About

Dominic Kao is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign and the director of the Virtual Futures Lab. His research interests focus on games, virtual reality, and human-computer interaction. He aims to build and leverage virtual worlds to understand their influence on people and to develop best practices for creating games and learning environments. Kao's work broadly encompasses games research, HCI, and virtual reality. He holds a Ph.D. in Computer Science from MIT, a M.S.E. in Computer Science from Princeton University, and a B.S. in Computer Engineering from the University of Alberta. Prior to his academic career, he worked as a game developer at Electronic Arts.

Research topics

• Computer Science
• Human–computer interaction
• Multimedia
• Simulation
• Artificial Intelligence
• Engineering
• Mechanical engineering
• Computer vision

Selected publications

• On the Intelligence and Knowledgeability of Virtual Agents

  2026-04-13

  articleOpen access

  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

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

  2026-04-13

  articleOpen access
  PublisherDOI

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

  ArXiv.org · 2026-01-29

  articleOpen accessSenior author

  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

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

  Open MIND · 2026-01-29

  preprintSenior author

  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 &lt; .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

• 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

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  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

• Let's Do It My Way: Effects of Personality and Age of Virtual Characters

  IEEE Transactions on Visualization and Computer Graphics · 2025-10-03 · 2 citations

  article

  Designing interactions between humans and virtual characters requires careful consideration of various human perceptions and user experiences. While numerous studies have explored the effects of several virtual characters' properties, the impacts of the virtual character's personality and age on human perceptions and experiences have yet to be thoroughly investigated. To address this gap, we conducted a within-group study (N = 28) following a 2 (personality: egoism vs. altruism) × 2 (age: child vs. adult) design to explore how the personality and age factors influence human perception and experience during interactions with virtual characters. In each condition of our study, our participants co-solved a jigsaw puzzle with a virtual character that embodied combinations of personality and age. After each condition, participants completed a survey. We also asked them to provide written feedback at the end of the study. Our statistical analyses revealed that the virtual character's personality and age significantly influenced participants' perceptions and experiences. The personality factor affected perceptions of altruism, anthropomorphism, likability, safety, and all aspects of user experience, including perceived collaboration, rapport, emotional reactivity, and the desire for future interaction. Additionally, the virtual character's age affected our participants' ratings of the uncanny valley and likability. We also identified an interaction effect between personality and age factors on the virtual character's anthropomorphism. Based on our findings, we offered guidelines and insights for researchers aiming to design collaborative experiences with virtual characters of different personalities and ages.

  PublisherDOI

• PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models

  ArXiv.org · 2025-09-19

  preprintOpen access

  Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolving query ambiguity and credit assignment during reward learning. In this paper, we introduce PRIMT, a PbRL framework designed to overcome these challenges by leveraging foundation models (FMs) for multimodal synthetic feedback and trajectory synthesis. Unlike prior approaches that rely on single-modality FM evaluations, PRIMT employs a hierarchical neuro-symbolic fusion strategy, integrating the complementary strengths of large language models and vision-language models in evaluating robot behaviors for more reliable and comprehensive feedback. PRIMT also incorporates foresight trajectory generation, which reduces early-stage query ambiguity by warm-starting the trajectory buffer with bootstrapped samples, and hindsight trajectory augmentation, which enables counterfactual reasoning with a causal auxiliary loss to improve credit assignment. We evaluate PRIMT on 2 locomotion and 6 manipulation tasks on various benchmarks, demonstrating superior performance over FM-based and scripted baselines.

  PublisherOA PDFDOI

• Towards Understanding the Impact of AI-Generated Visual and Vocal Self-Similarity on Avatar Identification, Motivation, and Engagement in Educational Games

  2025-09-30 · 1 citations

  articleSenior author
  PublisherDOI

• A Systematic Review of Generative AI on Game Character Creation: Applications, Challenges, and Future Trends

  IEEE Transactions on Games · 2025-01-01 · 9 citations

  reviewOpen accessSenior author

  In this paper, we review the impact of generative artificial intelligence (AI) on game character creation. We critically examine the application of AI-driven computer graphics (AICG) technology across the character creation workflow, including concept generation, clothing design, modeling, props, cultural embedding, personality traits, and behaviors. Our research identifies potential applications, challenges, and future trends of these technologies in game development. Furthermore, it explores how AI and large language models (LLMs) can streamline workflows, automate asset generation, and reduce technical barriers in character creation. In this systematic review, we provide valuable insights for AI developers, game designers, and researchers.

  PublisherDOI

Recent grants

• CAREER: Broadening Participation in Computing through Virtual Identities

  NSF · $219k · 2024–2025

Frequent coauthors

• Christos Mousas

  Purdue University West Lafayette

  24 shared

• D. Fox Harrell

  16 shared

• Alexandros Koilias

  University of the Aegean

  10 shared

• Alejandra J. Magana

  Bridge University

  8 shared

• Minsoo Choi

  7 shared

• Amogh Joshi

  Purdue University System

  7 shared

• Banafsheh Rekabdar

  Portland State University

  5 shared

• Rabindra Ratan

  Michigan State University

  4 shared

Labs

• Virtual Futures LabPI

  Research interests are in games, virtual reality, and human-computer interaction.

Awards & honors

• ACM Transactions on Interactive Intelligent Systems (TiiS) B…
• ACM Human Factors in Computing Systems (CHI) Honorable Menti…
• ACM Human Factors in Computing Systems (CHI) Honorable Menti…
• ACM Foundations of Digital Games (FDG) Best Paper (2023)
• ACM Foundations of Digital Games (FDG) Best Paper Nominee (2…