About

Christos Mousas is an Associate Professor and director of the Virtual Reality Lab in the School of Applied and Creative Computing at Purdue University. He is also a core faculty member of the Applied AI Research Center at Purdue. His research lies at the intersection of virtual reality, intelligent virtual agents, and human-computer interaction. He develops methodologies and algorithms to bring virtual characters to life and applies concepts from cognitive and experimental psychology to better understand how humans interact with these virtual agents. Mousas's work focuses on enabling virtual agents to communicate, collaborate, and behave in ways that feel natural and socially meaningful within immersive environments. He studies how people perceive cognitive traits of virtual agents, such as intelligence, reasoning, and memory, and investigates which behavioral and social cues, including gaze, proximity, gesture, and speech, most strongly influence user perception and interaction. His research also explores how embodiment, self-similarity, and familiarity affect user experience and performance in immersive human-agent interaction. Mousas's research is published in prominent venues such as IEEE VR, IEEE ISMAR, and ACM CHI. Prior to his current position, he was a Postdoctoral Researcher in the Department of Computer Science at Dartmouth College in 2016. He holds a Ph.D. in Informatics and an M.Sc. in Multimedia Applications and Virtual Environments from the University of Sussex, as well as an integrated Master's degree in Audiovisual Science and Art from Ionian University. He is a member of ACM and IEEE and serves as Associate Editor for several journals including Computer Animation and Virtual Worlds, Frontiers in Virtual Reality, Behaviour & Information Technology, and Empathic Computing. Mousas is actively involved in organizing and serving on program committees across the virtual reality, computer graphics/animation, and human-computer interaction communities. He has received multiple Best Paper Awards and Honorable Mentions from prestigious conferences such as IEEE ISMAR, ACM TiiS, ACM SIGGRAPH VRCAI, ACM CHI, and ACM CHI PLAY.

Research topics

• Computer Science
• Artificial Intelligence
• Multimedia
• Human–computer interaction
• Communication
• Psychology
• Mathematics education
• Medicine
• Medical education
• Engineering
• Mechanical engineering
• Simulation

Selected publications

• Out of Control: Effects of Multimodal Self-similarity on Embodiment During Autonomous Avatar Demonstrations in Virtual Reality

  2026-04-13

  articleOpen accessSenior author
  PublisherDOI

• Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

  Open MIND · 2026-01-29

  preprint

  Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014-2025). We coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (k = .918). Three patterns emerged: (1) approach-context-pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p < .001] and favored project-based learning [p = .009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

  DOI

• ORACLE NUMBERS: A GAMIFIED HUMAN-AI INTERACTION TOOL FOR TEACHING COMPUTATIONAL LOGIC IN DISTANCE LEARNING VIA A SIMULATED VIRTUAL AGENT

  INTED proceedings · 2026-03-01

  articleSenior author
  PublisherDOI

• Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

  2026-04-13 · 1 citations

  articleOpen access

  Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014–2025). We coded each study’s approach, learning context, skill level, modality, pedagogy, and outcomes (κ =.918). Three patterns emerged: (1) approach–context–pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p <.001] and favored project-based learning [p =.009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

  PublisherDOI

• Exploring Perception and Avoidance Behavior toward Real and Virtual Humans in Outdoor Mixed Reality

  2026-03-21

  articleSenior author
  PublisherDOI

• On the Intelligence and Knowledgeability of Virtual Agents

  2026-04-13

  articleOpen accessSenior author

  Intelligence and knowledgeability are sometimes treated interchangeably in virtual agents, yet they shape interaction in different ways. We disentangled these traits and tested how each drives human perceptions and interaction in virtual reality (VR). To address the lack of prior research examining both traits simultaneously, we created a VR application where participants collaborated with a virtual agent to complete a jigsaw puzzle while engaging in free-flowing conversation about the puzzle’s art piece. We manipulated intelligence through the virtual agent’s puzzle-solving ability and knowledgeability through its predefined depth of knowledge in art. Using a 2 × 2 within-group study, we collected perceptual responses, logged data, and qualitative feedback. Results showed intelligence significantly influenced perceptions of intelligence, knowledge, rapport, trust, co-presence, uncanny valley, and intelligence and knowledge comparisons, while knowledgeability impacted perceived knowledge, trust, and intelligence and knowledge comparisons. Interaction effects further highlighted their interdependence, offering design implications for virtual agents.

