227 research outputs found
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
Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions
In this paper, we introduce a novel conceptual model for a robot's behavioral
adaptation in its long-term interaction with humans, integrating dynamic robot
role adaptation with principles of flow experience from psychology. This
conceptualization introduces a hierarchical interaction objective grounded in
the flow experience, serving as the overarching adaptation goal for the robot.
This objective intertwines both cognitive and affective sub-objectives and
incorporates individual and group-level human factors. The dynamic role
adaptation approach is a cornerstone of our model, highlighting the robot's
ability to fluidly adapt its support roles - from leader to follower - with the
aim of maintaining equilibrium between activity challenge and user skill,
thereby fostering the user's optimal flow experiences. Moreover, this work
delves into a comprehensive exploration of the limitations and potential
applications of our proposed conceptualization. Our model places a particular
emphasis on the multi-person HRI paradigm, a dimension of HRI that is both
under-explored and challenging. In doing so, we aspire to extend the
applicability and relevance of our conceptualization within the HRI field,
contributing to the future development of adaptive social robots capable of
sustaining long-term interactions with humans
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