
and children watch television together, promoting parent-child connections
(Valkenburg et al., 1999).

Apart from television, parent mediation theory has also been extended to
explain the use of emerging technologies such as the internet (Durak and
Kaygin, 2020), video games (Nikken and Jansz, 2006), mobile devices/games
(Sobel et al., 2017), creation-oriented educational media (Yu et al., 2021),
and conversational agents (Beneteau et al., 2020). First, Nikken and Jansz
(2006) suggested that parental mediation strategies in video-game play for
children are comparable to those for television-watching, after conducting a
survey-based study. They further explained how specific contextual factors
such as parents’ game behavior relate to these strategies. Moreover, parental
mediation strategies were re-interpreted from traditional views for a
location-based mobile game. Through a mixed-methods study, Sobel et al. (2017)
found that the location-based mobile game, Pokemon Go mitigated parents’
traditional screen-time concerns, as it encouraged outdoor activity and
promoted opportunities for parent-child bonding. This study exemplifies how
the use of a different kind of technology can shift the interpretation of a
restrictive mediation strategy (e.g., restriction of screen time) from a
traditional perspective (e.g., great concerns about screen time) towards a
modified view (e.g., mitigated concerns about screen time). Furthermore,
Beneteau et al. (2020) recommended including the concept of augmented
parenting in addition to the existing mediation strategies for a
conversational agent. They investigated how voice-based interfaces, such as
Alexa, affected parent-child dynamics at home and found that voice interfaces
fostered communication between parents and children, disrupted each others’
access to the technology through interruption or parental control, and
augmented parenting practices. They argued that as the access to the
technology is democratized (i.e., all family members have independent access
to the device), parents might use technology to both mediate and augment
parenting practices. Finally, Yu et al. (2021) created a different mediation
framework for the context of using creation-oriented educational media,
inspired by the parent mediation theory. The newly-inspired mediation
framework included creative mediation, preparation mediation, and
administrative mediation. Each of these components partially reflected the
notion of the three strategies from the original framework (i.e., restrictive
mediation, active mediation, and co-viewing), but afforded its own meaning
tailored to the specific educational context.

The evolving framework or interpretation of parental mediation theory for
different kinds of technology reflects the distinct needs of parental
involvement, influenced not only by contextual factors but also by the
affordances of the technology. However, very little has been done to explain
the parental involvement needs for social robots in the context of education.
In our work, we intend to explore how parents prefer and envision the adoption
of a companion robot in their interactions with their child during in-home
learning.

Overall, studies related to interactions between a robot and a child or
multiple children are relatively abundant. However, more attention needs to be
oriented towards including adults in these interactions, given the importance
of mediating technology use, especially for young children. Although some work
has been done to understand the interaction dynamic among a robot, a child,
and a parent, questions related to how a parent would adopt a social robot to
mediate the use of the robot and foster learning in children as well as how
the design for such interaction should be like are yet unclear. We
investigated a different age group from Gvirsman et al. (2020) and a different
learning activity from Chen et al. (2022), while Chan et al. (2017) is not
relevant to a learning context but daily routines. To fill in the research
gap, we aim to address the following research questions in this work:

  1. (1)

RQ1: How do parents perceive and envision their use of a learning companion
robot to facilitate a child’s learning experience?

  2. (2)

RQ2: What are the considerations for designing a learning companion robot for
parents to engage their child in learning?

Table 1. Participant Demographics ID |  Child Age (month) |  Child
Gender |  Parent
Gender |  Ethnicity |  Maternal
Education |  Household
Income
---|---|---|---|---|---|---
1 |  55 m |  Male |  Female |  White |  Associate’s (2-year college) |  $15,000-$24,999
2 |  60 m |  Female |  Female |  Hispanic |  High School |  ¡ $15,000
3 |  60 m |  Male |  Female |  White |  Master’s |  $75,000-$99,999
4 |  55 m |  Female |  Female |  White |  Bachelor’s |  $100,000-$149,999
5 |  44 m |  Male |  Female |  White |  Master’s |  $150,000-$199,999
6 |  56 m |  Female |  Female |  Biracial |  Master’s |  $100,000-$149,999
7 |  42 m |  Male |  Female |  Black |  Ph.D. |  $150,000-$199,999
8 |  53 m |  Female |  Female |  White |  Master’s |  $50,000-$74,999
9 |  51 m |  Male |  Female |  Biracial |  Bachelor’s |  $75,000-$99,999
10 |  42 m |  Male |  Female |  White |  Master’s |  $75,000-$99,999

##  3\. Method

###  3.1. Participants

After obtaining institutional research ethics approval, we recruited families
from cities in the Midwestern United States via email distributed to community
centers and university employee mailing lists. We selected participants based
on the following criteria: (1) one parent and one child were required to
participate in the study together, (2) the child must have been between 3 and
5 years old, and (3) both the parent and the child were able to understand and
communicate in English. We selected this specific age group as it corresponds
to a period when informal learning experiences with parents at home are
relatively important (Purpura and Lonigan, 2013), occurring before children’s
active participation in formal schooling. Each family received $25 USD as
compensation upon completion of the study.

Our analysis includes data from 10 parent-child dyads, each with a child aged
between 3 and 5 years old (4 female, 6 male, $M=4.3$ years old, $SD=0.58$
years old). Each study visit involved one parent and one child. Other parents
and siblings were sometimes present nearby but did not take part in the study
activities. Participant demographics are listed in Table 1. We note that all
parents who chose to participate in the study were female as mothers typically
serve as primary caregivers (Schoppe-Sullivan et al., 2013) and are
accordingly the ones who were willing to participate in the study. In
addition, despite our efforts to recruit a diverse sample, our final sample
offers limited socio-demographic representation (80% of the mothers have at
least a Bachelor’s degree, 80% of families have at least $75,000 annual
income), which may further constrain the generalizability of our results and
omit insights into the unique needs of demographic groups that are less
represented in our sample. We will discuss this limitation more thoroughly in
Section 5.3.

Figure 2. Our study materials included a social robot (Misty II), a warm-up
booklet, two storybooks, and recording devices. Parent-child pairs read books
at a table with the robot placed in front of them.

###  3.2. Study Design

We conducted short-term in-home visits with families and used a learning
companion robot as a technology probe to foster discussion on how a parent
would engage and use the robot to support their child in learning and how they
envisioned the robot should be designed for parent-child interaction.

Our study objectives align with the three main goals described by Hutchinson
et al. (2003) about the technology probe approach–understanding users’ needs
and perception about the technology in a real-world setting, field-testing a
technology, and motivating design of new technology. With the limited
understanding of how parents might adopt a learning companion robot to
interact with their child, introducing the learning companion robot with
simple and flexible functionalities allowed us to explore unknown needs and
preferences of families, understand their interpretation of how to use the
technology, and inform new design opportunities for future improvements. In
the following sections, we describe the details of the study materials and
procedure.

