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Noname manuscript No.
(will be inserted by the editor)

Can a Social Robot Stimulate Science Curiosity inClassrooms?
Masahiro Shiomi · Takayuki Kanda · Iris Howley · Kotaro Hayashi ·Norihiro Hagita

Received: date / Accepted: date

Abstract This study investigates whether the presence of a social robot and interaction with it raises children’s interest in science. We placed Robovie, our social
robot, in an elementary school science class where children could freely interact with it during their breaks.
Robovie was tele-operated and its behaviors were designed to answer any questions related to science. It encouraged the children to ask about science by initiating
conversations about class topics. Our result shows thateven though Robovie did not influence the science curiosity of the entire class, there were individual increases
in the children who asked Robovie science questions.
Keywords Robots for children, Science class, Social
robot, Field study
Fig. 1 Social robot in an elementary school’s science class-room.

1 Introduction
Previous studies have reported that robots are beneficial for children’s learning. For instance, robot kits havebeen used for teaching mathematics, physics, and computer programming [1–3]. Interactive and social robots
have been useful for vocabulary and language learning[4–7]. The presence of a social robot made a learnercentered class more enjoyable and encouraged children
to participate again [8]. Future classrooms will oﬀer
many potential tasks for robots.
We wonder, however, what future robots will dowhen they are not being specifically used for their deMasahiro Shiomi, Takayuki Kanda, Kotaro Hayashi, Norihiro Hagita are with Intelligent Robotics and Communication Labs., ATR, 2-2-2 Hikaridai Keihanna-Sci. City, Kyoto,
Japan. Iris Howley is with Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. Tel.: +81-774-95-1432
Fax: +81-774-95-1401
E-mail: m-shiomi@atr.jp

signed learning activities? Will they be stored in a closet?
Will they be left out when their power is turned oﬀ?
How can we exploit their free time? Robots engage children’s curiosity by encouraging interaction [9]. Perhaps
such curiosity might be turned into science curiosity,
which is an important motivator for learning science[10]. In fact, in various field studies, we witnessed parents who thought their children would benefit from interacting with our robot. Can we design a social robot’s
behavior that fulfills these expectations and fuels children’s science curiosity? To answer these questions, we
conducted a field study in an elementary school scienceclassroom (Fig. 1).

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2

2 Related works
2.1 Science curiosity
Extensive research exists on curiosity, which is a crucial factor in human’s cognitive development and learning. Curiosity is associated with a preference for novel
and unfamiliar objects, complexity, and uncertainty.
Curiosity is the source of the desire to acquire knowledge and the motivation to explore unknown phenomena. It motivates children to learn in a self-regulatedmanner. In many domains, self-motivated students perform better than extrinsically-motivated ones [11, 12].
Children with high ability exhibit more curiosity thanother children, and their curiosity scores increase year
after year while other children’s curiosity decreased [13].
Even among above average students, children with autonomous inclinations were more curious and received
higher grades [11]. Research has shown that teachers
understand the importance of fostering curiosity in thecontext of science education [14].
One element of higher education is inquiry-basedlearning, which is related to curiosity, where learners develop in-depth scientific questions and find the answers
by themselves. Such learning techniques direct learners
to acquire experimental and analytical skills and foster
self-regulated learning strategies, thereby encouraging
curiosity [15]. Even though our study targets elementary school children, they are not too young for inquirybased learning [16]; then we believe that prompting children to ask questions will fuel their curiosity.

Masahiro Shiomi et al.

information [17, 18], and later studies used more interactive robots [19] and human-like robots [20]. Yet inthese studies, robots were used as a kind of exhibit withadvanced technologies, unlike our current study’s novel
aspect that stresses specific interaction designed to promote children’s science interest or curiosity.

2.3 Social/interactive robots for learning andmotivating

Recent researchers have designed several types of specific interactions to promote children’s learning. In thecontext of vocabulary and foreign-language learning,
robots engaged in various language-related activities,
such as games [6] and reading [4]. A robot was used
for “learning by teaching” activities so that children’s
intrinsic motivation to interact with it was successfully
leveraged to motivate them to teach the robot [7]. Such
activities typically fall within the domain of languagerelated education; this is reasonable because a positiveeﬀect is expected if children are interacting with language, and they are often motivated to interact withrobots. In a previous work more relevant to STEM education contexts, Pereira et al. used a cat-like robot tostimulate children to learn chess [21].
Beyond learning, a couple of long-term studies usedagents and robots as motivators. Bickmore et al.’s screen
agent motivated people to exercise [22] by engaging inhuman-like daily conversations and asking users to exercise more. Kidd et al. employed a robot to motivate
users to continue their weight-loss activities [23]. In
these studies, agents and robots engaged in dialogues
2.2 STEM education using robots
through which they quite explicitly motivated users.
Our current study does not resemble either of the
The needs of Science Technology Engineering and Mathabove type. Unlike previous work, our target is science(STEM) education are of large social interest. Researchers curiosity, which cannot be accomplished by repeating
have considered robots for it, including small robotsimple language-based dialogues. Our robot does notkits like Lego Mindstorms. With its visual programexplicitly request children to be curious about scienceming language, Lego Mindstorms was successfully usedbecause such curiosity is unlikely to be stimulated by ato help 5- to 8-year-old children build their own anirobot’s requests.
mated constructions [1]. With such learning, children’smath scores improved [3]. It also helped university students connect theory and real-world applications [2].
3 Robot as a Knowledgeable student
While these studies used robots as a learning material
to be constructed and programmed, our current study
3.1 Design consideration of robot’s behaviors
uses a social/interactive robot to intellectually stimulate children with its human-like interaction. Compared“Why is the sky blue?” Children are often curious about
with previous STEM education activities with robots,
such basic questions. Asking them is a good opportuours is an unexplored challenge in the robotic domain.
nity for children to satisfy their science curiosity. If they
Robots have been placed in scientific museums, exacquire answers in a reasonable time frame, their curiospositions, and STEM education contexts to raise chility will probably be more stimulated and they will ask
dren’s interest in science and technology. Early trials
further questions in a process that resembles inquiryused tool-like robots that people can touch to retrieve
based learning [15].

