Daijin Yang is an interdisciplinary researcher and game designer working at the intersection of human-computer interaction, game design, computational creativity, and human-AI collaboration. He is currently a Ph.D. candidate in Interdisciplinary Media and Design at Northeastern University and holds an M.S. in Game Science and Design from Northeastern University.
Daijin’s work explores how artificial intelligence can support creative practice, learning, and playful interaction. Rather than treating AI as only a content generator, his research investigates how AI systems can become partners in design, storytelling, education, and game creation. His projects often combine system design, game prototyping, user research, and qualitative analysis to understand how people interact with AI-driven creative tools.
His major accomplishments include designing and studying mixed-initiative storytelling games, developing interfaces that connect pedagogy with playable game structures, and synthesizing emerging research on generative AI in games. Through this work, he has contributed to conversations around AI-assisted game design, co-creative storytelling, educational game design, and the future role of large language models in interactive media.
Across his research and design practice, Daijin aims to build systems that make complex ideas more accessible, support human creativity, and create meaningful forms of play. His broader goal is to advance human-centered AI tools that help people learn, design, and imagine through interactive experiences.
Research/Publications Highlights
Huang, X., Wang, C., Hao, Y., Yang, D., & Ray, L. C. (2026). “Not Human, Funnier”: How Machine Identity Shapes Humor Perception in Online AI Stand-up Comedy. arXiv:2602.12763.
This paper studies how audiences perceive AI-generated stand-up comedy and shows that explicitly presenting AI with a machine identity can shape humor perception and make AI comedy more effective.
Yang, D., Kleinman, E., & Harteveld, C. (2026). Bridging Pedagogy and Play: Introducing a Language Mapping Interface for Human-AI Co-Creation in Educational Game Design. In Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), 1–6. ACM. doi:10.1145/3772363.3798862
This paper presents a language-mapping interface that helps designers and instructors translate pedagogical goals into playable educational game structures while maintaining human control in AI-assisted design.
Wu, S., & Yang, D. (2025). Automated Item Neutralization for Non-Cognitive Scales: A Large Language Model Approach to Reducing Social-Desirability Bias. arXiv:2509.19314.
This paper explores how large language models can rewrite non-cognitive assessment items to reduce social-desirability bias, while examining the effects on reliability, factor structure, and measurement invariance.
Yang, D., Kleinman, E., & Harteveld, C. (2025). GPT for Games: An Updated Scoping Review (2020–2024). IEEE Transactions on Games, 1–16. doi:10.1109/TG.2025.3563780
This journal article synthesizes recent research on GPT applications in games, mapping major areas such as procedural content generation, mixed-initiative design, gameplay, game-playing agents, and game user research.
Yang, D., Kleinman, E., & Harteveld, C. (2024). GPT for Games: A Scoping Review (2020–2023). In 2024 IEEE Conference on Games (CoG), 1–8. IEEE. doi:10.1109/CoG60054.2024.10645548
This conference paper reviews early research on GPT in games and identifies key application areas and future directions for generative AI in game development and game studies.
Yang, D., Kleinman, E., Troiano, G. M., Tochilnikova, E., & Harteveld, C. (2024). Snake Story: Exploring Game Mechanics for Mixed-Initiative Co-Creative Storytelling Games. In Proceedings of the 19th International Conference on the Foundations of Digital Games (FDG ’24), Article 3, 1–11. ACM. doi:10.1145/3649921.3649996
This paper introduces Snake Story, a mixed-initiative storytelling game that combines classic Snake mechanics with AI-generated narrative choices to study how gameplay shapes co-writing, authorship, and player experience.
Mohan, N., Simmons, T. L., Khedkar, A., Yang, D., & Chukoskie, L. (2023). Measuring Gaze Behavior to Characterize Spatial Awareness Skill in Rocket League. In 2023 IEEE Conference on Games (CoG), 1–8. IEEE. doi:10.1109/CoG57401.2023.10333185
This paper uses gaze behavior in Rocket League to examine how players’ visual attention patterns relate to spatial awareness and expertise in complex gameplay.
Yu, Y., Yang, D., Tang, Q., Wang, X., Yang, N., Cheng, M., Zhong, Y., Adu, K., & Ekong, F. (2023). Neural Image Caption Generator Based on Crossbar Array Design of Memristor Module. Neurocomputing, 560, 126766. doi:10.1016/j.neucom.2023.126766
This article proposes a neural image captioning approach based on memristor crossbar-array design, contributing to AI hardware and efficient neural computation.
Yang, D. (2023). Designing Mixed-Initiative Video Games. Northeastern University. arXiv:2307.03877.
This thesis develops and studies Snake Story as a mixed-initiative game, examining how game mechanics can make human-AI co-creation more engaging, accessible, and playful.
Yang, D., Zhou, Y., Zhang, Z., Li, T. J.-J., & Ray, L. C. (2022). AI as an Active Writer: Interaction Strategies with Generated Text in Human-AI Collaborative Fiction Writing. In A. Smith-Renner & O. Amir (Eds.), Joint Proceedings of the ACM IUI Workshops 2022, CEUR Workshop Proceedings, Vol. 3124, 56–65. CEUR-WS.
This workshop paper studies human-AI collaborative fiction writing and shows how writers interact with generated text, including editing, regenerating, and treating AI as an active creative collaborator.
Departments
Interdisciplinary, Art + Design
Education
- Ph.D. Canditate in Interdisciplinary Media and Design – Northeastern University
- M.S. in Game Science and Design – Northeastern University
- B.Eng in Electronic Science and Technology – University of Electronic Science and Technology of China
Research Focus
- Human-AI Interaction
- Game Design
- Mixed-Initiative Co-Creativity