GenAICHI 2025

Outline

Please join us for the fourth Generative AI and HCI workshop at CHI 2025. We previously ran workshops in 2022, 2023, 2024.

In the past year, we have seen or made powerful tools that can create images from textual descriptions or conduct reasonably coherent conversations, make writing suggestions for creative writers, and write code as a pair programmer. We have also seen claims of what an historical person “really looked like,” and of a “completed” version of a musical compositions left unfinished by their composer’s untimely death. What all of these examples have in common is that the AI does not simply categorize data and interpret text as determined by models, but instead creates something new—e.g., in images, molecules, or designs. This work moves the potential of AI systems from problem solving to problem finding, and it tends to change the “role” of the AI from decision-maker to human-supporter. Following a successful CHI workshop in 2022, we focus on various aspects of generative AI and its interactions with humans, including

  • new sociotechnical opportunities for work and recreation that are afforded by powerful new interactive capabili- ties
  • novel design challenges of systems that produce a different outcome after each invocation
  • ethical issues related to their design and use; and
  • useful patterns for collaboration between humans and generative AI in different domains

Generative AI can be defined as an AI system that uses existing media to create new, plausible media. This scope is broad, and the generative potential of AI systems varies greatly. Over the last decade, we have seen a shift in methodology moving from expert systems based on patterns and heavy human curating towards stochastic and generative models such as Generative Adversarial Networks (GANs) that use big data to produce convincingly human-like results in various domains, and Large Language Models (LLMs) that can generate text, source code, and images from simple instructions (“prompts”).

Full-text of Workshop proposal (GenAICHI 2025) GenAICHI

Past Workshop Proceedings

The Generative AI and HCI workshop has been running since 2022. Here you can find links to past workshop proceedings including calls for papers, programs, and accepted workshop paper.

Societal Implications

Lucas Anastasiou (The Open University): BCause: Human-AI collaboration to improve hybrid mapping and ideation in argumentation-grounded deliberation [pdf]</li>

Anna Neumann (Research Center Trustworthy Data Science and Security), Jat Singh (Research Center Trustworthy Data Science and Security & University of Cambridge): Between Threat and Tool: When Users Are Asked To Design Their Competitors [pdf]

René Schäfer (RWTH Aachen University), Sarah Sahabi (RWTH Aachen University) , Paul Preuschoff (RWTH Aachen University), Jan Borchers (RWTH Aachen University): Leveraging Digital Accessibility Using Generative AI [pdf]

Caterina Maidhof (Universitat Polytecnica de Valencia), Elena del Val (VRAIN, Universitat Politècnica de València), Jose Such (King`s College London): Privacy Risks for Underaged User Groups in LLM-based Conversational Agent Interactions [pdf]

Creativity - Abstract

Pauline Leininger (University of Television and Film Munich), Christoph Johannes Weber (University of Television and Film Munich): Democratizing Creative Participation and Collaboration in Virtual Film Productions with AI Visualizations [pdf]

Meredith Young-Ng (University of California, Davis), Jingxian Liao (University of California, Davis), Qingxiaoyang Zhu (University of California, Davis), Hao-Chuan Wang (University of California, Davis): Designing Support for Human-AI Idea Selection: Human Agency and AI Autonomy [pdf]

Atsuya Kobayashi (Sony Group Corporation), Masahiro Yoshida (Sony Group Corporation), Kazuki Kawamura (Sony Group Corporation), Kei Tateno (Sony Group Corporation): Designing the Interactive transition of Abstraction and Concretization in Creative Ideation: A Case Study on Lyric Composition

Creativity - Visual

DaEun Choi (KAIST), Kihoon Son (KAIST), HyunJoon Jung (Adobe), Juho Kim (KAIST): Expandora: Broadening Design Exploration with Text-to-Image Model [pdf]

Yenkai Huang (Dartmouth College), Zheng Ning (University of Notre Dame), Yenkai Huang (Dartmouth College): LACE: Controlled Image Prompting and Iterative Refinement with GenAI for Professional Visual Art Creators [pdf]

Kazuki Kawamura (The University of Tokyo), Jun Rekimoto (SonyCSL): SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills [pdf]

