Figure 1: The NAO robot and a child navigating a map and looking at different trees, symbolizing the collaborative search for personalized interesting topics.

As social robots gain prominence in supporting older adults’ health and well-being, understanding their effectiveness compared to virtual agents remains critical. This study investigated older adults’ perceptions of a physically embodied robot versus its virtual counterpart when taking on the role of an exercise coach. We recruited 25 healthy older adults, each of whom performed a series of exercises with both the physical NAO robot and its virtual simulation displayed on a computer screen. Participants’ experiences were assessed using the Unified Theory of Acceptance and Use of Technology (UTAUT) and the User Engagement Scale (UES) questionnaires collected after each condition. Results indicated that the Perceived Sociability of the NAO robot was significantly higher in the physically embodied condition compared to the virtual condition. However, no significant differences were found in Anxiety, Attitude, Perceived Enjoyment, Perceived Usefulness, Social Intelligence, or Trust. Similarly, the physically embodied NAO scored higher in Perceived Usability and Aesthetic Elements, but no significant differences were observed in Focused Attention and Reward Factor. These results suggest that physical embodiment could enhance perceptions of sociability and usability, however, it does not necessarily impact all engagement-related factors. Our findings contribute to the design of future socially assistive technologies in eldercare.

This paper investigates the efficacy of a passive Brain-Computer Interface (BCI) in enabling a robot tutor to adaptively respond to a user's engagement level in real-time. The BCI system extracted EEG Engagement Index from the user's electroencephalography (EEG) signals as an indicator of engagement during Human-Robot Interaction (HRI). A within-subjects study was conducted in which the robot performed attention-recapturing behavior during a learning task under two conditions; either in an adaptive manner whenever a lapse in the user's engagement level was detected by the BCI system (Adaptive condition) or at random intervals regardless of the user's mental states (Random condition). In both conditions, users completed an information retention test following the interaction. The study found no significant difference in the postinteraction test results or mean EEG Engagement Index values between the Adaptive and Random conditions. However, analysis of 10-sec time windows following robot interventions showed that adaptively timed gestures were significantly more effective in restoring user engagement to optimal level compared to randomly timed gestures. This finding provides evidence for the potential of passive BCIs in improving user experience in pedagogical HRI settings.

Brain Computer Interface (BCI) technology offers the possibility to monitor users’ attention and engagement during learning tasks, enabling adaptation of pedagogical strategies for a personalized learning experience. In this paper, we present an EEG-based passive BCI system for real-time evaluation of user engagement during a language learning task. The EEG Engagement Index, which has been previously associated with attention and vigilance, is measured from three frontal electrodes and used in this system as a neural indicator of engagement. To validate our system, we used it in a human-robot interaction (HRI) setting, in which a robot tutor monitored the learner’s brain activity and adapted its tutoring strategy when a lapse in engagement was detected. We discuss the challenges and preliminary results from our pilot study with eight participants.