Thesis Proposals

Problem: The introduction of XR technologies is presenting unexplored challenges to final viewers. It is not clear where traditional cinematic techniques would also apply to immersive movies and their effects on the users' experience. Objective: Deepening existing studies in the context of cinematic AR/VR to cope with challenges regarding users' behavior in immersive movies, i.e., camera movements, watching conditions, scene cut techniques, fear of missing out, volumetric videos, lighting conditions, dynamic storytelling. Approach: Design studies and realize short immersive movies aimed to validate cinematic techniques and users' behavior when immersed in the watching experience Expected Impact: Enhanced experience for users watching immersive movies

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.

The increasing adoption of immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) is enabling new forms of remote and distributed collaboration. In many emerging scenarios, users operate across different realities—for instance, one participant interacting through AR while another is immersed in VR. These cross-reality settings introduce inherent asymmetries, as users may have access to different visual cues, perspectives, and interaction capabilities. Such differences can significantly influence how individuals coordinate, communicate, and contribute to shared tasks. This thesis aims to investigate the emergence of leadership in cross-reality collaborative environments. In particular, the work will explore whether leadership dynamics are primarily shaped by the technological medium (e.g., AR vs. VR) or by the level of mutual awareness between users, often referred to as co-presence awareness. Understanding these factors is essential for identifying how control, initiative, and decision-making are distributed in asymmetric collaborative settings. The student will design and implement experimental scenarios in which an AR user collaborates with a VR user on shared tasks. The experimental design will systematically vary the level of awareness cues available to participants, such as visibility of actions, gaze direction, or attention indicators. User interactions will be analyzed to assess leadership behaviors, including initiative taking, guidance, and task control. Both qualitative and quantitative methods will be employed to evaluate how different configurations impact collaboration dynamics. The expected outcome of this research is a deeper understanding of how immersive systems can unintentionally introduce imbalances in collaborative interactions. The findings will contribute to the design of cross-reality experiences that promote fair participation, balanced decision-making, and effective collaboration, regardless of the underlying technology. More broadly, the work may inform guidelines and design principles for developing equitable and user-centered XR collaborative systems. References: 1. The Effects of Sharing Awareness Cues in Collaborative Mixed Reality https://pmc.ncbi.nlm.nih.gov/articles/PMC7805624/ 2. Empowerment and embodiment for collaborative MR systems https://doi.org/10.1002/cav.1838 3. Who Owns What? Psychological Ownership in Shared AR https://www.sciencedirect.com/science/article/abs/pii/S107158192100029X 4. Exploring User Behaviour in Asymmetric Collaborative Mixed Reality https://dl.acm.org/doi/abs/10.1145/3562939.3565630 5. Persuasive Vibrations: Effects of Speech-Based Vibrations on Persuasion, Leadership, and Co-Presence During Verbal Communication in VR https://ieeexplore.ieee.org/abstract/document/10108422 6. Towards Cross-Reality Interaction and Collaboration: A Comparative Study of Object Selection and Manipulation in Reality and Virtuality https://ieeexplore.ieee.org/abstract/document/10108776

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

The integration of Large Language Models (LLMs) with Virtual Reality (VR) offers promising opportunities to redefine how users interact with immersive environments. This thesis explores the use of LLMs to support more natural, intuitive, and hands-free interactions in VR, moving beyond traditional input methods like physical controllers. The primary goal is to design and implement a framework that leverages the language understanding capabilities of LLMs to enable voice-driven interactions within VR environments. Potential use cases include hands-free locomotion (e.g., teleportation or virtual walking via spoken commands), interaction with virtual objects or interfaces through natural language, and the integration of multimodal LLMs to interpret visual inputs, such as referencing objects in the environment through verbal cues. The work will involve defining the system architecture, developing prototypes using Unity and voice processing tools, and exploring how LLMs can interpret and respond to user input in real time. The system will be evaluated through user studies, collecting both quantitative and qualitative data to assess usability, command recognition accuracy, response times, and overall user experience. References: [1] D. Calandra, F. G. Pratticò, and F. Lamberti, "Comparison of Hands-Free Speech-Based Navigation Techniques for Virtual Reality Training," in 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, 2022, pp. 85-90. doi: 10.1109/MELECON53508.2022.9842994. [2] J. A. V. Fernandez, J. J. Lee, S. A. S. Vacca, A. Magana, B. Benes, and V. Popescu, "Hands-Free VR," arXiv preprint arXiv:2402.15083, 2024 [3] S. Özdel, K. B. Buldu, E. Kasneci, E. Bozkir “Exploring Context-aware and LLM-driven Locomotion for Immersive Virtual Reality” arXiv preprint arXiv:2504.17331, 2025