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Can a Social Robot Stimulate Science Curiosity in Classrooms?

We also believe that peer-like friendly robots would
encourage motivation to interact with them for children, as shown in past research works [5,9]. Thus, rather
than a teaching assistant, we portrayed our robot as atransfer student who has considerable science knowledge and integrated it into the school by imitating asituation where a new student joins a class. Such a robot
naturally behaves friendly to encourage approaches bychildren. Past HCI research reported the importance ofbuilding rapport, which is known as aﬀective computing[25]. Considering such interactions would be important
to avoid just short-sighted question-and-answer conversations with children, and increase social acceptance towards the robot in a real setting.
To observe what might be reasonable in near future situations, we designed a robot’s behaviors to always respond correctly and provide easily understandable answers, like parents and teachers. For instance,
when asked about centrifugal force, the robot described
it as “the force that draws a moving body away from thecenter of the movement. You’ve probably felt it in a car
when it turned a corner.” Keeping these considerations
in mind, we prepared a robot that provided simplified
summary answers by a Wizard-of-Oz technique.

3.2 Robot
We used Robovie, who is 120-cm tall with two arms
(4*2 DOF) and a head (3 DOF) (Fig. 1). It is equippedwith cameras, microphones, and a speaker. Its utterances were generated using speech synthesis software[26]. For this study, the robot is semi-autonomous; gestures and pre-determined speeches are autonomously
controlled, but an operator conducted speech recognition and chose them based on pre-determined rules ortyped in new text by an interface.

3.3 Environments and setup
Robovie was installed in an elementary school scienceroom (Fig. 2) that has eight desks in front of a blackboard, where children attend science classes. Four cameras were attached to Robovie for tele-operating it fromthe next room. Before the first lesson (about one monthago from it), we introduced Robovie to children andexplained that Robovie is semi-autonomous and it willconnect to network to find answers if needed.
At the first lesson when children met Robovie, we
conducted an introduction session, where Robovie introduced itself as a transfer student and explained thatit always stays in the science room and quietly learns
from behind the children. It expressed a desire to talk3
Blackboard

16 mFree space

Entrance
8m

Fig. 2 Layout of science room.

with the children about science during breaks, and it
also talked with the teachers about a motor mechanism
to demonstrate its science knowledge. To increase opportunities for the children to interact with Robovie,
the science room remained open before and after science lessons as well as during morning breaks (before
the first class) and lunch time. During science classes,
Robovie did not initiate conversation; if children talked
to it or stayed more than ten seconds in the front of
it, Robovie suggested, “let’s talk later, because we’re inclass now.” This feature was designed to avoid disturbing class activities.
During breaks, children were allowed to freely interact with Robovie in the free space. Children woke it up
during morning and lunch breaks by pushing its wakeup button to start interactions. This created a reason
for the children to visit the room.
Therefore, Robovie interacted with children only during breaks, unlike teachers. We note that this is not justan extension of the science classes to the breaks, because the interaction style of Robovie is quite diﬀerent
from common science classes and the teachers behaviors. For example, Robovie did not review their classes
during breaks; it talked with children about various science topics, which are not limited to topics about theirscience classes, through behaviors described below.

3.4 Details of implemented behaviors
An overview of the dialogue flow is summarized in Fig.
3. When children lingered around Robovie during breaks,
it turned to them and started conversations.
To establish peer-type relationships, Robovie exhibited such relational behaviors as greeting and selfdisclosure dialogs (Section 3.4.1). While Robovie be-

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4Masahiro Shiomi et al.
Start
Idle

Turn to childCall child’s nameIs there achild aroundthe robot?

Self-disclosure
Continuity behavior

No

Chat about
science topics

Yes
Is break timefinished?