####  3.2.1. Study Materials

The study materials (Figure 2) included (1) a Misty II social robot,111Misty
Robot: https://www.mistyrobotics.com/products/misty-ii/ which is a semi-
humanoid robot with a 4-inch LCD display for its face where customizable
facial expressions can be displayed; (2) a Raspberry Pi,222Raspberry Pi:
https://www.raspberrypi.org/ which supports the software operations of the
robot; (3) materials for the reading activity (i.e., one warm-up booklet and
two story books)–the warm-up booklet was drawn and designed by the first
author to familiarize parent-child dyads with how they can interact with the
robot; two storybooks include one called Pete the Cat and the Perfect Pizza
Party (40 pages)333Book: Pete the Cat and the Perfect Pizza
Party:https://www.amazon.com/Pete-Cat-Perfect-Pizza-Party/dp/0062404377 and
the other called How to Babysit a Grandma (32 pages),444Book: How to Babysit a
Grandma: https://www.amazon.com/How-Babysit-Grandma-
relationships/dp/0385753845 which were selected based on the appropriate
reading age and book length; (4) recording devices (i.e., a video camera and
an audio recorder), which were placed around the participants to capture
participants’ behavior and conversations.

####  3.2.2. Study Procedure

First, the experimenters offered the families an overview of the study and
obtained informed consent: parents signed consent forms, and children gave
verbal assents. Next, families were given the warm-up booklet to familiarize
themselves with the learning companion robot. Then, families completed reading
two storybooks with the robot. Finally, parents took part in a semi-structured
interview on their experiences and reflections. Each study took around 70
minutes in total, with a 40-min reading session followed by a 30-min
interview. Two experimenters were present throughout the reading session.
Experimenters sat out of participants’ sight to observe their interactions,
take field notes, and answer technical questions from the participants.

####  3.2.3. Interaction Design

Figure 3. The interaction features of the robot: (A) users scanned tags in the
book to initiate prompts, and (B) they pressed the right-front bumper to
repeat what the robot said and the left-front bumper to initiate a response to
the current prompt.

The main activity that the parent-child pairs did in the study was to read two
books with the robot. In this section, we explain how users interacted with
the robot during reading and how the prompts and responses of the robot were
developed. The interaction design is also illustrated in Figure 4.

Before the families read the two books we provided, they were given a warm-up
booklet which was drawn and designed by the first author in the format of a
hand-made pictorial book, providing information and examples for how to
interact with the robot. The booklet included guided activities that allowed
families to practice showing tags to the robot, pressing bumpers to repeat
what the robot previously said and obtaining responses.

After the warm-up activity, families were told to finish reading two books,
including Pete the Cat and the Perfect Pizza Party and How to Babysit a
Grandma, with the robot and were given the choice to start with either one.
Families were told that they were not required to initiate every tag and
response, and were encouraged to use the features of the robot whenever they
wanted to during the reading activity.

During the reading sessions, the robot provided verbal prompts and responses,
generated using Google’s cloud text-to-speech engine,555Google Cloud Text-to-
Speech: https://cloud.google.com/text-to-speech/ when initiated by the
participants. To initiate a verbal prompt, families needed to display April
Tags666April Tags: https://april.eecs.umich.edu/software/apriltag on the books
to the robot. These April Tags were implemented in the book for every two
pages and allowed the robot to autonomously recognize the given page and
provide an appropriate prompt. To initiate a response to the previously-
initiated prompt, families needed to press the left-front bumper button along
the robot’s base. The Repeat bumper on the right-front portion of the robot’s
base allowed families to repeat the robot’s previous prompt. The robot’s
interaction features are also illustrated in Figure 3. We note that the
experimenters did not instruct parent-child pairs regarding who should scan
the tag or who should press the button. Parents and children interacted with
the features naturally. It is possible that sometimes either the parent or the
child always initiated the tags or the responses, sometimes both of them were
involved interchangeably, and sometimes they never press the button to obtain
the robot’s responses as the parents were confident about the answers.

The reading activity is based on the notion of math talk, where parents
incorporate math concepts in daily-life conversations with their child to
foster development of math concepts. We developed prompts related to basic and
advanced math concepts for preschoolers based on literature in educational
psychology (Purpura and Lonigan, 2013; Engel et al., 2013) and these prompts
were delivered by the robot when participants show the corresponding tags on
the book to the robot. The reading activity allowed parents to engage in
problem solving and math-oriented conversations with their child. We chose
reading as it is a common parent-child shared activity and has also been
widely used to study in previous work to support language and math learning
(Vandermaas-Peeler et al., 2009; Purpura et al., 2017).

The framework underlying the design of prompts implemented in the robot was
developed based on the informal numeracy structure proposed by Purpura and
Lonigan (2013) and the classification rules of math proficiency levels
proposed by Engel et al. (2013) for preschool children. Specifically, the
informal numeracy skills (Purpura and Lonigan, 2013) were categorized into one
of the four math proficiency levels specified in the framework proposed by
Engel et al. (2013).

####  3.2.4. Semi-structured Interview

We conducted a semi-structured interview with parents after they finished
reading two storybooks with the child while engaging the learning companion
robot. The interview protocol included questions about parents’ overall
experiences (e.g., “What do you think about the interaction with the robot
overall”), comparisons with their regular informal learning experiences at
home (e.g., “How would you compare this reading experience with your regular
ones at home”), opinions on what they preferred the robot to support (e.g.,
“How would you prefer the robot to support you”), and how they envisioned the
use of the robot in their daily scenarios related to children’s learning
(e.g., “How would you see yourself using the robot in your daily life to
support [child]’s learning”). Other questions were generated by the first
author during the study, based on the parents’ responses to the open-ended
questions from the protocol and the field notes taken during their reading
sessions. Interview sessions were audio recorded and transcribed for analysis.

Figure 4. The interaction design for the reading activity. (1) The robot
introduced itself to users and users used the warm-up booklet to learn and
familiarize themselves with ways to interact with the robot. (2) Users
selected a book to read. (3) Users initiated prompts from the robot when
encountering a tag in the book during reading. (4) Users initiated a response
to the current prompt from the robot by pressing the robot’s left-front
bumper.

###  3.3. Data Analysis

To anonymize our participants in the data, we report the findings using
participants’ ID assigned by the experimenters based on their order of
completing the study. For example, P1 refers to the first participant. We
conducted a reflexive Thematic Analysis (TA) based on Clarke and Braun (2014)
and McDonald et al. (2019) on the semi-structured interviews to identify
patterns in the data and derive a grounded understanding of parents’
perspectives. First, the first author and an undergraduate assistant, who is
experienced in qualitative coding, transcribed and familiarized themselves
with the data based on the audio and video recordings. Second, the first
author generated initial semantic codes based on her interpretation of the
data and categorized the data based on the codes. For example, code “leverage
robot’s responses to engage children” and code “use robot’s answers as a
source of validation” were both categorized as “parents’ use of robot’s
response.” Third, both the first author and the undergraduate assistant coded
the data from P1 with the initial codes, followed by an iterative discussion
to find consensus on interpretations and emergent candidate themes and to
generate an agreed code book. Fourth, for the rest of the data, the first
author and the undergraduate assistant used the code book to code the rest of
the data independently and peer-reviewed each others’ codes. For instance, the
first author coded P2 and the assistant peer-reviewed P2; the assistant coded
P3 and the first author peer-reviewed P3. The finalized codes were reviewed
and discussed iteratively. Lastly, we reviewed, refined, and constructed the
themes as findings based on meaningful and insightful patterns.

