Affect and Inference in Bayesian Knowledge Tracing with a Robot Tutor

In this paper, we present work to construct a robotic tutoring system that can assess student knowledge in real time during an educational interaction. Like a good human teacher, the robot draws on multimodal data sources to infer whether students have mastered language skills. Specifically, the model extends the standard Bayesian Knowledge Tracing algorithm to incorporate an estimate of the student's affective state (whether he/she is confused, bored, engaged, smiling, etc.) in order to predict future educational performance. We propose research to answer two questions: First, does augmenting the model with affective information improve the computational quality of inference? Second, do humans display more prominent affective signals in an interaction with a robot, compared to a screen-based agent? By answering these questions, this work has the potential to provide both algorithmic and human-centered motivations for further development of robotic systems that tightly integrate affect understanding and complex models of inference with interactive, educational robots.National Science Foundation (U.S.) (Grant CCF-1138986)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant No. 1122374

AI Audit: A Card Game to Reflect on Everyday AI Systems

An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. There is little work in using game-based learning methods in AI literacy. Games are known to be compelling media to teach children about complex STEM concepts. In this work, we developed a competitive card game for middle and high school students called "AI Audit" where they play as AI start-up founders building novel AI-powered technology. Players can challenge other players with potential harms of their technology or defend their own businesses by features that mitigate these harms. The game mechanics reward systems that are ethically developed or that take steps to mitigate potential harms. In this paper, we present the game design, teacher resources for classroom deployment and early playtesting results. We discuss our reflections about using games as teaching tools for AI literacy in K-12 classrooms

Transparency, teleoperation, and children's understanding of social robots

Teleoperation or Wizard-of-Oz control of social robots is commonly used in human-robot interaction (HRI) research. This is especially true for child-robot interactions, where technologies like speech recognition (which can help create autonomous interactions for adults) work less well. We propose to study young children's understanding teleoperation, how they conceptualize social robots in a learning context, and how this affects their interactions. Children will be told about the teleoperator's presence either before or after an interaction with a social robot. We will assess children's behavior, learning, and emotions before, during, and after the interaction. Our goal is to learn whether children's knowledge about the teleoperator matters (e.g., for their trust and for learning outcomes), and if so, how and when it matters most (e.g. at what age)

Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions

We describe research towards creating a computational model for recognizing interpersonal trust in social interactions. We found that four negative gestural cues—leaning-backward, face-touching, hand-touching, and crossing-arms—are together predictive of lower levels of trust. Three positive gestural cues—leaning-forward, having arms-in-lap, and open-arms—are predictive of higher levels of trust. We train a probabilistic graphical model using natural social interaction data, a “Trust Hidden Markov Model” that incorporates the occurrence of these seven important gestures throughout the social interaction. This Trust HMM predicts with 69.44% accuracy whether an individual is willing to behave cooperatively or uncooperatively with their novel partner; in comparison, a gesture-ignorant model achieves 63.89% accuracy. We attempt to automate this recognition process by detecting those trust-related behaviors through 3D motion capture technology and gesture recognition algorithms. We aim to eventually create a hierarchical system—with low-level gesture recognition for high-level trust recognition—that is capable of predicting whether an individual finds another to be a trustworthy or untrustworthy partner through their nonverbal expressions

Expressive social exchange between humans and robots

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 253-264).Sociable humanoid robots are natural and intuitive for people to communicate with and to teach. We present recent advances in building an autonomous humanoid robot, Kismet, that can engage humans in expressive social interaction. We outline a set of design issues and a framework that we have found to be of particular importance for sociable robots. Having a human-in-the-loop places significant social constraints on how the robot aesthetically appears, how its sensors are configured, its quality of movement, and its behavior. Inspired by infant social development, psychology, ethology, and evolutionary perspectives, this work integrates theories and concepts from these diverse viewpoints to enable Kismet to enter into natural and intuitive social interaction with a human caregiver, reminiscent of parent-infant exchanges. Kismet perceives a variety of natural social cues from visual and auditory channels, and delivers social signals to people through gaze direction, facial expression, body posture, and vocalizations. We present the implementation of Kismet's social competencies and evaluate each with respect to: 1) the ability of naive subjects to read and interpret the robot's social cues, 2) the robot's ability to perceive and appropriately respond to naturally offered social cues, 3) the robot's ability to elicit interaction scenarios that afford rich learning potential, and 4) how this produces a rich, flexible, dynamic interaction that is physical, affective, and social. Numerous studies with naive human subjects are described that provide the data upon which we base our evaluations.by Cynthia L. Breazeal.Sc.D

Fostering parent–child dialog through automated discussion suggestions

The development of early literacy skills has been critically linked to a child’s later academic success. In particular, repeated studies have shown that reading aloud to children and providing opportunities for them to discuss the stories that they hear is of utmost importance to later academic success. CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills by supporting parents in shared reading with their children through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and, in combination with a common sense knowledge base, leverages this information to produce suggestions. Because of the unique challenges presented by our application, the suggestion generation method relies on a novel topic modeling method that is based on semantic graph topology. We conducted a user study in which we compared how delivering suggestions generated by our approach compares to expert-crafted suggestions. Our results show that our system can successfully improve engagement and parent–child reading practices in the absence of a literacy expert’s tutoring.National Science Foundation (U.S.) (Award Number 1117584