No

Encouragechildren to
ask science
questionYes
Say good-by
End

No question or
No response

Question asked
Exhibiting answer-difficulty inconversational filler
Answer science question

No

They want tolistenYes

Fig. 3 Flow of Robovie’s dialogue.

haved like a friend, it tried to elicit science curiosity by
performing a series of behaviors for talking about science (Section 3.4.2), such as chat and question-answer
behaviors. The operator controlled Robovie’s locomotion, greeted children by name, and chose the behavior
to be performed.
3.4.1 Relational behaviors
Calling children by their names In our previous
studies, since we found that people appreciated theirname being called by robots [5,9], we continued thisstrategy and prepared behaviors to greet the children
by names, such as “Hello, Yamada-san (pseudo name).”Self-disclosure with science topics
We followed a self-disclosure strategy that plays animportant role in human-robot interaction [5,9] to encourage children to interact with Robovie. Chattingwith a robot increased their desire to use robots as wellas their perceived enjoyment toward them [27] Even
though previous research focused on senior citizens, we
believe that such interactions would make Robovie moreaccessible for children, too.
To convert the student curiosity in Robovie into curiosity for science, it discussed science during 68 selfdisclosure behaviors (utterances accompanied by gazes,
gestures, and arm motions) that we implemented tomention its personal favorites or its experiences. For
example, Robovie asked the children about their school
lunch and said, “I eat electricity and save it in mylithium-ion battery, like smartphones,” and looked at
its own stomach where its battery is.
Continuity behaviors
We implemented ten continuity behaviors with which
Robovie mentioned that it investigated questions moredeeply if a child asked at the last interaction or thanked
the child who woke it up at the morning and at lunch
breaks. For example, when a child asked a science question about the weight of a colt, the next day Robovie
said, “Yamada-kun, I learned more about a colt’s weightsince we talked last. Want to hear what I learned?”
3.4.2 Behaviors that encourage science questions
Chatting about science topics Robovie aimed to establish a contextual background to talk about scienceby pretending that it failed to hear part of the lesson
and asked children to explain what it missed. We expected that children could easily discuss the things they
had just learned.
Robovie also initiated chats where it explained otherrelevant science topics. This behavior was used whenchildren did not have a science question or became too
quiet. Robovie first asked whether the children wanted
to listen to a talk about science. For example, “By theway, I learned about a future battery that uses waterto give an electrical charge. Want to hear more?” If thechildren were interested, Robovie explained the topic.
We prepared 64 such behaviors.
Encouraging children to ask science questions
After such chats, Robovie directly asked the children
whether they had any questions: “Any questions about
today’s science lesson? I’d like to answer them.” Robovie
explained how it felt happy when it talked about science. “As you know, I really like to talk about science.
Please ask me some science questions.” After answering them, Robovie said, “Thank you for asking me science questions. I really enjoyed answering them.” Notethat we did not review about the science class, e.g., doquizzes becuase it would be perceived as an extensionof their classes by children. We implemented 33 such
behaviors.
Answering science questions When a question
was asked, the operator judged its relevance to scienceor Robovie. To answer it, the operator employed two
strategies. When typical questions were asked, he selected from pre-implemented behaviors; otherwise, hedirectly typed utterances to implement new behaviors
after the session.
In both cases, we tried to provide easy-to-understandanswers. We initially prepared 60 behaviors that wererelated to the contents of the children’s science lessons
and exhibits in the science room. If the children asked

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Can a Social Robot Stimulate Science Curiosity in Classrooms?

about such exhibits, the robot used pointing gestures
for confirmation and explanation. After adding somebehaviors, the final number of answering behaviors was100.
Avoiding deviation from science questions On
the other hand, if children asked non-science questions,
Robovie politely refused and redirected their attention
to science. For example, if Robovie was requested toperform too simple math computations which did not
relate to science topics, he would respond, “Sorry, I
don’t know much about non-scientific topics. But I do
know science!”. These behaviors were kinds of social
behaviors to increase the total opportunity to interact with children, including conversations about science
topics.
Exhibiting answer-diﬃculty in conversational
fillers Because typing takes a few seconds, the operator used conversational fillers like “etto” or “well, let’ssee...” to buy time to type the responses [28]. With
such conversational fillers, we motivated children to ask
questions. We initially thought that the children would
ask very easy questions to test Robovie’s capabilities
and accept that it can respond to their utterances.
By providing feedback about the question’s diﬃculty,
we motivated the children to ask more diﬃcult questions. Along with the question’s diﬃculty, the operator
chose conversational fillers to provide feedback about
the diﬃculty. For example, Robovie said, “Wait a moment, that’s a very diﬃcult question... Let me search
my memory” and scratched its head. If the question
was easy, it only said, “Well, let me see...”5observers. The science curiosity of those who actively
ask science questions will be more influenced. Therefore, we made the following prediction:
Prediction 2: The more children ask science questions, the greater their science curiosity will increase.
4.2 Participants
Our study was conducted in a public elementary school
science room used by 4th-6th grade students. We targeted four classes of 114 5th graders.
4.3 Procedure
Robovie was installed in the science room for a month.
Science classes in this school are designed in a typicalteacher-centered learning paradigm and last 45 minutes, followed by a ten to twenty minute break. Eachclass had seven lectures during the month. Pendulums
and human birth were taught during the lectures. Eachclass had six morning breaks and six lunch breaks, whichlasted ten minutes. Robovie was introduced to children
at the first lecture.
We administrated questionnaires to the children.
Pre-tests were conducted before the first science class,and post-tests were conducted after the final scienceclass of the school term.
4.4 Measurements

4 Field study

4.4.1 Science curiosity: pre- and post-tests

4.1 Hypotheses and predictions

In both the pre- and post-tests, we measured science curiosity using a scale, which consists of five items, developed for 5th grade students [10]. The questionnaire used
1-to-5 point Likert scales, where 1 is the most negativeand 5 is the most positive. All five items were translated
from English to Japanese. It includes such questions
as “Science magazines and stories are interesting” and“I like touching diﬀerent things to learn about them.”
The Cronbach’s alpha at pre-test and post-test are 0.85
and 0.93, which were generally considered reliable. We
note that children knew the participation of Robovie
to their science classes before measuring the curiosity
at pre-test; this information might have influenced totheir science curiosity at pre-test.