Figure 5. An overview of the results. We identified three main themes,
including (1) parent-robot collaboration (parent/robot relative capability,
parent availability, and parent motivation), (2) parent-guided factors
(educational objectives and concerns about use of robot), and (3) robot-guided
factors (interaction dynamic and interaction initiative).

##  4\. Results

Our analysis of interview data from parents highlights three interacting
themes informing the design of a learning companion robot that supports the
context-based needs for parent-child interaction. The first theme, parent-
robot collaboration, highlights parents’ wanted to work with the robot as
collaborators based on relative capabilities between the parent and the robot,
the availability of the parent, and the motivation of the parent. A
consideration of the relative capabilities of the parent and the robot would
involve assigning more responsibility in a learning task to the party with
higher skill on that task (Horvitz, 1999). For example, the task of generating
educational prompts might be delegated to the robot, while the parent might
offer personalized scaffolding for children. The parent’s availability can
help determine times when the parent has limited availability, such as when
taking care of younger siblings, and assign more responsibility to the robot.
Finally, the parent might situationally lack motivation in pursuing some
learning activities or repeating prompts, which might provide an opportunity
for the robot to assume more responsibility.

The second theme, parent-guided factors on collaboration, underlines parent
expectations for the robot to respond to their overarching educational
objectives and concerns for the use of a robot at home. The learning objective
can determine the necessary tasks for a learning activity. For example, the
parent’s learning objective might be understanding the process of problem
solving, in which case either the parent or the robot needs to explain the
process rather than only provide an answer. The concerns will define the
constraints for the tasks and the corresponding delegation of the tasks,
unless a design feature effectively mitigates the concern. For example, the
parent’s concern might be about the privacy of their video data, in which case
they may not delegate tasks that require computer vision or video capturing to
the robot. And the third theme, robot-guided factors on collaboration,
highlights parents’ vision that the robot flexibly enables and manages
effective interaction paradigms, including the concept of interaction dynamics
and interaction initiatives, in line with the collaboration patterns and
parental expectations. The notion of interaction dynamic refers to how the
robot manages the number and kind of participants as well as the involvement
of each party. For example, the robot might need to switch to a different
interaction mode when the parent is involved in its interaction with the
child. The idea of interaction initiatives pertains to how much autonomy the
robot is granted to initiate an interaction. For example, the robot might
initiate an interaction anytime when free playing but initiate only as
requested when reading with the child. We present the details of each theme in
the following sections, and a summary of these findings can be found in Figure
5.

Figure 6. Summary of Theme 1: Parent-robot collaboration. We found that
parents wanted to work with the robot as collaborators based on parent/robot
relative capability (C), parents’ availability (A), and parents’ motivation
(M).

###  4.1. Parent-Robot Collaboration

In our data, parents frequently discussed several tasks (e.g., generating
creative educational prompts for specific learning concepts, offering
personalized scaffolding, etc.) that must be completed to achieve the learning
goals they expected for their child. More specifically, they illustrated how
these tasks should be delegated between themselves and the robot as they
reflected on their as well as the robot’s strengths and weaknesses in daily
learning scenarios. This pattern in our data suggested that parents were
thinking more about how they could collaborate with the robot, which was
further verified by their explicit description for the robot’s role as their
“collaborator.” In addition, task delegation choices were based on three
dimensions: parent/robot relative capability, parent availability, and parent
motivation. A summary of these findings can be found in Figure 6.

####  4.1.1. Robot as a collaborator

The majority of parents (P3, P4, P5, P7, P8, P9, P10) wanted the robot to act
as their collaborator to alleviate some of their workload rather than having
the robot replace their responsibility entirely. P5 summarized this
perspective by stating, “I think the robot is partnering with me. I don’t
think it’s taking the place of me.” In addition, P8 highlighted the robot’s
role as a team member to alleviate her authoritative stance with her child,
saying “Misty and I are a team and we are working together to try to grow
those concepts.” Furthermore, P9 and P10 emphasized how the robot offloaded
pressure from them by generating appropriate intellectual prompts during their
interactions. As articulated by P9, “parent is still very involved in being
the teacher, but the robot takes the pressure off of the parent to have to
come up with questions.” Although some parents also used different terms to
describe the robot’s functional role as, for instance, a facilitator (P4, P7),
a caregiver (P1, P2), or an educator (P1, P7), their articulations of how the
robot served these roles are still conceptualized as a collaborator. We argue
that parents perceived the robot’s functional roles differently depending on
which tasks the robot collaborated with parents on. For example, the robot may
have been perceived more as a facilitator if it helped with prompting parent-
child conversations. On the other hand, the robot may have been perceived more
as a tutor if it helped with demonstrating a problem solving process. In
whichever case, the robot served as a collaborator to the parent by offering
different kinds of support, leading to the perception of different functional
roles while staying under the umbrella of a collaborator. In the following
sections, we further describe what factors should be considered to determine
the complicated patterns of parent-robot collaboration.

####  4.1.2. Parent/Robot Relative Capability

The relative capability between the parent and the robot refers to the
strengths and weaknesses of the parent and the robot with respect to each
other in carrying out certain tasks. For each task, the more proficient party
is likely to assume more authority. We describe the collaborative tasks that
emerged from our data through the lens of this perspective in the following
sections.

##### Parents can use the robot to supplement their limited knowledge domains
for prompt generation.

All parents (P1–P10) mentioned that spontaneously generating prompts
associated with advanced or unfamiliar concepts might have been beyond their
capabilities. Despite their comprehension of the underlying academic concepts,
they recognized their limited knowledge of the pedagogical strategies required
to independently create age-appropriate prompts. For instance, P2 admitted
that she would find herself “reliant on the content provided in the book,”
while P4 mentioned that she could only generate “straightforward and basic
questions,” lacking the ability to formulate more advanced ideas.
Additionally, P10 perceived the collaboration to be based on hers and the
robot’s respective proficient domain knowledge, stating “I read the story and
provide literacy-related interaction, and the robot can assist by suggesting
math-related questions for them[children] to think about.” Moreover, parents
not only sought academic support but also envisioned prompting the child to
reflect on social interaction and emotions, nurturing their emotional
intelligence. For example, P3 and P5 believed that the robot could play a
pivotal role in generating prompts encouraging children to reflect on their
own and others’ feelings and emotions. These findings suggest that in cases
where parents lacked specific knowledge, the robot could assume the role of
generating advanced concepts and prompts to facilitate children’s learning.

