Large language models (LLMs) are rapidly being adopted for tasks like drafting emails, summarizing meetings, and answering health questions.In these settings, users may need to share private information (e.g., contact details, health records).To evaluate LLMs' ability to identify and redact such information, prior work introduced real-life, scenario-based benchmarks (e.g., ConfAIde, PrivacyLens) and found that LLMs can leak private information in complex scenarios.However, these evaluations relied on proxy LLMs to judge the helpfulness and privacy-preservation quality of LLM responses, rather than directly measuring users' perceptions.To understand how users perceive the helpfulness and privacy-preservation quality of LLM responses to privacy-sensitive scenarios, we conducted a user study (n = 94) using 90 PrivacyLens scenarios.We found that users had low agreement with each other when evaluating identical LLM responses.In contrast, five proxy LLMs reached high agreement, yet each proxy LLM had low correlation with users' evaluations.These results indicate that proxy LLMs cannot accurately estimate users' wide range of perceptions of utility and privacy in privacy-sensitive scenarios.We discuss the need for more user-centered studies to measure LLMs' ability to help users while preserving privacy, and for improving alignment between LLMs and users in estimating perceived privacy and utility.

Exercise supports healthy aging, but motivation often declines with age, increasing demand on therapists and coaches. We present a conversational robotic exercise coach that promotes engagement and assesses motivation through dialogue. In a WoZ study with ten adults aged 59 and above, participants showed varied interaction styles; even those with low motivation rated sessions positively, suggesting such agents can enhance exercise enjoyment. We identify three design needs for autonomous coaches: rephrasing for clarity, conversation beyond exercise, and adaptable speech delivery.

Different people respond to feedback and guidance in different ways, and their preferences may change based on their mood, tiredness, etc. We present a robot exercise coach that provides verbal and nonverbal feedback in two different styles: firm and encouraging. We collect a dataset of people experiencing both feedback styles and show that the style that someone performs best with may not be the one they have the best subjective experience with or be the one that they state they prefer. To account for this, we present a contextual bandit approach that enables the robot coach to learn the best style to use over time to improve the human's performance, and show that this approach performs quite well in expectation on the real human data.

Different people respond to feedback and guidance in different ways. Their preferences may even change depending on their mood, fatigue, physical health, etc. We present a robot exercise coach that provides both verbal and nonverbal feedback. We first introduce an exercise evaluation method where the camera feed from the robot is used to evaluate how well people perform exercises. We then present a multi-modal feedback controller that uses the exercise evaluation to respond with verbal and nonverbal feedback in different styles (firm and encouraging). Our user study found that participants have significantly different performances and subjective experiences with the different styles. We also found differences in how participants with different preferences for the styles perform with the different styles. These results show that varying feedback styles has an impact and builds the basis for a robot that adapts its style in real-time to personalize to the individual.

Nonverbal communication in the field of education can allow teachers to emotionally support their students and improve educational experience and performance. Robot nonverbal movements have been shown to improve both subjective experiences and task performance, and this work investigates whether affective robot behavior can improve human learning. This is tested using an online sorting game where players learn easy or difficult rules, aided by robot feedback videos that contain either neutral or affective movements. Results indicate that affective robot behavior improves learning of the sorting rules and reduces the perceived difficulty of the task. Extensions include expanding the features used to determine the robot feedback and increasing the possible robot motions to create a rich set of robot feedback options to personalize the education experience further for the student.