Conversational agents (CAs) are transforming human-computer interaction, evolving from text-based chatbots to digital humans (DHs) capable of rich emotional expression. This study explores integrating neural and physiological signals into the perception module of CAs to enable real-time emotion detection and empathetic responses. We conducted a user study in which participants engaged with a DH about emotional topics. The DH mirrored participants’ emotions in real-time using neural and physiological cues. Results showed that users experienced stronger emotions and greater engagement during interactions with the Empathetic DH, highlighting the benefits of these signals for enhancing empathy. However, challenges remain, including recognition accuracy, emotional transition timing, individual differences, and limited voice modulation. Addressing these issues is key to advancing empathetic digital agents. This research demonstrates the promise of real-time physiological and neural emotion recognition for building emotionally intelligent CAs that foster deeper, more meaningful human-agent interactions.

Digital humans are a highly realistic form of conversational computer agent. Eye gaze is a salient social cue that digital humans could use to facilitate rapport-building during conversations. However, eye gaze tendencies vary by gender and incorrect gaze patterns can have negative social implications. Analysis of observational data during human conversations can help inform the development of eye gaze models for digital humans. This study aimed to identify the eye gaze patterns of women dyads during a rapport-building conversation, and to evaluate the effect of different gaze patterns on rapport, trust, and psychological outcomes. 36 adult women (18 dyads) completed the Relationship Closeness Induction Task while wearing eye tracking glasses. Subjective rapport, trust, and psychological measures were collected. Gaze patterns of women were found to change as the conversation content became more intimate; specifically, gaze aversions for thinking (p=.042), turn-taking (p=.025), and intimacy modulation increased in duration (p=.012). Furthermore, gaze patterns were associated with perceptions of the conversation partner. Displaying fewer cognitive gaze aversions was associated with greater closeness (p=.029) and trust perceptions (p=.035). Longer periods of direct gaze while speaking was associated with greater rapport (p=.040). Results will inform the development of a humanlike gaze model for female digital humans during intimate conversations and may be applicable to social robots.

In this paper we present ZenG, a neurofeedback ARapplication concept based on Zen Gardening to fostercreativity, self-awareness, and relaxation through embodiedinteractions in a mixed reality environment. We developedan initial prototype which combined physiological sensingthrough EEG with AR visualisation on the Magic LeapDisplay. We evaluated the prototype through preliminaryuser testing with 12 adults. Results suggest users found theexperience to be enjoyable and relaxing, however theapplication could be improved by including more featuresand functionality. ZenG shows the potential for AR toprovide immersive and interactive environments that couldpromote creativity and relaxation, providing solid groundsfor further research.

Women are underrepresented in robotics, and this may be partly due to the educational emphasis on mechanical applications rather than social applications of robotics. This study aimed to investigate whether teaching robotics using social robots increased girls' engagement compared to using more mechanical vex robots. 20 girls were recruited from school robotics classes. They were taught 30 minutes of VEX robotics and 30 minutes of social robotics in a counter-balanced order. Engagement was measured using questionnaires and observations. Results showed that girls were significantly more engaged in the social robot classes than the vex robot classes. This pilot study suggests a possible way to encourage more girls to study robotics.