##### Parents can use the robot’s responses as a source of validation and
social persuasion method.

The majority of parents (P1, P4, P5, P6, P7, P8) leaned towards leveraging the
robot’s responses as a source of validation. They saw the responses as a means
to offer their children contextualized feedback and encouragement. P8
exemplified this concept, stating, “one thing I liked is that after [child]
came up with the wrong answer, and misty[robot] told us the right answer. And
then we could work backwards and figure it out together.” Interestingly,
several parents (P4, P5, P7, P8) explained that they utilized the robot’s
response because their children trusted third-party and non-parental opinions
more than their input. P4 summarized this phenomenon by noting, “if they have
feedback from someone who’s other than a parent, they seem to take that
feedback better.” One possible rationale, articulated by P5, is that “it’s
good to see answers not just always coming from mom and dad; there are various
information sources validating that.  Another explanation, as described by P8,
is that she thought her child had “never seen Misty made a mistake, unlike her
parents, so she has more chance to trust Misty’s abilities.” However, in the
spectrum of parental preferences regarding the robot’s responses, extreme
cases exist, where parents either expressed a desperate need (P2) or no need
at all (P3) for receiving a response or an answer to a prompt from the robot.
P2 admitted feeling uncertain about offering a proper response for her child,
while P3 demonstrated stronger leadership in guiding her child in line with
her parenting values, stating that “It’s not really about getting the right
answer. It’s more about the interaction between us and having open
conversations about the questions.” These findings indicate that the robot
could act as a source of validation for facilitating learning interactions, be
leveraged as a trusted third-party opinion to effectively persuade and guide
children, and support parents who lacked confidence in their own responses.

##### Parents envision the robot tailoring the level of scaffolding to their
capabilities.

In the current system design, many parents (P1, P4, P5, P6, P8, P10) expressed
that they needed to provide substantial scaffolding during the interaction
with the robot. This scaffolding encompassed activities such as clarifying the
meaning of questions (P4, P6, P8) and explaining the problem-solving process
(P1, P4, P5, P6, P8, P10). For instance, P6 stated that “If I wasn’t there
with her[child], she[child] would not understand the question, although
She[child] understood the math part of it.; P1 noted, “Robot didn’t explain,
but for young kids, they wouldn’t necessarily know unless someone is
explaining to them.” However, one parent (P2) encountered challenges in
providing scaffolding for her child. She expressed uncertainty, stating, “I am
unsure what to do, after observing that her child answered incorrectly. Unlike
most other parents, she pressed the robot’s bumper button to obtain the
standard responses without providing her child any feedback. These findings
highlight the need for the robot to tailor its level of scaffolding to the
parents’ capabilities to optimize the learning experience.

##### Robot could enhance child’s engagement with physical and interactive
features

Parents excelled at customizing their approaches based on their understanding
of their child’s interests (P1, P7, P10). For instance, P7 knew how to“use
something he[child] is interested in get their attention.” Simultaneously,
parents recognized the significance of the robot’s physical appearance (P3,
P4, P5, P8, P9) and interaction features such as buttons (P3, P6, P8, P9, P10)
in maintaining their child’s engagement and motivation. As P5 described, for
someone “who doesn’t like reading, it would be an incentive to have
Misty[robot]’s presence.” Moreover, when asked to compare delivering prompts
with the robot through speech versus via text, P3 mentioned that she would “be
more interested in robot than paper because it would engage her child better.”
P8 also emphasized the robot’s interactive feature, saying, “she really liked
to push the button so that kind of kept her engaged.” In summary, these
findings indicate that while the robot may not excel at offering customized
motivational approaches as effectively as parents, its physical and
interactive features can assist parents in enhancing their child’s engagement.

####  4.1.3. Parent Availability

The parent’s availability dimension helps identify times when the parent has
limited availability to support the child. Although we did not discuss the
robot’s availability respectively as it did not emerge from the discussion, it
could contextually mean the battery level of the robot or the ease of access
to the robot.

#####  Parents can use the robot as educational support when they are
temporarily unavailable.

Parents (P1, P4, P5, P6, P8, P9) recognized that there are occasions when they
cannot be available to assist their child. These scenarios may occur due to
various commitments such as household chores (P6), work (P4, P6), caring for
other siblings (P5), or even during the recovery of an unexpected incident
(P1). P5 described this situation by saying, “I have two other kids, so
sometimes I’m occupied with helping them, and I can’t be there.” Similarly, P6
suggested that “the robot could help her[child] for five minutes while I’m
cooking dinner.” Furthermore, P1 shared a personal experience, stating, “Last
year, I was in a severe accident and bedridden for three to four months.
During that challenging period, I might have taken the assistance of a robot
to entertain my kids when I was physically unable to do so.” Additionally, P9
highlighted that she preferred the robot to support her child doing learning
activities instead of being occupied by entertainments, stating, “When we are
busy, we would feel better about kids engaging in educational activities with
the robot than watching TV.” These parents’ perspectives emphasize the
potential role of the robot in offering educational support when parents are
temporarily unavailable or occupied with other responsibilities.

####  4.1.4. Parent Motivation

The parent’s motivation dimension helps identify times when parents lack
motivation in certain learning activities or tasks. We did not discuss the
robot’s motivation respectively as it did not emerge from the discussion and
we also assumed this dimension is not applicable to the robot.

##### Parents envisioned the robot taking on repetitive and scripted work that
they have less motivation for.

Several parents (P1, P2, P3, P4, P6, P9) expressed a preference for the robot
to handle certain tasks, particularly those that are repetitive, structured,
and scripted. For example, some parents (P1, P2, P3, P6) showed the desire to
have the robot read the content of the books, allowing them to focus on
providing personalized and instructional support. P6 illustrated this by
stating, “Misty[robot] can read the book to her[child], but I don’t think that
she[child] would understand additional instructions. I need to explain to
her.” Furthermore, P3 wanted the robot repeat stories to her child, so he can
“hear it again in a different way from somebody else.” P4 extended this idea
to educational games, as her child enjoys playing them repeatedly. She
envisioned having “Misty[robot] play the game with her[child]”, reducing her
involvement in repetitive tasks as she “‘sometimes don’t like to play it as
much.” This illustrates parents’ intention to delegate lower-level and
repetitive responsibilities to the robot, freeing them to engage in more
interactive and personalized interactions with their child.

Figure 7. Summary of Theme 2: Parent-guided factors on collaboration. We found
that parents expected the robot to respond to their overarching educational
objectives (intellectual development, social and emotion development, and
promotion of independence) and concerns for the use of a robot at home
(privacy, content appropriateness, and balance between human-human and human-
robot interactions.)

###  4.2. Parent-Guided Factors on Collaboration

Parents formulated their individual policies for using the social robot, while
upholding their core educational objectives when engaging in learning
activities with their child. Even when guided by prompts from the social
robot, they instinctively tailored their parental guidance to fill in any gaps
and ensure the attainment of these objectives. This not only reveals the
parents’ aspirations in supporting their children but also highlights
potential design implications for the social robot. We categorized these
learning objectives into three domains: intellectual development, social and
emotion development, and promotion of independence. A summary of these
findings can be found in Figure 7.