  PublisherDOI

• Demographic Influences on Balance-Based Locomotion in Virtual Reality

  2026-03-21

  articleSenior author
  PublisherDOI

• The Motion is the Message: Evaluating Motion Tracking Quality for VR Avatars

  IEEE Transactions on Visualization and Computer Graphics · 2026-04-03

  article

  Motion tracking to project users into embodied virtual reality (VR) as avatars is an essential application of real-time computer graphics. Most current embodied VR systems rely on head-mounted displays (HMDs) to estimate user pose, as headset sensors can track the head and hands, thereby reconstructing the full body without the need for external hardware. However, measuring the quality of motion reconstruction algorithms from HMD-based tracking, particularly those intended for use in social settings, remains challenging due to the complex interaction between motion and perceived social signals. This paper compares two industrial tracking reconstruction solutions, called HMD1 (i.e., a basic HMD-based method that uses head tracking and hand positions estimated from HMD cameras) and HMD2 (i.e., an advanced HMD-based method with additional onboard camera streams), that estimate user motion using only an HMD against ground-truth motion capture (MoCap) data. It advocates for a social signal-based analysis that views motion as a communication medium and employs user observations to measure whether viewers successfully perceive the information encoded in motion. Across 156 socially expressive clips, Social Signal ratings were more effective than generic measures at revealing differences between the HMD methods. HMD2 preserved social meaning more accurately than HMD1, with fewer significant deviations from MoCap, while both HMD methods were frequently rated less natural than MoCap. A qualitative review localized recurrent failure modes, such as arm swivel/shoulder errors, posture reconstruction issues, and floating/stance artifacts, which help explain the misreading of social signals. We release a dashboard scorecard, motion capture data, and a benchmark protocol to enable consistent motion evaluation. More generally, this work advocates for an underexplored approach to motion evaluation that focuses on assessing the semantics of motion to determine quality. As reliance on generative artificial intelligence (AI) increases, it is essential to standardize evaluation to preserve the authenticity of the social signals conveyed. The developed dataset and the evaluation framework are provided on our project's website: https://github.com/facebookresearch/MotionIsTheMessageDataset.

  PublisherDOI

• Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

  OSF Preprints (OSF Preprints) · 2026-01-25

  other
  Publisher

• Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

  ArXiv.org · 2026-01-29

  articleOpen access

  Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014-2025). We coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (k = .918). Three patterns emerged: (1) approach-context-pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p < .001] and favored project-based learning [p = .009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

  PublisherOA PDF

Frequent coauthors

• Christos‐Nikolaos Anagnostopoulos

  University of the Aegean

  30 shared

• Dominic Kao

  Purdue University West Lafayette

  24 shared

• Alexandros Koilias

  University of the Aegean

  24 shared

• Banafsheh Rekabdar

  Portland State University

  24 shared

• Minsoo Choi

  17 shared

• Dixuan Cui

  Sam Houston State University

  13 shared

• Paul Newbury

  13 shared

• Nicoletta Adamo‐Villani

  Purdue University System

  9 shared

Labs

• Virtual Reality Lab, Purdue UniversityPI

Education

• Postdoc, Department of Computer Science

  Dartmouth College

  2016

• PhD Informatics, School of Engineering and Informatics

  University of Sussex

  2015

Awards & honors

• Best Journal Paper Award, IEEE International Symposium on Mi…
• Best Paper Award, ACM Transactions on Interactive Intelligen…
• Recognition of Service Award, IEEE VR, 2023
• Best Paper Award, ACM SIGGRAPH VRCAI 2022
• Recognition of Service Award, ACM CHI Play 2022