In previous studies, robots increased children’s curiosity and motivated mutual interaction in classrooms [5,9]. Similarly, we expect that the children in the current
school will actively interact with Robovie. Its behaviors
were designed to talk about science and encourage children to ask science questions. Based on these designs,
we hypothesized that its presence would influence all
the children in the classroom and fan their science curiosity. We made the following prediction:
Prediction 1: Children’s science curiosity will be
increased by the robot’s presence in the classroom.
We also speculated that it matters greatly whether
a student asks Robovie a science question. Those who
do not ask questions will passively observe Robovie andthe interaction will have less influence on such passive

4.4.2 Perception of robot: post-tests
We investigated the children’s perceptions of Robovie
in the post-tests. The questionnaire used 1-to-5 point

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6

Masahiro Shiomi et al.

Likert scales where 1 is most negative and 5 is mostpositive. We kept the questionnaire short to match theirlimited attention spans by truncating many scales andused the questions weighted most highly by the factorial
analyses described below in the corresponding papers.
We prepared all other post-test items ourselves.
Likability: “I liked this robot” [29].
Enjoyment: “I enjoyed talking to the robot.”
Intention to use: we adapted three items including,“If I had the chance, I’d talk with the robot again”[13].(The Cronbach’s alpha was 0.94.)
Perceived friend-likeness: “I think the robot was myfriend.”Fig. 4 First week: children gathered around Robovie.
Perceived value of knowledge: “The robot’s knowledge was valuable.”
Perceived helpfulness: “The robot was helpful.”
Perceived understandability: “I understood the robot’s
explanations.”
Perceived science expertise: “The robot was a science expert.”
4.4.3 Number of questions to the robot
We counted how many times the children asked Robovie
science questions (e.g., why is the sky blue?) and Robovieconcentric questions (e.g., what is your favorite food?).
We analyzed the recorded audio and video, coded who
Fig. 5 Fourth week: Few children talk with Robovie, but
asked the questions, and whether they were science orothers return to their room or talk with friends.
Robovie content.

the robot’s novelty had worn oﬀ. Although almost allof the children continued to greet Robovie before and
5.1 General trends
after the lessons, only about half had follow-up conversations with it (Fig. 5). In a typical scene, when a
The children’s interaction with Robovie gradually changed break started, a group of active children ran to Robovie
during our month-long trial. As observed at anotherand interacted with it while relatively quiet children
field trial at elementary schools [5,9], in the first andwatched. After the active children left, these quieter
second weeks many children seemed interested and gathchildren took turns interacting with it. The frequency
ered around Robovie (Fig. 4). The robot(s greeting-byof their requests to hear their names was less than thename behavior attracted their attention. Typically theyfirst two weeks. Instead, children more frequently lisasked, “Do you know my name?”
tened to Robovie without interrupting it.
In contrast, during this time period, children seemedmuch less attracted to the behaviors related to science.
They sometimes asked questions, but their purpose was
5.2 Verification of prediction 1
more to test its capabilities; they often asked Robovie
whether it knew the topic that had just been taught.
Regarding prediction 1 (children’s science curiosity will
For instance, in a break just after the lesson about penbe increased), we ran an analysis to reveal whether
dulums, a child asked, “Do you know anything about
Robovie contributed to changes in science curiosity uspendulums?” In addition, they often ignored Robovie’s
ing linear mixed-eﬀect models with within-participant
request for science questions and interrupted it by askvariables of science curiosity scale (pre- and post-test
ing Robovie-related questions.
scores) as a fixed eﬀect. We used class as a random efAfter the third week, the situation started to change.
fect due to the diﬀerences of teachers and children beThe size of the crowd around it reduced, maybe because
tween them. However, there was no significant eﬀect in
5 Results

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Can a Social Robot Stimulate Science Curiosity in Classrooms?

science curiosity (F(1,109)=1.705, p=.194). Prediction
1 was not supported.

5.3 Verification of prediction 2
Regarding prediction 2 (children’s science curiosity will
increase if they ask Robovie questions) we ran an analysis to reveal whether interaction with Robovie contributed to changes in science curiosity using linear
mixed-eﬀects models with variables in the following equation as the fixed eﬀect and the children’s class as therandom eﬀect:
Model (curiosity at post-test - curiosity at pre-test)= intercept + curiosity at pre-test + number of science
questions + number of Robovie questions.
There were significant eﬀects in curiosity at pre-test( F(1, 104.945) =23.327, p <.001) and number of science questions ( F(1, 105.505) =4.075, p=.046), but nosignificant eﬀect in number of Robovie questions ( F(1,104.164))=0.140, p=.709) (Table 1). These results indicate that the curiosity at pre-test and number of science
questions have significant eﬀects towards the curiosity
at post-test. Children who asked science questions weremore likely to become more curious. It also indicates atrend where the curiosity of children who did not ask
science question become lower. Note that the detailsof relationship between the science curiosity at pre-test
and question asked will be discussed at the discussionsection. Overall, prediction 2 was supported.