####  4.2.1. Educational Objectives

##### Intellectual development

For the majority of parents (P1, P2, P4, P5, P6, P8, P10) their primary goal
was to nurture their children’s comprehension of the problem-solving process,
prioritizing it over the mere attainment of correct answers. As articulated by
P1, her goal was “just to make sure he [child] knew how the right answer was
achieved,” and P10 added that she was “not super concerned with him getting
the answer right.” However, P2 noted that she was “unsure or didn’t know what
to do” when her child encountered difficulties in understanding how to tackle
a problem, highlighting the challenges she faced in providing effective
guidance to achieve her desired educational outcomes. Additionally, beyond the
realm of problem-solving, P2 mentioned her efforts to teach her child Spanish
and English to facilitate bilingual communication, allowing her child to
interact effectively with both family members at home and peers at school: “ I
tried to speak more Spanish at home, because they speak English in school.
Their grandma doesn’t speak English, so we have to speak Spanish with her.”

##### Social and emotion development

Some parents expressed that one of the objectives in doing learning activities
at home is to spend time together to strengthen their parent-child bond (P3,
P9, P10) and promote the child’s comprehension of social interactions with
others, including the ability to recognize and empathize with others’ emotions
(P3, P5, P7, P9). For example, P3 mentioned that “it was the interaction
between us that we were going for, rather than whether you are right [for the
answer].” In addition, P9 described her aim saying: “we’re more focused on
social and emotional learning, and get him [child] to talk about feelings.”

##### Promotion of independence

Parents believed that a substantial aspect of learning involves cultivating
the independence of acquiring knowledge (P3, P1, P5, P7) and making critical
judgements on information from various sources (P4). As articulated by P1, her
aim was to “try to get him to figure things out on his own.” Similarly, P4
shared that she was “trying to teach them good values around technology, and
to use critical thinking skills to find good sources [of information] by
themselves.” In addition, this perspective was cited as a rationale for
favoring occasional one-on-one child-robot interactions, a choice contingent
upon the specific learning goals at any given moment (P5). As specified by P5,
she thought “there’s some value in interacting and stepping back to let them
process things for themselves and try to understand stuff, which also depends
on what the goals are in that moment.”

####  4.2.2. Concerns and mitigation approach

Parents expressed concerns about using the social robot at home and discussed
potential solutions. These findings shed light on the factors that limit the
robot’s usage and offer insights into how design improvements could help
alleviate parental concerns. Three primary concerns emerged from our analysis:
privacy, content appropriateness, and achieving a balanced use of the robot.

##### Privacy risk and mitigation

While some parents voiced apprehensions regarding privacy, with a particular
focus on data security (P1, P4, P6, P8), and their child’s understanding of
data privacy (P4), others (P9, P10) explicitly stated that they had no such
concerns. Specifically, P8 harbored concerns about the robot’s installed
cameras potentially capturing in-home video data, cautioning that “Remote
cameras can be used in malicious ways. I would need to be mindful of what room
the robots in.” Meanwhile, P4 voiced uncertainty about the content and timing
of data recording, raising questions such as “Is it always quietly listening,
waiting for a voice activation feature? Can I actively turn it on and off?Is
it recording my house all the time?” Additionally, both P4 and P6 shared
concerns about their child inadvertently disclosing personal information to
the robot without a comprehensive understanding of how data would be handled,
including unexpected sharing and retention. P4 emphasized this by remarking
“even just reminding my kids that what goes on the internet really is
forever.”

Parents offered several suggestions to mitigate their privacy concerns,
emphasizing transparency (P4), clear explanation (P4), and reduced reliance on
internet connectivity (P8). P4 proposed that data recording, storage and usage
should be transparently and comprehensibly explained through the system
interface in a user-friendly manner. She said, “I would want transparency
about what the robot is doing. If it is collecting data. I want to know what
it’s collecting.” Meanwhile, P8 expressed a preference for the robot’s
operation not to be contingent on an internet connection to safeguard data
privacy, stating “If Misty could download the stories and the question so it
will not be Wi-Fi dependent, I would feel more comfortable with it.”

##### Content Appropriateness and parental control

Several parents raised concerns about the age-appropriateness (P2, P4, P5, P6,
P7, P10) and trustworthiness (P6) of the content delivered by the robot. They
shared the desire to prevent the robot from presenting questions or materials
they considered inappropriate. Parents held varying perspectives on how best
to control and filter the content. The majority strongly preferred to
personally evaluate and determine content suitability (P4, P5, P6, P10). As
remarked by P4, “I would have the ability to say–Yes, this is appropriate; No,
this is not.” Additionally, P6 added that she wants “very explicit parental
controls over what the kids can access and what’s being said and heard and
talked about.” Aside from simply filtering the content, P10 emphasized her
desire to be “involved in what the child is doing, instead of giving him the
robot and leaving.” This hands-on approach allowed her to actively supervise
her child’s usage in line with her parental expectations. It’s worth noting
that these observations stem from a short-term study and may evolve as parents
establish increased trust in the robot over time. Further elaboration on this
point is provided in Section 5.3.

##### Balanced robot use with human-human interaction

Several parents (P1, P5, P6, P7, P9) expressed concerns about their child
potentially overusing the robot and consequently neglecting other important
aspects of learning and development, such as outdoor activities and social
interaction. P1 worried that her child might become excessively absorbed in
electronic devices and “just want to sit in front of it all day versus being
social or playing outside.” Similarly, P6 expressed concerns about children
becoming isolated, primarily engaging with technology instead of learning how
to interact with others: “I feel like kids are not learning how to interact
with each other and just want to play by themselves, with technology
involved.” Despite these concerns, parents recognized the value of the robot
in enriching their children’s learning. P7 voiced a preference for technology
to improve their lives positively rather than exerting excessive control:
“It’s more like I want technology to enhance their lives instead of letting it
control them.” This sentiment highlights the parental aspiration for a
balanced integration of technology into their child’s development.

Figure 8. Summary of Theme 3: robot-guided factors. We found that parents
envisioned the robot enabling effective interaction paradigms by handling
various interaction dynamics (child-robot interaction, parent-child-robot
interaction, parent-child interaction) and mixed-initiative forms of
interaction (user-driven, user-authored and robot-driven, robot-driven with
user permission, and robot-driven).

###  4.3. Robot-Guided Factors on Collaboration

The concept of interaction paradigm defines a specific module of interaction
with the robot, governed by the interaction dynamic and the preferred
initiation of communication. In our data, we found that parents preferred the
robot to flexibly supplement their level of involvement and they favored
taking more control over when the robot initiates an interaction. A summary of
these findings can be found in Figure 8.

##### Parents wanted the robot to adapt to flexible choices of interaction
dynamics.

The concept of interaction dynamics refers to how the robot manages the number
and kinds of participants as well as the involvement of each party. Instead of
adhering to a fixed mode, most parents (P4, P5, P6, P8, P9, P10) wanted the
robot to flexibly support a range of interaction dynamics based on situational
needs. For instance, P5 illustrated that she would like to “have choice to
switch between having parents in the interaction or letting the child be on
his own.” Parents conveyed a variety of dynamics they preferred in different
scenarios with the robot, including child-robot interaction without a parent,
parent-child-robot interaction, and parent-child interaction without the
robot.

