=Paper=
{{Paper
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|storemode=property
|title=Adaptive Agents for Promoting Intercultural Skills
|pdfUrl=https://ceur-ws.org/Vol-587/paper5.pdf
|volume=Vol-587
}}
==Adaptive Agents for Promoting Intercultural Skills==
https://ceur-ws.org/Vol-587/paper5.pdf
Diana Pérez-Marín, Ismael Pascual-Nieto, Susan Bull (Eds): 1st APLEC Workshop Proceedings, 2010
Adaptive Agents for Promoting Intercultural Skills
W. Lewis Johnson and Alicia Sagae
Alelo Inc., 12910 Culver Bl., Suite J, Los Angeles, CA 90066 USA
ljohnson@alelo.com, asagae@alelo.com
Abstract. Pedagogic conversational agents can be effective in promoting the
acquisition of language and intercultural skills, both as virtual coaches and
virtual conversational partners. This paper gives an overview of a framework
for utilizing conversational agents to promote acquisition of intercultural
communication skills. Adaptation plays an important and increasing role, in
creating courses that are adapted to the needs of particular learners, as well as
pedagogic agents that adapt to the skills of the learner and the conversational
context. In our current work we are developing agents with explicit models of
culture, which may be used to create agents with adaptable levels of
intercultural sensitivity. This makes it possible to adapt practice scenarios to the
skills of the individual learner.
Keywords: Virtual coaches, virtual conversational partners, second language
learning, adaptation
1 Introduction
Animated pedagogical agents have shown significant potential for promoting learning
[5]. A number of recent studies have identified benefits from using them (e.g., [1]).
However other studies have produced mixed results [9], or have suggested that agent
features such as voice [6], language style [7], and adherence to politeness norms [8]
are more important than having an animated persona. For the domain of language
learning, however, animated agents offer obvious benefits, if they are designed and
utilized properly. Animated agents that can engage in face-to-face conversation can
give learners rich opportunities to develop and practice their language skills.
This paper gives an overview of a framework for utilizing animated pedagogical
agents to promote intercultural skills, implemented in a deployed suite of learning
products. The characteristics of the domain (second language learning) and the
teaching method (game-based learning) necessitate an approach centering on the use
of virtual conversational partners and virtual coaches, in contrast to the tutor-centric
approach which is common in intelligent tutoring systems. We then discuss the
general issue of adaptation in our courses, and the specific issues involved in tracking
the learner’s application of communication skills and adapting agent responses
accordingly. Finally, we discuss current work aimed at incorporating explicit cultural
models into conversational agents, which will make it possible to create agents with
varying degrees of intercultural sensitivity, affecting the difficulty of the scenario.
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2 Background: Intercultural Skill Learning Environments
Fig. 1. Operational Indonesian language and culture training system.
Figure 1 shows an example learning environment, Operational Indonesian. In this
course learners can learn the basic skills necessary to engage in overseas operations
such as humanitarian assistance. They practice their skills in interactive game
scenarios. In this scenario the learner’s character (center left) is engaged in a
conversation with the local military commander (center right) about providing aid.
The learner communicates with the non-player characters by speaking in Indonesian
into a microphone, and selecting accompanying nonverbal gestures as appropriate.
The goal is to get learners to the point where they can engage in conversation without
hints or assistance, but until they get to that point they can refer to a list of hints of
what to say, either in English (top left), or in Indonesian.
The courses cover the language and cultural knowledge and skills necessary to be
effective in the target missions and situations. Curricula employ a stepwise process of
knowledge acquisition and skill development. Lessons introduce the relevant phrases,
vocabulary, cultural knowledge, and linguistic knowledge, and then give learners
opportunities to practice applying this knowledge. Learners practice individual
conversational turns, and then progress to more extended conversations, as in Fig. 1.
Approximately 100,000 people around the world have used these courses to date to
learn about foreign languages and cultures [3]. We have developed a major language
learning Web site that has over 10,000 registered users around the world, and many
more guest users. Feedback from this user base has contributed to the development of
the ideas presented here.
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3 Conversational Partners, Coaches, and Scaffolds
Conversational agents in these courses fall into two main categories:
conversational partners and virtual coaches. Conversational partners respond to the
learner’s spoken utterance and nonverbal actions, in a manner that is appropriate for
the culture, the partner’s social role, and the social context of the conversation. For
example in Fig. 1 the conversational partners represent officers in the Indonesian
Army, and the learner should address them in a manner appropriate each officer’s
social standing. The manner in which the agents respond provides learners with cues
as to how well they are performing. For example, conversational partners may express
approval then learners speak in a courteous and culturally appropriate manner, or may
express offence when they commit a faux pas and say something inappropriate. This
helps to make feedback become an intrinsic part of the interaction of the practice
scenarios. We find that such intrinsic feedback is generally more salient and
memorable than extrinsic feedback such as critiques and commentary of the learner’s
performance. For example, if a learner says something that is culturally offensive and
inappropriate, learners will be more likely to remember and learn from their mistake
if they can see the conversational partner display offence at the learner’s actions.
For scenarios designed as final learning assessments, the feedback from the learner
comes only from the conversational partners. In such cases the learner should be able
to decide to say and do based only on what the non-player characters say, and if they
require help beyond that they will receive deductions in their performance score. For
practice scenarios however learners typically require more feedback than what the
conversational partners provide. The agent’s reaction to the learner may be subtle or
ambiguous, just like in real intercultural situations, where people often avoid showing
offence, out of politeness. Reactions to faux pas may be subtle and easily overlooked
by someone who is not familiar with the culture. And even when learners recognize
that they have made a mistake, they may not understand what exactly they did wrong
or understand why it is a mistake. We therefore often find it useful to scaffold practice
dialogs with hints and additional feedback and explanations.
