Technology for a Better World

About Me

Hello, I am Hajun Kim. I'm currently working at NEOWIZ Games as an AI researcher.

My research explores the critical tension between AI-driven efficiency and the preservation of human cognition. I design AI-augmented interfaces and interaction patterns that reshape user workflows, prioritizing human-led reasoning and agency. This approach enhances core abilities like critical thinking, learning, and creativity.

Education

2022 Korean Advance Institute of Science & Technology(KAIST)

M.S in Graduate School of Culture Technology
(specialization in Human Computer Interaction)
Advisor: Juhan Nam & Byungjoo Lee

2020 Handong Global University(HGU)

B.S in Information and Communication Technology Convergence (specialization in Human Computer Interaction)
B.S in User Experience Engineering

Work Experience

2025 NEOWIZ (Game Company)

AI Researcher in AI R&D Center
Key Research Contributions:
- Developing Novel Pipelines for interaction among Character Agents (LLM-based)
- Developing an image generation tool, focusing on Designer UX and Controllability through user studies.

Selected Research

IdeAmigos: Human-Orchestrated Multi-agent Tool for Supporting End-to-End Brainstorming

(In preparation: DIS 2026 Paper)
Hajun Kim, Jeongeon Park

Developed a multi-agent system enabling users to actively orchestrate AI agents with distinct roles for end-to-end brainstorming.
Designed a system that allowed users to tune agent-level attributes (e.g., Creativity) and dialogue-level flow (e.g., Response Length).
Conducted a within-subjects study (N=12) comparing the user-orchestrated tool against a single-agent brainstorming tool.
Results suggest that user-orchestration led to higher-quality and more diverse ideas, while highlighting concerns about social presence and user bias.

Generative AI as a Collaborative Mediator: Design Opportunities, and Concerns in Creative Collaboration

(Under Review: Revision at CSCW 2026 Paper)
Hajun Kim*, Jini Kim* (co-first author), Yunjae Choi

Explored GenAI tools as mediators for inter-creator collaboration via interviews (N=20; narrative writers & visual designers) and a participatory design study.
Analyzed the collaboration challenges between writers and designers during the initial conceptualization phase.
Conducted a participatory design study, using multi-stage stimuli to help creators envision GenAI-mediated collaboration.
Identified design opportunities (e.g., bridging knowledge gaps) and creator concerns (e.g., privacy issues, reduced communication) for GenAI as a mediator.

Unlocking Creator-AI Synergy: Challenges, Requirements, and Design Opportunities in AI-Powered Short-Form Video Production

(CHI 2024 Paper)
Hajun Kim*, Jini Kim* (co-first author)

Explored AI-powered Short-form Video Generators (ASVG) as creator collaboration tools via interviews (N=17) and design workshops (N=12).
Analyzed participation workshops, defining three distinct creator types and their challenges.
Conducted participatory workshops, using four distinct stimuli to help creators envision collaborative AI tools.
Identified design opportunities (e.g., inspiration) tailored to the distinct creator workflows and key creator concerns (e.g., identity dilution) for collaborating with AI.

Image Generation Tool Tailored for Visual Designers to Maintain Game IP Consistency

Project Lead, NEOWIZ

Developed an image generation tool to support IP-consistent art creation across game studios.
Conducted formative interviews with concept artists from eight in-house studios to analyze their needs.
Built web-based tools using customized ComfyUI pipelines optimized for each studio’s workflow.

A Multi-Character Dialogue System for Interactive Game Storytelling

Project Lead, NEOWIZ

Developed an interactive multi-character dialogue system that allows users to create evolving stories through dynamic conversations.
Designed an architecture that integrates each character’s unique persona and long-term memory into the dialogue flow.
Implemented an algorithm that determines the most likely responding agent based on user input, ensuring coherent and context-aware interactions.

Personalizing Game Spaces Based on Individual Visual-Motor Skills

Master Thesis, Paper, Book

Developed a novel difficulty balancing technique that personalizes game design from the start, based on user-skill and mental models.
This technique adjusts game parameters by using a player’s pre-measured visual-motor skill (MTA model) to reach a target error rate.
Conducted a comparative user study across three distinct games (SMG) (personal vs. average design), demonstrating personalization achieved designer-intended performance but yielded unexpected player experience results.