Firstly, in the dynamic of Child-robot interaction (without a parent), parents
leaned towards allowing their child to interact with the robot independently
when they are temporarily unavailable (P4, P5, P6, P8, P9), when they lack
motivation (P1, P9), as the child matures to an older age (P2, P5), or when
the objective is to nurture their child’s independence for self-directed
learning (P5, P8). For instance, P5 described that when she was “trying to do
three or four different things at the same time, [child] will be on his own.”
Moreover, P9 indicated that she prefer to do more outdoor activities “rather
than learning activities at home with her child,” and thus would favor a
child-robot interaction model for in-home learning activities. In addition, P2
and P5 both imagined their children gradually interacting with the robot on
their own as they get older. Lastly, P8 illustrated her desire to promote
independent learning for her child, stating that the robot “provide the
response that gives her the tools to be a little bit independent and
practicing the skills without me” (P8).

Secondly, in the dynamic of Parent-child-robot interaction, all parents
(P1—P10) expressed their preference for a flexible division of roles in
guiding interactions with the child, allowing either the robot or themselves
to take the lead while the other played a supplementary role. This adaptable
approach results in varying levels of involvement for each party and thereby
variations within this specific dynamic. Firstly, parents leaned towards
increased robot involvement when they lack the availability (P5, P6),
motivation (P4), or capability (P2, P8) to actively guide the activities, when
they aim to cultivate their child’s independence in learning (P5). For
example, P8 described the lack of experience her husband has in facilitating
children’s learning, stating that “my husband does not know how to facilitate
and he wouldn’t enjoy getting emails about it. The robot could guide his
interaction and maybe have it model him[husband] more.” Conversely, parents
themselves wanted more active involvement when they seek quality bonding time
with their child during learning (P4, P6), when they intend to contribute
specific intellectual concepts (P4, P10), when personalized support is deemed
necessary, or when they want to maintain supervisory control over their
child’s interaction with the robot to address any concerns (P7). P10
illustrated this when she stated “I think it is always beneficial from having
some parental interaction and interest in what he’s doing. So I want to be
there to encourage him and let him validate that he’s doing the job.”
Combining these two views, parents prefer to have choice in both. P5 explained
that she “would like to have choice” in the dynamics, and be able to “switch
between involving parents to interact more and less.” In particular, she
stated that “I can provide guidance or assistance, but only being pulled in
when needed, letting him do what he needs to do by himself.”

Finally, in the dynamic of Parent-child interaction (without the robot), a few
parents preferred to interact with their child without the robot when they
want to enjoy their own time together (P9), when the concepts they want to
explain are not enabled by the robot (P8), or when they have concerns over the
information the robot offers (P6). For instance, P9 illustrated the situation
when she said “I feel it is valuable to have one on one time with the child,
doing reading or an activity together.”

##### Parents envisioned the robot accommodating flexible forms of interaction
initiatives.

The notion of interaction initiative refers to how much autonomy the robot is
granted to initiate interactions with users. Our data suggested different
patterns of initiating an interaction including user-driven, user-authored and
robot-driven, robot-driven with user permission, and robot-driven. In the
following sections, we articulate each kind of initiative and the the possible
scenarios correspondingly.

First, in a user-driven case, parents (P2, P3, P7, P8, P10) preferred the
robot to initiate an interaction only when they manually request one. P3
described that she preferred the robot “to be manual, rather than talks on its
own after a certain period of time.” Second, in a user-authored and robot-
driven case, where parents prefer to author the timing and initiatives of the
robot. P4 described that she “wouldn’t want the robot to just be able to barge
in to say whatever it wants or ask for permission randomly.” She further
illustrated her intention to author the robot’s initiatives by scheduling a
specific prompt or behavior for a specific time frame, stating, “it[robot] can
initiate to say, ‘it is time for this thing, do you want to do it now,’ and I
can confirm if I want to do it now or later.” Third, in the case of robot-
driven with user permission, parents (P2, P7) described certain scenarios when
the robot can drive the initiative by indicating its intention but need to
wait for user permission before actually putting the initiative into actions.
P7 described that she preferred the robot to “raise her hand while we read
together before bedtime” to keep the interaction “at a lower intensity level.”
Finally, in a robot-driven case, parents (P6, P7) described that sometimes the
robot can initiate an interaction or conversation anytime. P7 exemplified this
case by describing a scenario when her child is playing toys: “There could be
a time when she can just interact and interrupts. I can imagine her roaming
around and say ‘let’s build a racetrack’ or something similar” to help come up
with creative learning opportunities.”

##  5\. Discussion

Given the importance and ignorance of parental involvement in children’s
adoption of learning companion robots, we aimed to answer the following
research questions: (1) How do parents perceive and envision their use of a
learning companion robot to facilitate a child’s learning experience? (below
and Section 5.1); (2) What are the considerations for designing a learning
companion robot for parents to engage their child’s learning? (Sections 5.2
and 5.3)

Through a technology probe study with contextual interviews, we found that
most parents wanted the robot to act as their collaborator in facilitating
learning activities for young children. We therefore introduce this notion of
parent-robot collaboration to capture the collaborative dynamics between the
parent and the robot. Furthermore, we identified three dimensions that
determine various possibilities of parent-robot collaboration scenarios and
will later refer to them jointly as CAM factors, which represents parent/robot
relative capability (C), parent availability (A), and parent motivation (M).
We believe that CAM helps inform a desired parent-robot collaboration dynamic,
guiding decision regarding the robot’s responsibilities based on practical
situations. Additionally, educational objectives and concerns are the two main
parental factors that define and constrain parent-robot collaboration
respectively. These two factors are connected to some of the strategies,
especially Restrictive Mediation, described in the parental mediation theory
from prior literature (Section 2.3). Our findings regarding parents’ concerns
about invasion of privacy and decreased human-human interaction for children
also interestingly align with concerns teachers raised for students using a
social robot in a classroom setting (van Ewijk et al., 2020). Finally, the
concepts of interaction dynamics and interaction initiatives articulate the
interaction with the robot in terms of the number of participants and the
timing of interaction, implying that the interaction design should support
flexible multiparty scenarios and mixed-initiatives (Horvitz, 1999). In the
following sections, we unpack the connections between these themes to
elaborate the notion of parent-robot collaboration and explain how they
contribute to the contextual understanding of interaction design for a
learning companion robot through the lens of parental mediation theory
(Valkenburg et al., 1999; Beneteau et al., 2020) and mixed-initiative
principles (Horvitz, 1999).

###  5.1. Research Implications

We see an opportunity to extend our understanding of parent-robot
collaboration and the factors that might affect the emergence and outcomes of
this collaboration by connecting findings across the three themes emerged in
our analysis, which we discuss below.