5.4 Additional analysis
Since we focused on whether children asked science questions as an important factor in their increased science
curiosity, we analyzed whether their perception of therobot changed using linear mixed-eﬀects models with afactor whether children asked a science question as afixed eﬀect and the class as a random eﬀect (Fig. 6).
Our analysis revealed significant diﬀerences in perceived likability ( F(1, 111.415)=16.928, p <.001), enjoyment ( F(1, 110.754) =11.905, p =.001), intention
to use ( F(1, 111.615)=10.590, p=.002), friend-likeness( F(1, 110.703)=11.295, p =.001), value of knowledge( F(1, 111.232)=5.588, p=.020), and helpfulness ( F(1,109.430)=8.239, p=.005). A significant trend was foundin science expertise ( F(1, 111.928)=3.558, p=.062), andno significant diﬀerence was found in understandability( F(1, 109.218)=2.300, p=.110).
These student ratings about perceptions suggest thatthe perceived pleasantness (e.g., likability, enjoyment,

7
Table 1 Factor analysis results for increase of curiosity

Intercept
Curiosity at pre-test
Science questions
Robovie questions

Estimate
1.164-0.3480.083-0.019

p
<.001
<.001

.046
.709

and friend-likeness) of the robot had a greater relationship with whether a student asked questions. The
robot’s perceived capabilities (e.g., understandability
and science expertise) might have a relatively small relationship with whether children asked questions.
Moreover, as a supplemental analysis, we investigated whether questions about the robot change perception of the robot like science questions. For this purpose, we analyzed whether their perception of the robotchanged using linear mixed-eﬀects models with a factor whether children asked a non-science question (i.e.,
robovie question) as a fixed eﬀect and the class as arandom eﬀect (Fig. 7).
Our analysis revealed significant diﬀerences in perceived friend-likeness ( F(1, 109.900)=4.086, p =.046).
A significant trend was found in perceived enjoyment( F(1, 109.974)=3.247, p =.074) and intention to use (F(1, 110.448)=3.107, p =.081). There is no significant
diﬀerence was found in likability ( F(1, 110.380 )=1.962,
p =.164), value of knowledge ( F(1, 110.252)=1.059,
p =.306), helpfulness ( F(1, 108.867)=0.281, p =.597),
science expertise ( F(1, 110.878)=0.348, p =.556), andunderstandability ( F(1, 108.809)=0.561, p =.455).
These student ratings about perceptions would suggest that the perceived pleasantness of the robot had agreater relationship with whether a student asked questions about the robot; this result seemed similar to theanalysis of the students asked questions about the science. On the other hand, the robot’s perceived capabilities might have no relationship with whether children
asked questions about the robot.
These facts might indicate that the robot’s presence
had an influence on the increases of children who asked
questions. However, impressions about the robot were
measured at post-test only. Therefore it is diﬃcult tojudge whether they asked questions because they had
high perceived pleasantness, or their perceived pleasantness was raised because they asked questions, eventhough the impressions about the robot might have arelationship with whether children asked questions.

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8

Masahiro Shiomi et al.
Did not ask science ques!on

**

**

**

**

5

Asked science ques!on+ + : p < .10** **** : p < .01

4
3
2
1

Fig. 9 Children tried to look under Robovie.

Fig. 6 Perception of robot (science question eﬀects).

Did not ask Robovie ques!on

+
5

+

*

Asked Robovie ques!on+ : p < .10* : p < .05

4
3
2
1

Fig. 7 Perception of robot (Robovie question eﬀects).

Robovie and talked to it after it used his name in aninteraction; this child was only a passive observer until the robot greeted him by name. Children requested
to hear their names 46 times. Robovie used the namesof 85 children (74.6%) and said children’s names 257
times.
The robot’s self-disclosure behaviors encouraged children’s reciprocal self-disclosures toward it. For example, Robovie talked to a child about soccer because
its brothers (other types of Robovie) won the Robocup
championship. Then the child self-disclosed: “I like soccer, too.” Next he asked Robovie, “How many friends
do you have?” The robot explained about its friends
and family (same versions of Robovie). In return, thestudent described his family: “There are four people in
my family. My parents and my little brother, who’s asecond grader.”Self-disclosure behaviors with science topics raised
children’s interest in Robovie. For instance, Robovie
explained its own locomotion abilities (Fig. 9, left): “I
use wheels to move around instead of legs. Like a car!”
Then children squatted and tried to look into Robovie’s
lower part (Fig. 9, right).

Fig. 8 After class, a child ran to Robovie.

