Alessandro Visconti

xtended Reality (XR) technologies are opening new frontiers for the study of human cognition, emotion, and social behavior by enabling immersive, controllable, and interactive environments. Despite their strong potential, XR systems are still underutilized in psychological research, which traditionally relies on less dynamic and less ecologically valid experimental settings. As a result, fundamental questions about how immersive experiences shape mental processes, perception, and behavior remain only partially explored.
This thesis aims to investigate how XR can be used as a tool to study and influence human experience, focusing on the role of adaptive and embodied virtual agents, immersive environments, and psychologically-driven interventions. In particular, the work will explore three complementary research directions: (i) the impact of virtual agent embodiment on user engagement, social presence, and self-disclosure; (ii) the use of eye-tracking data to analyze attentional patterns and predict perceived immersivity; and (iii) the effects of cyber-hypnosis and immersive suggestion techniques on arousal and subjective experience.
The student will design and implement XR experimental scenarios integrating virtual agents, interactive environments, and adaptive mechanisms driven by multimodal data. Behavioral and physiological signals (such as gaze, interaction patterns, and biosignals) will be used to personalize the experience in real time. User studies will be conducted to evaluate the impact of different experimental conditions, combining objective measures with subjective assessments of presence, engagement, emotional response, and perceived immersion. The work will be carried out in collaboration with the University of Florence (UniFi), Department of Psychology, ensuring interdisciplinary integration between technological development and psychological research.
The expected outcome of this research is a deeper understanding of how immersive technologies influence human experience and behavior. The findings are expected to contribute to both methodological advancements in XR-based psychological research and practical guidelines for designing immersive systems in domains such as training, therapy, and human-centered applications. More broadly, the thesis will help establish XR as a systematic and reliable tool for investigating psychological and social processes in controlled yet ecologically valid environments.
References:
Bosta, A., Vosinakis, S. (2026). Assessing the Immersive Experience with Physiological Measures: A Systematic Literature Review. In: Michael-Grigoriou, D., Zachmann, G., Kopper, R., Yoon, S.H., Zollmann, S., Bourdot, P. (eds) Virtual Reality and Mixed Reality. EuroXR 2025. Lecture Notes in Computer Science, vol 16101. Springer, Cham. https://doi.org/10.1007/978-3-032-03805-0_10

Organizations increasingly operate in volatile, uncertain, and information-dense environments where effective decision-making depends on the ability to rapidly synthesize large volumes of data, align diverse perspectives, and coordinate action across distributed teams. Traditional decision-support systems, however, remain largely static and non-immersive, offering limited mechanisms for real-time collaboration, strategic alignment, and cognitive workload management. Despite significant advances in Artificial Intelligence, Virtual Reality, and Augmented Reality, the integration of XR technologies with adaptive management tools capable of actively supporting collaborative decision-making processes remains largely unexplored.
This thesis aims to investigate how intelligent XR environments, integrating AI, VR, and AR, can enhance collaborative decision-making, strategic coordination, and sensemaking in complex organizational contexts. The research will explore how adaptive immersive systems can dynamically personalize information presentation, simulation scenarios, and communication strategies to improve team coordination, reduce cognitive overload, and facilitate consensus-building and negotiation among decision-makers.
The student will design and develop a collaborative XR platform that combines immersive analytics, virtual agents, and AI-driven adaptive interfaces. The system will leverage multimodal behavioral signals, including gaze patterns, spatial movement, interaction dynamics, and response timing, to continuously assess collaboration quality, cognitive load, and individual engagement. Based on these inferences, the platform will dynamically personalize visualizations, recommendations, and decision-support strategies in real time, enabling teams to navigate complex information spaces more effectively. Particular attention will be devoted to the design of adaptive mechanisms for group sensemaking, the modeling of coordination signals, and the integration of human–agent and human–human decision loops within immersive environments.
The developed solutions will be evaluated through user studies combining objective metrics, such as decision quality, coordination efficiency, task completion time, and interaction patterns, with subjective assessments of cognitive load, strategic alignment, perceived collaboration effectiveness, and user experience within the XR environment.
The expected outcome of this research is the identification of design principles and adaptive strategies for XR-based decision-support systems that actively enhance collaborative intelligence in organizational settings. The findings are expected to advance the fields of Human-AI collaboration, immersive analytics, and strategic teamwork, informing the development of next-generation decision environments in which intelligent agents and adaptive interfaces play a central role in supporting complex, high-stakes organizational decisions.
References:
1. B. Han et al., “Exploring Mediation by an Embodied Virtual Agent in Immersive Triadic Collaborative Decision-Making,” in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2026.3679103.
2. D. Garkov et al., “Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis,” in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2026.3671472.