####  5.1.1. Robot’s responsibilities in parent-robot collaboration: Parent-
guided and CAM factors

To determine what the robot should or should not do when collaborating with a
parent to support a child in a learning context, the connection between the
parent-guided factors (Section 4.2) and the CAM factors (Section 4.1) plays an
important role. From an overarching perspective, parents’ educational
objectives and concerns determine what the robot should support and restrict.
This aligns with the idea of Restrictive Mediation from Parent Mediation
Theory, referring to how parents make rules to guide or prohibit the use of
technology for children (Valkenburg et al., 1999). Meanwhile, from a practical
standpoint, CAM factors further determine specific tasks for the robot based
on real-life scenarios. For instance, in our study, P1 wanted the robot to
help her child understand processes of problem solving (educational
objective). However, P6 was hesitated to engage the robot due to doubts about
the sources of the robot’s content (concerns), affecting her willingness to
trust the robot’s responses. The example also implies that the robot’s
overarching responsibility should include not only supporting the child’s
learning but also explaining sources of its content to mitigate parents’
concerns.

While parents could leverage trustworthy content sources to support their
child’s understanding of problem solving, the CAM factors may practically
limit parents from carrying out the tasks by themselves, creating
opportunities for the robot to support and thereby defining the practical
responsibilities the robot should take on. For instance, some parents may not
have sufficient pedagogical knowledge (C) to effectively support a child’s
problem-solving process, but they may have the time (A) and strong motivation
to do so (M). In such cases, the robot’s responsibility would be to compensate
for the parents’ lack of pedagogical knowledge as needed, rather than
completely taking over the task. Conversely, some other parents may have
sufficient pedagogical knowledge (C) and motivation (M) but may be too busy
(A) to support their children. In such cases, if the robot has better
capability than the parent, it could take over the job during parental
absence. However, if the robot does not have the same or better level of
capability as the parent, it is worth considering how the parent could
transfer their knowledge to the robot so that the robot could later support
the child independently. These examples demonstrate that we can use parent-
guided factors to establish the overarching goals the robot needs to help
achieve and identify the concerns the robot needs to help mitigate.
Additionally, we can utilize CAM factors to further consider parents’
participation and ultimately determine the tasks the robot should and should
not undertake in real-life scenarios.

####  5.1.2. Robot’s level of involvement in parent-robot collaboration:
Robot-guided and CAM factors

To collaborate with parents to support children in learning contexts, the
robot may (1) take over the responsibility to support the child in a child-
robot interaction scenario, (2) at times adjust its level of involvement
dynamically in a parent-child-robot interaction scenario, or (3) exclude
itself from the interaction to support a parent-child interaction scenario.
The interaction dynamic among the three agents (i.e., parent, child, robot)
shifts across different patterns from time to time based on parent-child
needs. We note that the idea of parent-robot collaboration should extend
beyond interactions involving a parent, a child, and a robot, taking a
holistic view and including scenarios where the robot helps carry out the
parenting practices parents would have done on their own but were unable to
carry out practically. The connection between the CAM factors and the
interaction dynamics (see Section 4.3) provides insights into various
interaction dynamics the robot might need to handle.

In the previous section, we described how CAM factors influence the delegation
of responsibility between a parent and the robot. Here, we want to emphasize
how the outcome of this delegation naturally shapes diverse interaction
dynamics the robot needs to manage. For instance, P7 suggested that when she
was occupied with other kids (A), she preferred the robot to engage solely
with her child, forming a robot with a single child dynamic; other times she
preferred involvement in a robot, parent, and child dynamic. In addition, the
three parties involved in a parent-child-robot triadic interaction have
varying levels of involvement based on CAM factors. For example, P3 wanted the
robot to provide prompts for her to lead the conversation due to her strong
teaching ability. In this case, the parent preferred more involvement than the
robot. In contrast, P7 appreciated the robot taking the lead, with her
providing supplementary support given her time constraints. In this case, the
parent preferred the robot to be more involved than herself. Finally, P13
explained that she thought it was valuable to sometimes have one-on-one time
with her child in a parent-child bonding opportunity without the robot
involved. These examples show how we can use CAM factors not only to explain
what the robot should do but also the interaction dynamics the robot should
handle from time to time.

####  5.1.3. Robot’s initiative in parent-robot collaboration: Parent-guided
and robot-guided factors

In the process of parent-robot collaboration, the robot may take automated
initiatives for the interaction or be directly controlled by the participants
(e.g., the parent or the child). The parents’ preferences for the forms of
initiatives could be described by the connection between parent-guided factors
(Section 4.2) and interaction initiatives (Section 4.3), as well as the type
of activities in which participants are involved. For example, P7 explained
that she preferred certain informal learning activities that required
creativity and idea generation to grant the robot a higher degree of autonomy
in taking initiatives, but only during a specific period of time. P3, on the
other hand, preferred direct control over the robot as she had her own vision
of how to guide the child in learning, using the robot as a supportive tool.
P4 took a unique perspective of authoring the robot’s initiatives and allowing
the robot to autonomously execute her decisions.

We reflect on this concept of interaction initiatives with the notion of
mixed-initiatives proposed by Horvitz (1999), which discussed mixed-initiative
principles that aim to combine the power of direct user operation and system
automation services to improve the user experience and foster productivity
during collaborative work. They argued that a mixed-initiative system should
gather information and make inferences about users’ intentions and needs to
properly handle uncertainty during the interaction. In addition, they
demonstrated the application of mixed-initiative principles to task-oriented
computer systems, which mainly support computer-based tasks to increase
productivity in general workspace (Horvitz, 1999). Our findings contribute to
an extension of this notion to relatively complicated home environments,
physically-present embodied agents, as well as the nature of a multi-party
interaction dynamic, which involves the different needs of each individual as
well as the parenting policies that govern and constrain the child’s use of
the robot.

###  5.2. Design Implications

Here, we present the design considerations for the notion of parent-robot
collaboration. We note that this idea extends beyond interactions actually
involving a parent, a child, and a robot. Instead, we adopt a holistic
perspective and include scenarios where the robot helps implement the
parenting practices that parents would have done by themselves but were unable
to carry out practically. In the upcoming sections, we articulate the meaning
of each design implication and outline potential design considerations for
practical implementation. Based on the discovery of three major themes and
their connections, we have identified the following design implications:

####  5.2.1. Design Implication #1:

To enable parent-robot collaboration, a learning companion robot should align
tasks with parental goals, address concerns, and supplement parental
limitations based on capability, availability, and motivation. This
implication addresses what tasks the robot should handle in collaboration with
parents. Most of these decisions rely on parents’ intentions, which, in turn,
depend on user information. Following the concept of mixed-initiatives, the
robot should either seek direct input from parents before or during the
interaction or make intelligent inferences by monitoring user activities.
Moreover, the robot should effectively communicate its capabilities during
collaboration. For example, it can inform the parent if it cannot detect the
child’s emotional state or provide specific personalized support. This
communication helps establish accurate user expectations regarding the robot’s
capabilities and limitations.