5.5 Observations
5.5.1 Eﬀect of relational behaviors
We observed many scenes where relational behaviors
seemed to motivate the children to interact with Robovie.
As in past research works [5,9], children liked it when
Robovie called them by their names. For instance, atthe first lesson a child ran to Robovie (Fig. 8) who
greeted him by name. The boy always asked, “Robovie,
do you remember me?” and interacted with it during
most breaks after the science lessons. Such greeting-byname behavior motivated another child who approached

Even though the total number of the uses of continuity behaviors was relatively small (15 times), thisbehavior was well accepted. For example, at a breakafter a lesson, children crowded around Robovie andshouted at it to say their names. A child touched its
shoulder and asked, “Do you know me?,” and anotherchild pointed to another student and asked, “Do you
know his name?” (Fig. 10, left). However, continuity behaviors changed such noisy situations. Robovie talked
to a child who had asked about Kepler’s second law
during a previous interaction: “Yamada-kun, I learned
some more about your last question.” All the children
stopped their requests that Robovie say their names.
They seemed surprised that it remembered previous
questions. “Should I explain it?” the robot asked, andthe questioner and the other children said “Yes” and“Please” and quietly listened to its explanation (Fig.
10, right).

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http://link.springer.com/article/10.1007/s12369-015-0303-1

Can a Social Robot Stimulate Science Curiosity in Classrooms?

Fig. 10 Children listening to Robovie after continuity behav-

ior.

Fig. 11 Interaction during lunch break.

5.5.2 Did the robot encourage science questions?
Robovie conducted behaviors which talk about topics
related to the children’s science lessons 48 times, andchildren asked eight science questions after those behaviors in total. For instance, at a break after a science lesson about human birth, the children listenedto Robovie talk about the abilities of unborn children.
One child asked relevant science questions:
Robovie: “An unborn child can hear the voice of its
mother from inside her body.”Child: “Why are unborn children only in their mothers?”
Robovie: “Well, because mothers have wombs.”
Child: “Do fathers have wombs?”
Robovie: “No, they don’t.”Overall, the children asked Robovie 57 science questions. 38 (66.7%) were relevant to their science lessons
and 19 (33.3%) were on general topics beyond thosetaught in class, typically of three types: nature (e.g.,what is the temperature of the sun’s surface?), animals/human beings (e.g., how much does a colt weigh?),and engineering (What is eco-friendly energy?). 19 children asked science questions (3.00 questions per child9

on average, S.D is 1.60.). The numbers of each category
through time is detailed in the table 2. As shown in thetable, in the latter half period (third and fourth weeks),
the number of science questions about their lessons weredecreased, contrary to the number of science questions
about general topics. This trend would indicate that, as
time passes, children ask more question about general
topics than questions which they already know answers.
During the field trial, Robovie exhibited the ”encourage children to ask science questions” behaviors 36
times, and children asked science questions 12 times after those behaviors (note that the rest of science questions (45/57) were asked by children at diﬀerent time).
For instance, at a lunch break, a child visited the science room alone and asked a science question after being encouraged by Robovie (Fig. 11). Five days before
he participated in a science lesson about human birththat did not include the abilities of unborn children:
Robovie: “If you have a science question, please letme know. I’l try to answer it.”Child: “Well, . . . can a fetus see anything in its
mommy’s womb?”
Robovie: “Hmm, a fetus has very poor eyesight, yet
it can perceive brightness.”
At another lunch break, another child visited alone
and asked science questions:
Robovie: “Do you have any science questions? I canprobably answer them.”Child: “I wonder. . . Why is the sky blue?”
Robovie: “Oh, that’s a good question. Well, the atmosphere scatters blue light from the sunlight. That
scattered blue light approaches the earth, and so thesky looks blue. That is also why the sea is blue. Water
scatters blue light, too. ”
Child: “I see. I understood the first time!”
After this exchange, he asked three other science
questions: the source of the influenza virus, why ants
and spiders have diﬀerent numbers of legs, and why theearth is round. We thought that ”Why is the sky blue?”
could be a chance to start more diﬃcult topics suchas what happens to light when it crosses a refractive
medium, but Robovie provided rather simple answer
based on our behavior design policy. It might be diﬃcult to judge whether Robovie’s answer was better forchild, but the child asked many kinds of questions after
this answer; these questions seemed to reflect genuine
science curiosity.
The avoiding deviation from science questions strategy worked well. Children sometimes asked questions

The final publication is available at
http://link.springer.com/article/10.1007/s12369-015-0303-1

10Masahiro Shiomi et al.

Table 2 The number of each category of science questions

The first half
The latter half

Related to lessons
22
16

General topics
8
11

that were not related to science and robots. For example, such math questions as “What’s one and one?” were
frequently asked. In such situations, Robovie politely
declined and steered the topic back to science questions.
As we expected, after such behavior from Robovie, children typically asked science questions. Another childalso asked a math question as ”What’s four and four?”to test capability of Robovie, but the child stopped suchquestion after a declining behavior by Robovie, andshe asked science questions later. Note that Robovie
declined only these too simple math questions duringexperiment, it would not restrain motivations of children to ask questions to Robovie. In addition, sometimes another child helped Robovie get science questions through this behavior. For instance, a child said
to the child who asked a math question that Robovie
wants science questions, and then another child said tothe surrounding children that “Robovie wants to answer science questions, do you have any?”
Moreover, exhibiting answer-diﬃculty in conversational fillers also worked well. Children who watchedthese behaviors seemed to understand of Robovie’s state,and some of children seemed to be interested in asking
more diﬃcult questions to Robovie. A part of children
said ”Maybe Robovie is connecting to Internet to findanswers” when Robovie is exhibiting answer-diﬃculties.