Virtual coaches play an important role in providing this scaffolding. They help
present and explain the cultural and linguistic knowledge that they will require,
providing voiceover narrations of learning materials. They introduce conversational
exercises, preparing learners cognitively for the exercise (by reminding them of
communication skills that they will need to employ during the exercise) and preparing
them affectively as well (by encouraging attitudes and affective states conducive to
successful conversation). After the exercise is complete, the coach provides the
learner with feedback on how they performed, so they understand what they did
wrong and why. It may also give advice on which skills the learner ought to practice
to perform better in the future. However we deliberately avoid developing coaches
that engage in extended tutorial dialogs, so that the learners can focus attention on
culturally appropriate interactions with conversational partners.
Figure 2 illustrates how a conversational partner and a virtual coach are combined
in a single exercise. The learner is requested to ask his friend Matt (on the left)
whether he wants to stop for a burger. The learner has attempted to make the request,
but got it wrong, and so the coach has come in and explained what the learner should
have said.
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Fig. 2. Combined conversation and tutorial feedback.
One disadvantage of using a virtual coach or tutor is that the coach’s intervention
can disrupt the flow of the scenario and distract the learner from the conversation.
Therefore during ongoing scenarios we use subtler scaffolding cues instead. We
employ simple auditory signals (earcons), as graphical symbols (green plusses and red
minuses) to signal when the learner has done something particularly good or bad.
These alone are usually sufficient to make the learner aware of what they have done
and help them adjust their behavior. Then when the scenario is done the virtual coach
can come in and explain what exactly the learner did wrong and why.
4 Adaptation
It is useful to adapt the level of difficulty of practice scenarios according to the skill
level of the learner. This is currently accomplished by adjusting the amount of
additional scaffolding that is provided in the scenario. Depending upon the level of
difficulty selected the symbols and earcons that signal a change in the agent’s attitude
and reaction can be disabled, and subtitles and translations can be removed.
The most important type of adaptation is in making the behavior of the
conversational partners adapt in real time to the level of communicative skill of the
learners, in the course of the conversations between the learners and the agents. Each
agent has a level of rapport with the learner, which increases when the learner says
culturally appropriate things and decreases when the learner says culturally
inappropriate things. In more complex scenarios agents may include additional
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dynamic social variables, such as the agent’s level of trust of and fondness toward the
learner. The agent’s response to the learner is dependent in part upon the levels of
rapport and other social variables that have been established to that point. This is
particularly important when modeling relationship-oriented cultures, where it is
important to establish a personal relationship with one’s counterpart before getting
down to business.
Agent processing is organized in a pipeline. The agent first interprets the meaning
of the learner’s speech and gestural inputs as a communicative act, i.e., a
generalization of the concept of speech act. The agent then selects a communicative
act to perform in response. Finally, it generates a combination of speech and body
movements to realize the communicative act. In our currently deployed learning
environments, such as those illustrated in Figures 1 and 2, agent communicative act
selection is implemented using finite state machines, where state transitions may be
conditioned by predicates over the social variables. We have recently developed a
new architecture, called VRP (Virtual Role Player) [4], which incorporates explicit
representations of the physical and social environment, and rules governing agent
behavior.
We have also been experimenting with dynamic learner models that track the
learner’s ability to use words and phrases in conversation. The learner model tracks
and records each attempt on the part of the learner to say a particular phrase. We
intend to use this information to filter the curriculum, to focus on learning activities
that require learners to practice the phrases that they are having difficulty with.
5 Explicit Models of Culture and Cultural Sensitivity
In our current work we are extending our VRP agent architecture to increase the level
of flexibility and adaptability that is supported. This provides additional opportunities
for adapting agent behavior to adjust to the skill level of the learner. By making these
representations part of a shared state across multiple dialog instances, we can create
agents whose behavior adapts over a series of episodes to the learner’s
communicative competence, creating practice experiences that are both more realistic
and provide learners with an appropriate level of challenge.
A new project named CultureCom is developing formal models of the cultural
influences underlying dialog and utilizing them to increase the flexibility and realism
of the behavior of non-player characters in training simulations. The work is being
conducted in collaboration with Dr. Michael Agar of Ethnoworks and Prof. Jerry
Hobbs of the University of Southern California. Cultural and linguistic
anthropologists are developing validated sociocultural data sets for Afghanistan and
other cultures of interest, consisting of annotated dialogs of cross-cultural
interactions. Experts in artificial intelligence then use these data to develop logical
models of sociocultural behavior in different cultures, based upon a formal ontology
of microsocial concepts underlying interpersonal communication. This in turn is being
used to create an enhanced version of the VRP architecture in which agent intent
planning utilizes explicit validated models of sociocultural reasoning for different
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cultures, which can swapped in and out to enable agents to model a variety of
different cultural characteristics.
The following example illustrates how CultureCom cultural models will be
developed and used. American culture and Afghan culture differ in the way they
express promises and commitments. Afghans sometimes agree to a request as a way
of being socially agreeable, without making a firm commitment. In CultureCom we
explicitly model for communicative acts what sociocultural inferences can be made
from them, such as whether a statement of agreement constitutes a firm promise and
commitment. This in turn can be used to ensure that the non-player character’s actions
consistent with the culture throughout, and can also provide helpful feedback to the
learner. For example it can help learners to recognize when intercultural
misunderstandings can arise due to different views of what has been promised and
agreed to.
Acknowledgments. The author wishes to express his thanks to the members of the
Alelo team who contributed to this work. This work was sponsored by PM TRASYS,
Voice of America, the Office of Naval Research, and DARPA. Opinions expressed
here are those of the author and not of the sponsors or the US Government.
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