####  5.2.2. Design Implication #2:

To enable parent-robot collaboration, a learning companion robot should
predict and adapt interaction dynamics based on user preferences and ongoing
user input. This implication emphasizes the varying levels of engagement among
participants in the interaction and highlights the need for the robot system
to possess technical capabilities to accommodate diverse interaction dynamics
based on individual user requirements. Additionally, parents may alter their
participation by either rejoining the interaction later or temporarily leaving
to address urgent matters. Therefore, future design should consider the
robot’s adaptability to changing dynamics and the seamless integration of
ongoing user feedback.

####  5.2.3. Design Implication #3:

To enable parent-robot collaboration, a learning companion robot should
consider parenting policies and contextual needs when determining its
interaction initiatives. This implication addresses the timing and
circumstances under which the robot should take the initiative in an
interaction. The degree of autonomy granted to the robot should be determined
not only by established parenting policies but also by ongoing parental input.
This approach helps refine the robot’s understanding of user preferences and
contextual needs over time. Additionally, the robot should be equipped with
the technical capabilities necessary to support mixed-initiatives. For
instance, the robot may need to be capable of initiating a conversation
without interrupting an ongoing interaction. Conversely, there may be times
when the robot needs to switch to a more passive mode, allowing users to have
full control over its initiatives. In future design, it’s important to
consider methods for users to communicate their preferences for interaction
initiatives with the robot. Additionally, models should be developed that
enable the robot to adapt to different modes of initiatives smoothly.

####  5.2.4. Integrating Design Implications in Practice

To implement the design implications, we propose the following preliminary
interaction design workflow, which integrates our implications and includes
concrete examples of potential features. Parents should have the ability to
continuously adjust and add their preferences to the robot as they progress
through each of the following steps. It’s important to note that these are
early-stage concepts based on qualitative evidence. Future work is needed to
develop prototypes and evaluate these design concepts and technical
requirements.

  1. (1)

Set up background profile for both the parent and the child:  Participants
create profiles to share demographic information and personal preferences,
enabling the robot to customize interactions, such as recommending age-
appropriate content. During this step, clear explanations about data storage
and usage should be provided, while also respecting user privacy preferences
and offering the option to prevent sharing non-essential information to
address privacy concerns.

  2. (2)

Define learning objectives and concerns:  The robot can prompt parents to
define their learning objectives and concerns, collaboratively generating
feasible and acceptable tasks to support the child’s progress. The system may
auto-suggest ideas for task definition, validate user choices, offer guidance
and recommendations to accomplish each task, and highlight potential risks and
benefits.

  3. (3)

Define task delegation between parent/robot:  After tasks are defined, the
robot can propose task distribution between the parent and itself by
collecting data on CAM factors from users: parental capability (e.g.,
confidence in task execution), availability (e.g., when they can participate),
and motivation (e.g., time commitment). Parents should have the ability to
modify the task assignment, with the robot transparently acknowledging its
limitations to establish realistic user expectations.

  4. (4)

Suggest and confirm preference for interaction dynamics:  As CAM choices are
determined, the robot can display a visual report illustrating the potential
interaction dynamics between the parent and the robot (e.g., a pie chart
representing their respective contributions). This allows the parent to assess
their desired level of involvement.

  5. (5)

Suggest and confirm preference for interaction initiatives:  The robot can
propose various approaches to initiate the interaction during collaboration,
seeking confirmation from the parents to ensure alignment with their
expectations.

  6. (6)

Save desired interaction paradigms: Finally, we suggest that parents have the
option to save frequently used interaction paradigms, creating distinct
personas for the robot that can support collaboration across various
objectives.

###  5.3. Limitations & Future Work

A key limitation of our work is that our participant sample offers limited
representation in the following socio-demographic dimensions: (1) location:
all participants lived in the Midwestern United States; (2) gender: all
parents were females; (3) income: more than half of the families have middle
to high level of household income; and (4) education: more than half of the
mothers had a Master’s degree or above. First, the parents self-selected which
parent would participate, rather than the study team, and thus our entire
sample was composed of mothers. This is consistent with prior literature in
education (Schoppe-Sullivan et al., 2013; McBride and Mills, 1993). Despite
our efforts to recruit a representative sample, it was challenging to balance
multiple demographic factors (e.g., gender, race, income, maternal education,
etc.) all at the same time. Plus, fathers at times signed up for the study but
mothers ended up being the ones who participated. Similarly, due to common
challenges of recruiting lower-income families (Nicholson et al., 2011), we
have little low-income families in our sample. It is well-established that
children in families with higher income or education levels tend to have
better academic abilities (Sirin, 2005), which may influence the kinds of
support those families prefer the robot to offer. There may be specific needs
and support that fathers or lower-income families prefer but were not well
captured in our results because of lacking enough representatives in our
sample. We will expand our efforts to recruit a more representative population
in our future work.

We conducted brief home visits to gain insight into parents’ perspectives on
using a learning companion robot. However, this short-term exploration may
restrict our findings to the short-term effects and immediate parental
experiences with the robot. It may not encompass the evolution of interactions
over time, necessary updates, and design considerations for adapting to
changes within an evolving ecosystem. For example, parents have voiced
concerns regarding content appropriateness and the need for parental control.
These sentiments may evolve over an extended period as parents develop
stronger bonds with the robot and the robot demonstrates its reliability.
Future research should employ long-term deployment approaches to examine how
parents engage with the robot over an extended duration.

Finally, our work has presented qualitative findings concerning user
perceptions, preferences, and design implications. To practically implement
our design recommendations and better assess their feasibility, it is
essential to construct viable prototypes and analyze the statistical
relationships between the identified factors. Future research should
prioritize the development of prototypes based on the design implications,
evaluate the practicality of these design concepts, and quantitatively
investigate the relationships between the factors.

##  6\. Conclusion

In this paper, we conducted an in-home study with 10 parent-child pairs, each
with a child aged 3–5 years old, to explore their interaction with a learning
companion robot in an informal learning setting. We developed prototype
capabilities for a learning companion robot to deliver educational prompts and
responses to the parent-child pairs during reading sessions. Our study data
revealed that parents wanted to collaborate with the robot to support their
child’s learning, introducing the concept of parent-robot collaboration. We
further unpacked factors influencing shared responsibility between the parent
and the robot, guiding or limiting the use of the robot, and shaping
interaction dynamics and initiatives enabled by the robot. Our findings offer
an empirical understanding of the needs and challenges of parent-child
interaction in informal learning activities, along with design prospects for
incorporating a companion robot into such scenarios. We contribute valuable
guidance on how robots can be designed to foster parent-robot collaboration,
encompassing supplementing practical limitations of parents, augmenting
parenting policies, and adapting to flexibal interaction paradigms.

###### Acknowledgements.

This work was supported in part by the National Science Foundation awards
2202803 and 2312354. We thank Nathan White for his help with the technical
development of our robot system and Jungseok Roh for his help with data
collection during the field studies.

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