Extended Reality (XR) technologies are creating new opportunities for social interaction and collaboration by enabling shared immersive environments where users and virtual agents can communicate and work together. These environments have the potential to reduce social friction, awkwardness, and stress (especially among unfamiliar users) by mediating interaction through intelligent interfaces. However, current XR systems rarely incorporate adaptive or personalized strategies to actively support social dynamics in real time, limiting their effectiveness in fostering natural and engaging communication.
This thesis aims to explore how adaptive and intelligent virtual agents, together with social augmentation interfaces, can enhance communication and collaboration in XR environments. The focus will be on both human–human and human–agent interactions, investigating how real-time adaptation can reduce social discomfort while improving coordination, engagement, trust, and mutual understanding among participants.
The student will design and develop XR scenarios in which virtual agents and social interfaces act as companions, mediators, or teammates within collaborative tasks. The system will leverage multimodal signal (such as gaze behavior, posture, movement, and interaction dynamics) to continuously infer the state of the interaction and adapt communication strategies accordingly. This may include modifying agent behavior, expressivity, timing, and social roles to better support users and facilitate smoother interactions. Particular attention will be devoted to the design of adaptive mechanisms, the modeling of social signals, and the integration of human–agent and human–human interaction loops. The research will be conducted in collaboration with the Adelaide University, specifically with the Empathic Computing Lab, fostering an interdisciplinary and international research context.
The developed solutions will be evaluated through user studies combining objective metrics (such as coordination efficiency, response timing, and interaction patterns) with subjective assessments of social presence, communication quality, trust, and perceived collaboration effectiveness.
The expected outcome of this research is the identification of design principles and adaptive strategies for XR systems that actively support and enhance social interaction. The findings are expected to inform the development of next-generation immersive environments in which intelligent agents and interfaces play an active role in improving teamwork, communication, and overall user experience.
References:
A. Visconti, D. Calandra, F. Giorgione and F. Lamberti, “Enhancing Social Experiences in Immersive Virtual Reality with Artificial Facial Mimicry,” in IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 3325-3335, May 2025, doi: 10.1109/TVCG.2025.3549163.

As robots become increasingly present in industrial and collaborative environments, effective human-robot interaction (HRI) is emerging as a critical challenge. While industrial robots have reached high levels of precision and efficiency, they are still primarily designed with a strong focus on functionality and automation, with limited attention to social and communicative aspects of interaction. As a consequence, robots are often perceived as intimidating, unpredictable, and emotionally distant, which can reduce user trust, increase anxiety, and negatively affect collaboration in shared workspaces.
A promising direction to address these limitations is the integration of human-like social and communicative behaviors into robotic systems. In human-human interaction, non-verbal cues such as movement expressivity, gaze direction, and feedback signals play a fundamental role in conveying intention and improving predictability. Introducing similar mechanisms in industrial robots could make their behavior easier to interpret, fostering a sense of safety and improving the overall interaction experience. Despite growing interest in social robotics, the application of these concepts in industrial contexts remains relatively underexplored, especially in scenarios requiring real-time adaptation to the user.
The objective of this thesis is to design and implement an adaptive interaction system for a collaborative industrial robot, capable of modifying its behavior dynamically based on the ongoing interaction with the human user. The system will leverage multimodal signals such as movement patterns, spatial proximity, and interaction dynamics to adjust aspects of the robot’s behavior, including motion expressivity, communicative feedback, and interaction timing. The goal is to create interactions that feel more natural, predictable, and aligned with human expectations, without compromising task efficiency.
The proposed solution will be integrated into a collaborative robotic platform and evaluated through a user study. Both subjective and objective measures will be considered to assess the effectiveness of the approach, including perceived trust, safety, user comfort, and task performance. The study will compare adaptive and non-adaptive behaviors to understand the impact of socially aware interaction strategies on human-robot collaboration.
This research is expected to contribute to the development of socially intelligent industrial robots capable of improving trust, reducing anxiety, and enabling more effective and intuitive collaboration. The results may have implications for a wide range of applications, including manufacturing, logistics, and training environments, where humans and robots increasingly operate side by side.
References:
[1] Linda Onnasch and Clara Laudine Hildebrandt. 2021. Impact of Anthropomorphic Robot Design on Trust and Attention in Industrial Human-Robot Interaction. J. Hum.-Robot Interact. 11, 1, Article 2 (March 2022), 24 pages. https://doi.org/10.1145/3472224
[2] Naendrup-Poell, L., Onnasch, L. Predictive robot eyes shape visual attention, performance, and trust in interaction with an industrial CoBot. Sci Rep 16, 14171 (2026). https://doi.org/10.1038/s41598-026-50476-4
[3] Esmeralda Faria, Ana Pinto, Soraia Oliveira, Gustavo Assunção, Carla Carvalho, Paulo Menezes, Collaborative robots and user trust: The role of saccadic gaze, anthropomorphic motion, and repetitive training, Computers in Human Behavior Reports, Volume 21, 2026, 100938, ISSN 2451-9588, https://doi.org/10.1016/j.chbr.2026.100938
[4] Jessup, S.A., Alarcon, G.M., Harris, K.N. et al. The Influence of Robot Anthropomorphism and Trust Violation Types on Trustworthiness Perceptions and Trust Behaviors. Int J of Soc Robotics 17, 1437–1452 (2025). https://doi.org/10.1007/s12369-025-01295-6