6 Discussion
6.1 Which comes first: questions or curiosity?
Our study revealed that science curiosity increased more
for the children who asked Robovie more science questions. This result begs a cause-and-eﬀect relationship.
In fact, it is diﬃcult to judge whether they asked Robovie
science questions because they had high science curiosity, or whether their science curiosity was raised because
they asked them.
We believe that both are generally true. A kind of
curiosity loop drives questions, which fuel more curiosity. The research question is how to cause such a positive spiral. On the other hand, based on our evidence
from this study, we conclude that children who asked
science questions had higher science curiosity (mean:
4.14, S.D.: 0.61) than children who did not (mean: 3.59,
S.D.: 0.91). We conducted analysis with linear mixedeﬀects models with a factor whether children asked ascience question as a fixed eﬀect and the class as a random eﬀect. The analysis revealed a significant diﬀerence(F(1, 107.986) = 6.657, p =.012), meaning that higher
science curiosity is a required (but not suﬃcient) condition to ask science questions. Simply having higher science curiosity did not result in the increase; our analysis
revealed that its influence had an opposite eﬀect (Table
1).
Interaction with Robovie was the fundamental causal
factor. For instance, some children asked many good science questions (see Section 5.5.2) that seemed driven
by genuine science curiosity, and such question-asking
was enabled by the interaction with Robovie. Knowingthat Robovie could answer science questions motivatedthem to ask more questions and increased their science
curiosity. We note that one limitation of this research
work is the low number of question asked, which make itdiﬃcult to discuss the above question and the eﬀects ofchildren’s science questions towards their science curiosity. A longer duration experiment would increase such
number then the eﬀects of a social robot and children’sscience questions towards curiosity would become clear.
However, it was diﬃcult due to limitation of installinga real robot to real environment.
Even though Robovie encouraged a positive spiralby motivating science learning or solving science questions for children who have high science curiosity, thequestion remains open how to motivate science learning for children with less science curiosity. This question would be diﬃcult for human teachers too. For thispurpose, human teachers would prepare a lot of ”new”
items to attract such children because it is unknown
what is interesting for them in advance. We think, Robovie
could become one of such ”new” items; but it is diﬃcult
to statistically prove it from current results. Moreover,
it is still open question how to reach out children who
did not have interest in the robot.

6.2 Could an alternative entity cause a similar eﬀect?
One remaining question is that whether other entities
such as screen agents could cause a similar eﬀect. In thisresearch we only tested the eﬀects of our social robot,
thus we cannot estimate the eﬀects of these entities. We
thought that other than robots, such entities as tutor
agents on a screen [21] would provide an easy-to-use
interface for children to ask questions.
On the other hand, past research works showed theadvantages of a use of physical robots by comparing
screen agents. For example, Powers et al. compared arobot and a computer agent displayed on a monitor
to investigate social interactions [29]. Shinozawa et al.

The final publication is available at
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Can a Social Robot Stimulate Science Curiosity in Classrooms?

compared the eﬀect of persuasions in a laboratory environment between a robot and a computer agent displayed on a monitor [30]. Bainbridge et al. also compared the persuasion eﬀects between a computer agent
and a robot for various tasks [31]. We also compared arobot and a computer agent displayed on a monitor toinvestigate persuasion eﬀects in a shopping mall [32].
These research works reported that real robots aﬀect
subject decision-making more eﬀectively or attract people than computer agents in real world environments.
Moreover, Iris et al. have investigated the ease of asking
question comparing between a robot and a human with
diﬀerent roles (helper and teacher) [33].
These research works would suggest that the physical existence and the social interaction of the robot
encouraged children to ask questions; however, one limitation of our current study is its inability to answer thisquestion from our experiment.

11

rent technology a human operator might help in situations where a technology fails. With this shared autonomy approach and small eﬀorts from human operators,
robots might operate the tasks in this study.