Extended Reality (XR) technologies have become powerful platforms for immersive training, simulation, and entertainment. However, the quality and naturalness of user interaction remain constrained by tracking inaccuracies, hardware limitations, and individual differences in perception and comfort. These factors can lead to reduced precision, increased effort, or discomfort phenomena such as cyber-sickness, ultimately diminishing immersion and presence. Recent advances in artificial intelligence and machine learning enable predictive approaches that analyze multimodal data to understand and anticipate user behavior. By processing inputs such as eye gaze, hand motion, physiological signals, application context, and user actions, AI models can infer interaction intent and detect variations in cognitive or physical state in real time. This predictive understanding allows the system to refine interactions dynamically, compensate for inaccuracies, and adapt both the behavior and appearance of interactive elements according to the user’s condition and task, resulting in a more stable and personalized experience. This thesis aims to explore interaction refinement in XR through AI-based predictions of user state, interaction intent, and contextual conditions. Depending on the research direction, the focus may involve predictive enhancement of fine manipulation and control, or adaptive adjustment of interactive representations to mitigate discomfort and sustain immersion. The system will combine multimodal data from eye and hand tracking, user actions, and, when available, physiological sensors within an experimental XR testbed designed to evaluate adaptive interaction performance. The research will include the design, implementation, and evaluation of an AI-driven framework for real-time interaction refinement. User studies will assess its impact on accuracy, comfort, and perceived presence. By leveraging AI to anticipate user intent and dynamically adjust both interaction and representation, this work aims to advance the development of intelligent, user-aware XR systems that provide natural, effective, and comfortable experiences across diverse scenarios. References: D. Calandra and F. Lamberti, "A Testbed for Studying Cybersickness and its Mitigation in Immersive Virtual Reality," IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 12, pp. 7788–7805, Dec. 2024. R. Islam, Y. Lee, M. Jaloli, I. Muhammad, D. Zhu and J. Quarles, "Automatic Detection of Cybersickness from Physiological Signal in a Virtual Roller Coaster Simulation," IEEE VRW, 2020, pp. 648–649. M. P. Olsson et al., "Predicting User Intent in Virtual Reality for Fine Manipulation Tasks," Proceedings of the ACM Symposium on Spatial User Interaction, 2023. D. A. Bowman et al., "Evaluation of Techniques for Predictive Interaction in Virtual Environments," Proceedings of CHI, 2016.

Problem: Students often struggle to engage deeply with complex topics, and most study tools do not support the cognitive benefits of learning-by-teaching. Without a system that lets learners explain and refine knowledge for a “novice,” they miss a powerful mechanism for strengthening understanding. Objective: Study and develop a platform where students “teach” an agent that simulates progressive learning. Using structured memory and adaptive dialogue, the system will tailor future interactions based on what the student has explained. Approach: Select a suitable topic; design and develop an agent (e.g., a chatbot) that builds knowledge from student input through adaptive memory. Validate the tool and compare it to a static, non-adaptive chatbot. Expected Impact: A more engaging learning environment that leverages the learning-by-teaching paradigm to improve comprehension, metacognition, and student motivation.

