While most research in Human-Robot Interaction (HRI) studies one-off or short-term interactions in constrained laboratory settings, a growing body of research focuses on breaking through these boundaries and studying long-term interactions that arise through deployments of robots “in the wild”. Under these conditions, robots need to incrementally learn new concepts or abilities (i.e., “lifelong learning”) to adapt their behaviors within new situations and personalize their interactions with users to maintain their interest and engagement. The second edition of the “Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)” workshop aims to address the developments and challenges in these areas and create a medium for researchers to share their work in progress, present preliminary results, learn from the experience of invited researchers and discuss relevant topics. The workshop focuses on studies on lifelong learning and adaptivity to users, context, environment, and tasks in long-term interactions in a variety of fields such as education, rehabilitation, elderly care, collaborative tasks, service, and companion robots.

Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the AT-POMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n=28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.

For practical reasons, most human-robot interaction (HRI) studies focus on short-term interactions between humans and robots. However, such studies do not capture the difficulty of sustaining engagement and interaction quality across long-term interactions. Many real-world robot applications will require repeated interactions and relationship-building over the long term, and personalization and adaptation to users will be necessary to maintain user engagement and to build rapport and trust between the user and the robot. This full-day workshop brings together perspectives from a variety of research areas, including companion robots, elderly care, and educational robots, in order to provide a forum for sharing and discussing innovations, experiences, works-in-progress, and best practices which address the challenges of personalization in long-term HRI.

Personalized learning environments have the potential to improve learning outcomes for children in a variety of educational domains, as they can tailor instruction based on the unique learning needs of individuals. Robot tutoring systems can further engage users by leveraging their potential for embodied social interaction and take into account crucial aspects of a learner, such as a student’s motivation in learning. In this article, we demonstrate that motivation in young learners corresponds to observable behaviors when interacting with a robot tutoring system, which, in turn, impact learning outcomes. We first detail a user study involving children interacting one on one with a robot tutoring system over multiple sessions. Based on empirical data, we show that academic motivation stemming from one’s own values or goals as assessed by the Academic Self-Regulation Questionnaire (SRQ-A) correlates to observed suboptimal help-seeking behavior during the initial tutoring session. We then show how an interactive robot that responds intelligently to these observed behaviors in subsequent tutoring sessions can positively impact both student behavior and learning outcomes over time. These results provide empirical evidence for the link between internal motivation, observable behavior, and learning outcomes in the context of robot--child tutoring. We also identified an additional suboptimal behavioral feature within our tutoring environment and demonstrated its relationship to internal factors of motivation, suggesting further opportunities to design robot intervention to enhance learning. We provide insights on the design of robot tutoring systems aimed to deliver effective behavioral intervention during learning interactions for children and present a discussion on the broader challenges currently faced by robot--child tutoring systems.

Thinking aloud, while requiring extra mental effort, is a metacognitive technique that helps students navigate through complex problem-solving tasks. Social robots, bearing embodied immediacy that fosters engaging and compliant interactions, are a unique platform to deliver problem-solving support such as thinking aloud to young learners. In this work, we explore the effects of a robot platform and the think-aloud strategy on learning outcomes in the context of a one-on-one tutoring interaction. Results from a 2x2 between-subjects study (n=52) indicate that both the robot platform and use of the think-aloud strategy promoted learning gains for children. In particular, the robot platform effectively enhanced immediate learning gains, measured right after the tutoring session, while the think-aloud strategy improved persistent gains as measured approximately one week after the interaction. Moreover, our results show that a social robot strengthened students» engagement and compliance with the think-aloud support while they performed cognitively demanding tasks. Our work indicates that robots can support metacognitive strategy use to effectively enhance learning and contributes to the growing body of research demonstrating the value of social robots in novel educational settings.

The benefits of personalized social robots must be evaluated in real-world educational contexts over periods of time longer than a single session to understand their full potential to impact learning outcomes. In this work, we describe a personalization system designed for longer-term personalization that orders curriculum based on an adaptive Hidden Markov Model (HMM) that evaluates students’ skill proficiencies. We present a study investigating the effectiveness of this system in a five-session interaction with a robot tutor, taking place over the course of 2 weeks. Our system is evaluated in the context of native Spanish-speaking first-graders interacting with a social robot tutor while completing an English Language Learning educational task. Participants either received lessons: (1) ordered by our adaptive HMM personalization system which selects a lesson based on a skill that the individual participant needs more practice with (“personalized condition”) or (2) ordered randomly from among the lessons the participant had not yet seen (“non-personalized condition”). We found that participants who received personalized lessons from the robot tutor outperformed participants who received non-personalized lessons on a post-test by 2.0 standard deviations on average, corresponding to a mean learning gain in the 98th percentile.

A common practice in education to accommodate the short attention spans of children during learning is to provide them with non-task breaks for cognitive rest. Holding great promise to promote learning, robots can provide these breaks at times personalized to individual children. In this work, we investigate personalized timing strategies for providing breaks to young learners during a robot tutoring interaction. We build an autonomous robot tutoring system that monitors student performance and provides break activities based on a personalized schedule according to performance. We conduct a field study to explore the effects of different strategies for providing breaks during tutoring. By comparing a fixed timing strategy with a reward strategy (break timing personalized to performance gains) and a refocus strategy (break timing personalized to performance drops), we show that the personalized strategies promote learning gains for children more effectively than the fixed strategy. Our results also reveal immediate benefits in enhancing efficiency and accuracy in completing educational problems after personalized breaks, showing the restorative effects of the breaks when administered at the right time.

In intelligent tutoring systems, one fundamental problem that limits learning gains is the unproductive use of on-demand help features, namely overuse or aversion, resulting in students misusing the system rather than engaging in active learning. Social robots as tutoring agents have the potential to mitigate those behaviors by actively shaping productive help-seeking behaviors. We hypothesize that effectual help-seeking behavior is a critical contributor to learning gains in a robot-child tutoring interaction. We conduct a between-subjects study where children interacted with a social robot solving fractions problems over multiple sessions (29 children; 4 sessions per child) in one of two groups. Results showed that participants in our experimental group, who received adaptive shaping strategies from the robot targeting suboptimal help requests, reduced their suboptimal behaviors over time significantly more than a control group, as well as improved their scores from pretest to posttest significantly more than a control group.

This paper describes an extended (6-session) interaction between an ethnically and geographically diverse group of 26 first-grade children and the DragonBot robot in the context of learning about healthy food choices. We find that children demonstrate a high level of enjoyment when interacting with the robot, and a statistically significant increase in engagement with the system over the duration of the interaction. We also find evidence of relationship-building between the child and robot, and encouraging trends towards child learning. These results are promising for the use of socially assistive robotic technologies for long-term one-on-one educational interventions for younger children.

While re-identification of sensitive data has been studied extensively, with the emergence of online social networks and the popularity of digital communications, the ability to use public data for re-identification has increased. This work begins by presenting two different cases studies for sensitive data re-identification. We conclude that targeted re-identification using traditional variables is not only possible, but fairly straightforward given the large amount of public data available. However, our first case study also indicates that large-scale re-identification is less likely. We then consider methods for agencies such as the Census Bureau to identify variables that cause individuals to be vulnerable without testing all combinations of variables. We show the effectiveness of different strategies on a Census Bureau data set and on a synthetic data set.