6.4 How did children perceive the robot’s role?

In this study, we decided the role of the robot as atransfer student, not a teacher (assistant). However, inthe questionnaire, we did not ask to children to investigate whether they perceived the robot as a peer or ateacher assistant. Therefore, we did not have statisticalresults about whether children perceived Robovie as ateacher assistant, or a kind of friend as we designed.
One limitation of our current study is its inability toanswer this question.
However, we think that the questionnaire results
about friendly-likeness would be one of evidences tosupport our statement. Because the average value of
6.3 Can Robovie be autonomous?
this item is more than three at one-to-five scale, if wedesigned the robot as a teacher assistant then this value
Next we discuss the possibility of making our robot auwould become lower value.
tonomous. Person identification is an essential function.
One of future works of this paper is to investigate
In our past works we successfully identified children usthe
learning eﬀects of diﬀerent roles of the robot by
ing RFID (Radio Frequency IDentifier) tags [5, 9]. A
considering
the conjunction between the human teacher
system with such identification techniques and humanandtheclimatesof classes. Appropriate roles of the
tracking [34] would enable a robot to accurately identify
robot
in
the
schoolwould be diﬀerent depending onchildren.
personalities
of
the
human
teachers and children, andSpeech recognition is another challenge. Current speech
the
style
of
lectures.
recognition systems, even those prepared for noisy environments, are only 21.3% successful in actual use withrobots [35]. Many sources of diﬃculties exist: unexpected noise, background music, and children’s speak7 Conclusion
ing patterns, which are diﬀerent from adults. On the
Our field study investigated whether a social robot canother hand, similar to iPhone’s Siri and Google speechstimulate children’s science curiosity in an elementary
recognition, the growth of data for training such sysschool. Robovie was installed in a science room for one
tems might greatly improve performance in the near fumonth, where children were allowed to freely interact
ture. Perhaps there is a chicken-and-egg problem here:with it during breaks. Although Robovie did not raise
the more technology we use, the more data we gather,
the entire class’s science curiosity, the children’s science
leading to greater improvements in performance. But tocuriosity scores increased more when they asked more
make technology usable in real settings, we need better
questions. We conclude that an eﬀect in Robovie stimperformance. Starting to use robots, even tele-operated
ulated the science curiosity of children who were very
ones, is an important step toward gathering data andmotivated to ask science questions. However, the sciincreasing autonomy.
Preparing easily understandable answers to children’s ence curiosity of children who were passively observing
was not positively influenced.
questions requires very sophisticated intelligence, which
is currently diﬃcult for an autonomous robot. Even in
the future, such answers will probably be prepared by
Acknowledgements We thank the staﬀ at the Higashi-Hikari
human professionals. However, once enough answers are
elementary school, its students, and their parents for theircooperation. We also thank Prof. Naomi Miyake (Univ. of
prepared, robots can be mostly autonomous because
Tokyo) for her helpful advice and Kanako Tomita who helped
they can select answers from pools vetted by experts.
conduct the experiment. This work was supported by JSPS
All of the above possibilities need further improveKAKENHI Grant Numbers 21118001, 21118003, 21110008,
ments of each technical component. Yet even with curand 25240042.

The final publication is available at
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Masahiro Shiomi received M.E. and Ph.D. degrees inengineering from Osaka University, Osaka, Japan, in
2004 and 2007. From 2004 to 2007, he was an Intern
Researcher at the Intelligent Robotics and Communication
Laboratories (IRC) and is currently a Researcher at IRC
at the Advanced Telecommunications Research InstituteInternational (ATR) in Kyoto, Japan. His research interests
include human-robot interaction, interactive humanoid
robots, networked robots, and field trials.
Takayuki Kanda received his B. Eng, M. Eng, and Ph.

D. degrees in computer science from Kyoto University,
Kyoto, Japan, in 1998, 2000, and 2003, respectively.
From 2000 to 2003, he was an Intern Researcher at ATRMedia Information Science Laboratories and is currently
a Senior Researcher at ATR Intelligent Robotics andCommunication Laboratories, Kyoto, Japan. His current
research interests include intelligent robotics, human-robot
interaction, and vision-based mobile robots. Dr. Kanda isa member of the ACM, the Robotics Society of Japan, theInformation Processing Society of Japan, and the JapaneseSociety for Artificial Intelligence.
Iris Howley is currently a PhD Candidate in Human-

Computer Interaction at Carnegie Mellon University, after
having received her B.S. in Computer Science from Drexel
University in 2008 and her M.S. in Human-Computer
Interaction from Carnegie Mellon in 2012. Her dissertation
focuses on the use of technology, including robots, toreduce students’ fear of being judged in social learning
contexts to improve student question-asking and learning.
Additional research interests include: computer-supported
collaborative learning, help-seeking, dialogue tutors, conversational agents, discourse analysis, and privacy in social
learning environments.
Kotaro Hayashi received an M. Eng and Ph. D. degrees

in engineering from the Nara Institute of Science andTechnology, Nara, Japan in 2007, 2012. Since 2009, hehas been a doctoral student at the Graduate Schoolof Information Science, Nara Institute of Science andTechnology, Nara, Japan and an Intern Researcher at
the Intelligent Robotics and Communication Laboratories(IRC), at the Advanced Telecommunications ResearchInstitute International (ATR) in Kyoto, Japan. His current
research interests human-robot interaction, interactive
humanoid robots, Human factors and field trials.
Norihiro Hagita received B.E., M.E., and Ph.D. degrees inelectrical engineering from Keio University in 1976, 1978,
and 1986. In 1978, he joined Nippon Telegraph and Telephone Public Corporation (now NTT), where he developed
handwritten character recognition. He was a visiting researcher in the Department of Psychology, University ofCalifornia, Berkeley from 1989-90. He is currently a Board
Director of ATR and an ATR Fellow, a director of the Social

13
Media Research Laboratory Group and Intelligent Robotics
and Communication laboratories. His research interests include networked robotics, human-robot interaction, patternrecognition, and data-mining technology. He is a member ofIEEE, the Information Processing Society of Japan, and theJapanese Society for Artificial Intelligence, The Instituteof Electronics, Information and Communication Engineers(IEICE) and The Robotics Society of Japan.