IEEE Robotics & Automation Magazine - June 2016 - 65
choice to build an interaction situation based on the learning
by teaching paradigm.
Remediation Efficacy
Vincent's case study did provide us with initial material to
evidence handwriting improvements (Figure 8), and the
study with Thomas provides further data, both quantitative
(Figure 9) and qualitative (feedback from the therapist that
points how Thomas is much better at drawing consistent
shapes in a repeated manner as well as reflecting on his
own performance by training several times before actually
sending a demonstration to the robot). We must, however,
remain cautious here regarding the actual role of the sys-
tem: although the children were the ones deciding what to
teach to the robot, and the robot was autonomously learn-
ing and responding, the role of the adults (the experi-
menters or the occupational therapists) should not be
underestimated. Beyond the normal explanations of how to
operate the tablet and how to interact with the robot, the
adults played the role of a facilitator in each of the studies
by prompting the children to comment on the robot's per-
formance, suggesting possible corrections, or proposing to
try another letter/number/word. This facilitation not only
compensates for the possible shortcomings of the interac-
tion but also is a fundamental part of the learning process
itself. In that respect, our robot is essentially a tool that cre-
ates a favorable learning situation for the child and where
the adult (be it a teacher or a therapist) keeps their entire
educative role.
Conclusion
We believe that this research provides a novel perspective on
educative robots at several levels. Namely, we show that:
● Robots in an educative context are certainly relevant and
effective beyond science, technology, engineering, and
mathematics teaching.
● We can successfully transpose the well-established learning
by teaching paradigm from education sciences to robotics,
even in a complex form: handwriting is a difficult physical
skill, and the robot learns and interacts autonomously, and
the child is responsible not only for the teaching, but also
for the teaching orchestration by managing the turn taking
and the progression of the activity.
● Blending machine-learning techniques with human-robot
interaction allows for building a believable agent that
induces social commitment.
● This social commitment induces cognitive engagement of
the child with the robot, which is a key learning lever as it
elicits reflective metacognitive mechanisms on the learn-
ing task.
● We have been able to sustain a long-term interaction (sev-
eral hours) involving a task that would typically be consid-
ered repetitive and challenging by the children.
Supported by several field experiments, our initial results are
promising as we ultimately evidence handwriting improve-
ments. It is, however, still early to quantify the lasting effects
of this remediation: handwriting is a complex cognitive skill
that builds on many individual and social factors. Self-confi-
dence is one of them. Our approach endows the child with
the role of a teacher who can help a robot: we expect it may
as well help some children to recover self-esteem and self-
confidence by putting them in a positive gratifying role. The
experiments that we have conducted so far do not allow us to
confirm this hypothesis yet, and more research will have to
be conducted in this direction.
Possible Ethical Concerns
There are two aspects of this research that should be dis-
cussed in terms of their possible ethical implications: the
perceived role of the robot vis-à-vis the real teachers, and
the implications of the
mentor-protégé relation-
ship for children, espe-
Social commitment
cially vulnerable ones.
The place and role of the
induces cognitive
robot vis-à-vis the teach-
er can be questioned: as
engagement of the child
we see it, the role of the
robot within the class-
with the robot, which is
room (or at the therapist's
clinic) does not infringe
a key learning lever.
upon the role of the adult
(teacher or therapist).
The core of the learning
by teaching paradigm relies on the child becoming the
teacher of an underperforming pupil (the robot): from that
perspective, the robot does not replace the teacher; on the
contrary, it plays a different role in the classroom, which
happens to be novel as well: the robot is the least performing
student and still a very patient, always eager to improve, one.
In our experience, the teacher/facilitator retains an instru-
mental role during the interaction, and the learning would
hardly occur if the child is left alone (or even semialone).
The initial feedback that we received from the teachers
during the focus groups confirms this perception: once
explained, our approach makes sense to them and is wel-
comed as a relevant pedagogical innovation.
The implication of the mentor-protégé relationship on
the children is less clearly understood. We have certainly
seen that the children can establish strong affective bonds
with the robot (as witnessed, for instance, by the letter sent
by Vincent several months after he interacted with the
robot), but we are not yet able to precisely characterize these
bonds. The ethical implications of the mentor-protégé rela-
tionship have been explored before in the context of human
teaching [8], [16], but they mostly looked at the question
from the perspective of the protégé, whereas in our case, the
child is the mentor. As such, relatively little is known about
the psychological implications for a child to commit to help-
ing a robot, and as advocated by Belpaeme and Morse [1],
we likely need to first gain more field experience before
being able to draw conclusions. Beyond handwriting, we do
june 2016
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IEEE ROBOTICS & AUTOMATION MAGAZINE
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