Wearable AI devices, particularly smart-glasses like Meta Ray-Ban Display, face a "Usage Paradox": the friction between always-on utility (integration with AI without chip implants) and social/environmental constraints (privacy, battery, smartphone competition, and input limitations). Objective: Design and evaluate Edge-AI frameworks, solutions or HCI paradigms that enable smart-glasses to function as context-aware extensions of the user's digital persona without infringing on social or legal norms and expanding possibilities in interacting with digital contents. Different theses path are available to be pursued: 1. Seamless Hybridization with Smartphone: Cross-device interaction (HCI) and low-latency input handoffs between smartphone and smartglass. 2. Privacy Mitigation: Developing software-based solutions and/or pragmatic "Privacy Shields" for bystander protection. 3. Misbehavior or Misuse Detection: Recognizing forbidden usage (e.g., exams, banks, etc.) via sensor fusion (Vision+Bluetooth) without requiring eyewear removal as a prehemptive countermeasure. 4. Low-Power tracking optimization. Optimization of SLAM and object detection pipelines for all-day wearable battery performance. E.G. Exploitation in Entertainment settings, e.g. Augmented Theatres. 5. Impact on Social Dynamics and Behaviours: analyzing how sentiment, and habits of people will be affected by large scale adoption of this devices. Qualitative & quantitative analysis of behavioral shifts in high-frequency AR/AI interaction settings.

Problem: Navigating large-scale virtual environments remains an open challenge in VR research. World-in-Miniature (WIM) interfaces have been studied almost exclusively in flat environments, leaving critical issues unresolved regarding scale management, occlusion handling, and the topographic complexity of real-world spaces. Objective: Extend and deepen WIM research in complex VR contexts, addressing challenges related to: multi-story indoor environments, occlusion management, adaptive scaling techniques, alternative input modalities (mid-air gestures, eye-gaze control), and hierarchical navigation through Points of Interest (POIs). Approach: Design and prototype novel WIM techniques and evaluate them through controlled user studies, measuring navigation efficiency, cognitive load, and spatial knowledge acquisition. Expected Impact: Effective WIM techniques for topographically complex VR environments.

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

Several thesis proposals are available regarding the use of haptic feedback in Virtual Reality, Augmented Reality and Mixed Reality experiences. The proposals are in collaboration with different teams of the IRISA-Inria center at Rennes university and include an internship at their premises (http://www.irisa.fr/en/scientific-departments-irisa). Duration of the thesis/internship is approximately 5-6 months. Experience in C/C++/C#, Unity3D, VR/AR tools, human-machine interaction are required. Topics: - Development of a Multi-Modal Vibro-Thermal Haptic Device for Enhancing Social Touch and Emotional Connection in Virtual Reality - Shifting autonomy for micro- and small-scale robotic swarm control - Optimized time-domain control of teleoperation systems - Modular Multisensory Encounter-Type Haptic Device for Virtual and Augmented Realities More information at https://www.dropbox.com/scl/fo/x4r8xv1g6ilzmnsa5h89o/AEOG27cG-hfn1S2GnLkN4dU?rlkey=ozcy8da3f6d0xgolj7hes40fe&st=wgkge0u8&dl=0

Virtual Reality (VR) has proven effective in simulating procedural tasks and structured training scenarios, particularly in domains where experiential learning is critical. However, current VR learning experiences are often limited in their ability to support dynamic problem-solving, especially in situations where learners are required to devise tools, strategies, or solutions in real time. This lack of adaptability reduces learner agency and constrains the exploration of alternative approaches within immersive environments. This thesis aims to investigate the use of Generative Artificial Intelligence to enhance learner agency in VR-based training systems. The objective is to enable users to interactively solve training scenarios by leveraging AI to generate context-aware suggestions, including tools, hypotheses, and procedural steps. In addition, the work will explore the integration of Reinforcement Learning techniques to adapt the behavior of the AI assistant over time, optimizing when and how support is provided based on user interactions and learning progress. The student will design and develop prototype systems in which users can invoke AI assistance during VR training sessions. The approach will initially focus on unconstrained environments, where learners can freely explore AI-generated solutions in a sandbox setting. Subsequently, domain-specific constraints will be introduced to guide the generation process toward feasible and coherent solutions aligned with the procedural logic of the task. Reinforcement Learning strategies will be investigated to model adaptive assistance policies, allowing the system to personalize feedback, regulate the level of guidance, and improve its effectiveness through interaction. Particular attention will be devoted to balancing flexibility and control, as well as to the definition of meaningful reward signals based on user behavior, task completion, and learning outcomes. The developed systems will be evaluated through qualitative and quantitative methods, considering aspects such as user engagement, perceived agency, solution diversity, coherence with domain knowledge, and overall usability. The expected outcome is an enhancement of learner creativity and autonomy through AI-assisted problem solving, along with the development of adaptive learning pathways tailored to user behavior. This work may also contribute to the definition of intelligent, responsive training environments capable of evolving in real time through the combined use of Generative AI and Reinforcement Learning. References [1] S. Doroudi, V. Aleven, and E. Brunskill, “Where’s the Reward? A Review of Reinforcement Learning for Instructional Sequencing," in International Journal of Artificial Intelligence in Education 29, 2019, pp. 568-620. doi: 10.1007/s40593-019-00187-x. [2] S. Yao et al. “ReAct: Synergizing Reasoning and Acting in Language Models,” arXiv:1907.00456, 2019, pp. 1-16, doi: 10.48550/arXiv.1907.00456. [2] N. Jaques et al. “Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog,” arXiv:2210.03629, 2023, pp. 1-33, doi: 10.48550/arXiv.2210.03629.