Kay Jingyan Zeng (Splunk), Zifan Zhang (Cherry Technologies), Huiran Yi (University of Michigan), Lu Xian (University of Michigan): Understanding Designers’ Perceptions of the Usefulness of Generative AI Tools in Creative Design Processes [pdf]

Role of AI

Auren Liu (Fluid Interfaces), Pattie Maes (Fluid Interfaces, MIT Media Lab): AI-Mediated Character Development to Explore Identity as an Intervention for Loneliness [pdf]

Long Ling (Tongji University): Brain Cache: Generative AI as a Cognitive Exoskeleton for Externalizing, Structuring, and Activating Knowledge [pdf]

Takuya Sera (NEC Corporation), Yusuke Hamano (NEC Corporation): ChatNekoHacker: Real-Time Fan Engagement with Conversational Agents [pdf]

Role of AI - Tool or Partner?

Ilya Zakharov (JetBrains), Ekaterina Koshchenko (JetBrains), Agnia Sergeyuk (JetBrains): From Teacher to Colleague: How Coding Experience Shapes Developer Perceptions of AI Tools [pdf]

Rendi Chevi (MBZUAI), Alham Aji (MBZUAI), Thamar Solorio (MBZUAI), Kentaro Inui (MBZUAI): How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction [pdf]

Jaehoo Bae (Seoul National University), Jane Lee (Seoul National University), Eunseo Ryu (Seoul National University), Honghua Lyu (Seoul National University), Dain Kim (Seoul National University), Myung Hwan Yun (Seoul National University): When Do Humans See AI as Humanlike? The Role of Perceived Performance in AI Anthropomorphism [pdf]

Applications

Hirokazu Miyachi (Sekisuihouse Ltd), Naoko Yano (Sekisuihouse Ltd.), Momoyo Yadani (Sekisuihouse Ltd.), Yuya Hasegawa (Sekisuihouse Ltd.), Takahiro Suezawa (Sekisuihouse Ltd.), Akiko Taniguchi (Sekisuihouse Ltd.), Yumiko Ikeda (Sekisuihouse Ltd.), Issei Watanabe (Sekisuihouse Ltd.), Takahiro Yokoi (Sekisuihouse Ltd.), Yousuke Motohashi (NEC Corporation), Junichi Nakano (NEC Corporation ), Tomoki Tanaka (NEC Corporation), Miki Tanaka (NEC Corporation): A Case Study of the Development of a Sensitivity-Based Interactive House Design Assistance System Using Generative AI [pdf]

Rana Talukdar (Vellore Institute Of Technology, Bhopal), Bindupautra Jyotibrat (VIT Bhopal), Arunim Gogoi (VIT Bhopal), Ankur Jain (VIT Bhopal): A State-of-Art Survey on Generative AI Techniques for Floor Planning [pdf]

Sunil Sharma (Capital One): Leveraging Generative Adversarial Networks for Unsupervised Fraud Detection [pdf]

Applications - Education and Related

Roberto Theron (Universidad de Salamanca), Andrea Vázquez-Ingelmo (Universidad de Salamanca), Alicia García-Holgado (Universidad de Salamanca), Francisco José García-Peñalvo (Universidad de Salamanca), Nastaran Shoeibi (Universidad de Salamanca): A Technology-Mediated Approach to Addressing Reading Diversity in German Classrooms [pdf]

Varshiga P S (M Kumarasamy College of Engineering), Selvi A (M Kumarasamy College of Engineering), Sarathi S (M Kumarasamy College of Engineering), Bharath Priyan S (M Kumarasamy College of Engineering): EduAcademia: A Comprehensive Integrated Platform for Enhanced Learning [pdf]

Valerie Chen (Carnegie Mellon University), Alan Zhu (Carnegie Mellon University), Sebastian Zhao (UC Berkeley), Hussein Mozannar (Microsoft Research), David Sontag (MIT), Ameet Talwalkar (Carnegie Mellon University): Need Help? Designing Proactive AI Assistants for Programming [pdf]

Formal Approaches

Mingyue Yuan (university of the new south wales): A Case Study of Scalable Content Annotation Using Multi-LLM Consensus and Human Review [pdf]