Workplace stress in corporate and office environments is an increasingly critical issue, with significant implications for employee wellbeing, productivity, and long-term mental health. Existing stress management solutions are often generic, reactive, and detached from the actual work context, providing limited support when it is most needed. At the same time, Extended Reality (XR) technologies offer new opportunities to create immersive, adaptive, and context-aware interventions. However, their potential for real-time stress detection and mitigation in workplace settings remains largely unexplored.
This thesis aims to investigate how XR technologies can be leveraged to monitor, detect, and actively reduce stress in office and corporate environments. The focus will be on integrating multimodal physiological and behavioral signals to enable personalized, real-time interventions that support employee wellbeing. Particular attention will be devoted to understanding how adaptive XR experiences can respond dynamically to users’ internal states and environmental conditions.
The student will design and develop a system capable of continuously assessing stress levels through biofeedback data, such as heart rate variability, skin conductance, gaze behavior, and posture. Based on these signals, the system will trigger adaptive XR interventions tailored to the user’s current condition. These interventions may include immersive Virtual Reality relaxation scenarios, guided breathing and mindfulness exercises, and Mixed Reality overlays designed to reduce cognitive load and promote recovery during work activities. Emphasis will be placed on real-time adaptation, personalization strategies, and the seamless integration of interventions into everyday workflows.
The developed solution will be evaluated through user studies combining objective physiological measures with subjective assessments of perceived stress, recovery, and overall wellbeing. The analysis will consider the effectiveness, usability, and acceptability of XR-based interventions in realistic workplace scenarios.
The expected outcome of this research is the definition of novel approaches for technology-driven stress management in professional environments. The results are expected to contribute to the design of intelligent, adaptive XR systems for corporate wellness programs, providing guidelines and best practices for delivering timely, personalized, and effective stress reduction interventions in the workplace.
References:
Lakmal Meegahapola, Marios Constantinides, Zoran Radivojevic, Hongwei Li, Michael Eggleston, and Daniele Quercia. 2026. Stress Mindset Matters: Rethinking Mental Stress Detection with Multimodal Wearable Sensors. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). Association for Computing Machinery, New York, NY, USA, Article 1026, 1–26. https://doi.org/10.1145/3772318.3791340

Socially immersive Virtual Reality environments allow users to interact through avatars, yet current systems often animate only the head and hands, while the rest of the body relies on basic Inverse Kinematics (IK). This can result in unnatural gestures and physical interactions, reducing social presence, engagement, and the overall quality of multi-user experiences.
This thesis aims to enhance avatar-based social interaction animation by making movements more realistic, expressive, and responsive to interpersonal exchanges. The goal is to support natural social gestures such as handshakes, high-fives, hugs, or other forms of embodied communication, improving the sense of presence and fostering richer collaborative and social experiences in VR.
Machine Learning techniques may be used to refine avatar animations beyond standard IK solutions, but the primary focus is on the quality and naturalness of social interactions, rather than on the algorithms themselves. Other key aspects include adaptive management of avatar morphology, real-time collision handling, and the development of a multi-user VR system optimized for seamless, context-aware social interactions.
The expected outcome is a more immersive and socially engaging VR experience, where users can interact naturally through avatars that behave intuitively and expressively, enhancing both social presence and collaboration without compromising system performance.
References:
1. Visconti, Alessandro; Macaluso, Roberta; Di Bartolomei, Gabriele; Calandra, Davide; … (2026)
Improving fidelity of close social interaction animations in social VR with a machine learning-based refinement framework. In: 38th International Conference on Computer Animation and Social Agents (CASA 2025), https://dx.doi.org/10.1007/978-981-95-0100-7_13