Recent advances in Generative Artificial Intelligence are opening new possibilities for the design and development of immersive Virtual Reality (VR) learning experiences. Traditional VR authoring pipelines rely heavily on manual processes, including 3D modelling, scripting, and scenario design, which limit scalability, adaptability, and responsiveness to evolving training needs. At the same time, generative AI techniques are increasingly being integrated into VR systems to automate the creation of environments, assets, and interactions, enabling more dynamic and personalized experiences. This thesis aims to investigate the integration of Generative AI into VR-based training systems, with the objective of automating the creation of virtual environments, interactive objects, and narrative logic. The work will explore how textual or semantic inputs can be leveraged to generate coherent VR experiences by combining generative models with procedural content generation techniques. In particular, hybrid approaches will be considered, where text prompting guides the generation process while procedural systems enforce constraints, consistency, and scalability of the resulting environments. The student will design and develop prototype systems supporting semi-automatic or fully automatic authoring of VR training scenarios. This includes the integration of large language models for scenario generation, generative models for 3D asset creation, and procedural methods for assembling environments and interactions. Particular attention will be devoted to the interaction between generative and procedural components, the definition of intermediate representations, and the design of prompting strategies for controllable and reliable generation. The developed systems will be evaluated through both qualitative and quantitative analyses, considering factors such as generation quality, coherence of the resulting experiences, adaptability to different training contexts, and usability. The expected outcome is a reduction in development time and cost for immersive training applications, along with contributions toward a structured framework for integrating Generative AI and procedural techniques into VR authoring pipelines, supporting scalable and adaptive training systems. References: [1] Y. Song, K. Wu, and J. Ding, “Developing an immersive game-based learning platform with generative artificial intelligence and virtual reality technologies – “LearningverseVR," in Computers & Education: X Reality 4, 2024, pp. 1-7. doi: 10.1016/j.cexr.2024.100069. [2] X. Mao, “Procedural Content Generation via Generative Artificial Intelligence,” arXiv:2407.09013, 2024, pp. 1-15, doi: 10.48550/arXiv.2407.09013. [3] F. Rahimi, A. Sadeghi-Niaraki, and S. Choi “Generative AI Meets Virtual Reality: A Comprehensive Survey on Applications, Challenges, and Future Direction,” in IEEE Access, vol. 13, pp. 94893-94909, 2025, doi: 10.1109/ACCESS.2025.3574779.

Problem: VR-based learning experiences, especially those centered on procedural aspects, demand repeated practice sessions to be effective. However, these experiences often suffer from limited variety, which can hinder educational outcomes due to the principle of "encoding specificity". Objective: Studying and adapting core mechanics from the rogue-lite genre (encompassing the procedural generation of scenarios and retention of progress across game sessions) to create engaging, effective, and coherent VR learning environments and training systems. Approach: Select an appropriate learning procedure; design and develop a VR application integrating rogue-lite mechanics to improve procedural skills. Validate the tool and compare it to a static learning scenario Expected Impact: A new perspective on how to design and implement effective VR learning experiences