Hello, thank you for joining me today.
My name is Nicole Salomons, and I'm a sixth-year PhD student in the social robotics lab at
Yale University.
The title of the talk today is we make a great team.
Adults with low prior domain knowledge learn more peer robot than a tutor robot.
There have been many prior studies looking at robot tutoring.
However, in 86% of prior studies, the robot has taken on the role of either a teacher
or tutor.
Furthermore, most of the work in robotic tutoring has really been focused on tutoring children.
However, should we restart rethinking the roles that a robot takes when instead of interacting
with children, it interacts with adults?
We take inspiration from education studies and psychology, where we saw that participants
not only learn from their teacher, but also learn from colleagues.
And often they learn even more from their peers and they do from their teacher.
Therefore, the main question of our research study is when tutoring adults should a robot
take on the role of a peer or a tutor?
For the purpose of this experiment, we devised an electronic circuit building task.
Now, there are several reasons why we chose this task.
The first is that most adults do not have prior circuit building experience, allowing
them to learn several new skills during the interaction.
The second is that because there are many skills necessary to complete this circuit,
there are many opportunities for the robot to intervene and provide personalized help.
Another characteristic of this task is that a computer vision system is necessary to observe
what the user is doing on the circuit board.
And therefore, observations are often very noisy.
Therefore, we created our own user skill modeling algorithm, which we call CBKT, to Model User
skills during the interaction.
To test how the different types of robots, the peer robot and the tutor robot are perceived,
we created two conditions.
In the first condition, which we call the peer robot condition, the robot was introduced
by the experimenter, as being a colleague or collaborator.
And during the interaction, it used we and us pronouns.
In a tutor condition, the robot was introduced by the experimenter, as being a teacher.
And during interaction, it used you pronouns.
Other than this, we maintained everything and experiment constant to really study the
different perceptions people have, depending on the robot role.
And whether they learn more from one robot versus the other?
Here are some examples of robot utterances.
So for example, in the peer condition, it might say, awesome, looks like we did that
perfectly.
Whereas in the tutor condition, it will say, awesome, looks like you did that perfectly.
Here we have a video showing some of the utterances, the robot said during the interaction, and
also how the setup of a study was.
Here, let's try to add a resistor to the circuit.
Awesome, we make a good team.
Here try to add a resistor to the circuit awesome, you are a good student.
So participants were brought into the experimental room, and they interacted with a robot on
a large table.
In the middle of the table, they had a circuit board, and to their right and to their left,
they had different circuit pieces and wires that they could add to the board.
There was a tablet present on the table.
However, this tablet did not present any type of help.
Its only role was to say whatever the next task was, for example, build a circuit that
has a constant light on.
there was an overhead connect camera that looked down on the table and saw which pieces
the participants were adding and removing from the board and creating a personalized
user skill model.
A UR5e robot provided different Help actions every 30 seconds, depending on which skills
the users were lacking for the current task.
participants completed a total of 10 different circuit tasks with a robot, and each task
took around three minutes long.
And therefore the interaction with the robot was 30 minutes long.
There were 37 participants in the study.
20 Were the tutor condition, and 17 were in the peer condition.
To really test whether participants are learning more, with one condition rather than the other,
we created pretest and post tests, and these were nearly identical to each other.
But for one thing, we presented the post test flipped either vertically or horizontally
compared to the pretest.
This allowed us to test the exact same skills with the same difficulty without the participants
realizing that they were doing the same test over again.
Our first question that we asked was, did participants successfully learn new skills
with the robot, and in both the peer condition and the tutor condition, the robot was able
to successfully teach new skills to the participants.
Next, we wanted to see whether there were any significant learning differences between
the two conditions.
However, at an initial glance, there were no significant differences between the two
conditions.
However, upon looking into a little more depth into the data, we realized that participants
with low prior domain knowledge learn significantly more with the peer robot than the tutor robot.
Now, this was really exciting news, by only changing this very small aspect of how the
robot interacted with the participants, they were actually able to learn more.
We also asked participants questions after the experiment ended about the perceptions
of the different types of robots.
And compared to the tutor robot, the peer robot was viewed as significantly more warm,
smart and competent, friendly, respectful, and it's treating them more as an equal.
In general, the peer robot was viewed more favorably than the tutor robot.
We also looked into engagement, but we found no significant differences.
There are several questions that arise from this study.
The first being, why was the peer robot viewed more favorably than the tutor robot?
The first reason why we believe this might have happened is that the peer robot was perceived
as an in group member, whereas the tutor robot was perceived as an outgroup member.
And psychology research shows that in-group members are viewed more favorably than out-group
members.
Another reason why the peer robots might have been viewed more favorably is that participants
had lower expectations for it.
Whenever we're introduced to another entity, as them being a teacher, we create really
high expectations.
And when those expectations are not met, we might view them less favorably.
But the big question still remains why we're low prior knowledge participants learning
more with a peer robot than with a tutor robot.
And we leave this as a really exciting question that hopefully will be answered by future
research.
So to conclude, low prior knowledge participants learn significantly more with the peer robot
than the tutor robot and participants view the peer robot, a significantly friendlier,
smarter and more respectful than the tutor robot.
By seeing the peer robot you more favorably and participants learning more from it.
More research in the future should really reconsider what types of roles robots are
taking, and really show start thinking about having robots presented more as peers or collaborators
especially when interacting with adults.
Thank you for joining my talk today.
Please let me know if you have any questions and feel free to email me.
Thank you.
Bye