Elakkiya Daivam (Capital One): Probabilistic Deep Learning for Energy Time Series Forecasting: A Comparative Study [pdf]

Title+Authors (without formal presentation)

Dmitri Goldenberg (Booking.com), Yulia Goldenberg (Ben Gurion University): Agent Experience: Nielsen’s Usability Heuristics Analysis for GenAI Agents [pdf]

Farnaz Asrari (Indiana University), L. Jean Camp (Indiana University): AI-Enhanced Thematic Analysis of COVID-19 Impact: Combining Human Expertise with Generative AI [pdf]

Naoko Hayashida (Fujitsu Limited): Beyond the Winding Path of Learning: Exploring Affective, Cognitive, and Action-Oriented Prompts for Communication Skills [pdf]

Zhuoran Huang (Northeastern University): Designing an LLM AI-Powered Digital Storytelling Assistant for Inclusive and Authentic Health Narratives [pdf]

Jenny Huang (University of Television and Film Munich), Fangli Lu (LMU Munich), Christoph Johannes Weber (University of Television and Film Munich, LMU Munich): Exploring User Preferences for Seamless Scene Text Translation in Video [pdf]

Rayna Ney (ETH Zurich): Generative AI for Wellness Applications via User Generated Immersive Virtual Environments [pdf]

Niklas Pfützenreuter (Universität Duisburg-Essen): Investigating Implicit Support for Image Generation Processes [pdf]

Nicholas Wang (Stellar Learning Technologies): Leveraging Interactive Generative AI for Enhancing Intuitive Learning [pdf]

Alexander Htet Kyaw (Massachusetts Institute of Technology), Miana Smith (Massachusetts Institute of Technology), Se Hwan Jeon (Massachusetts Institute of Technology), Neil Gershenfeld (Massachusetts Institute of Technology): Making Physical Objects with Generative AI and Robotic Assembly: Considering Fabrication Constraints, Sustainability, Time, Functionality and Accessibility [pdf]

Bekzat Tilekbay (KAIST), Saetbyeol LeeYouk (Sogang University), Alex Suryapranata (KAIST), Saelyne Yang (KAIST), Juho Kim (KAIST): Supporting AI-assisted Task Learning with Hierarchical Representation of Procedural Knowledge [pdf]

Presentation and Attendence Details

This hybrid workshop will involve participants at their own locations around the world and in the CHI conference venue in Yokohama.

Topics and Themes

Our workshop is open to diverse interpretations of interactive generative AI, characterized by the AI systems’ abilities to make new things, learn new things and foster serendipity and emergence. We are interested in several dimensions of generative AI, including mixed initiative, human–computer collaboration, or human–computer competition, with the main focus on interaction between humans and generative AI agents. We welcome researchers from various disciplines, inviting researchers from different creative domains including, but not limited to art, images, music, text, style transfer, text-to-image, programming, architecture, design, fashion and movement. Because of the far-reaching implications of Generative AI, we propose the following list of non-exhaustive, thematic questions to guide our discussions at the workshop:

  • What is generative AI and how can we leverage diverse definitions of it? Does generative AI go beyond intelligent interaction? What distinguishes generative AI?
  • How do you design in this characteristically uncertain space? What design patterns do we need to think about? How does uncertainty play into this and how to we help people set expectations to designing with AI?
  • Do generative AI algorithms contribute needed serendipity to the design process—or simply randomness—or worse, chaos?
  • Is presenting AI as a desirable and “objective” method appropriate for generative AI?

We encourage people to write and answer their own questions as well. We hope that the workshop leads to new ways-of-thinking.

These themes can be addressed within the following topics:

  • The emerging capabilities of generative AI.
  • Generative AI existence in different domains including (but not limited to) images, music, text, design, and motion.
  • The role of generative AI in assisting, replacing, and regimenting human work.
  • Human-AI collaboration and co-creative systems.
  • Ethical issues including misuses and abuses, provenance, copyright, bias, and diversity.
  • The uncanny valley in Human-AI interactions.
  • Speculative futures of generative AI and its implications for human-AI utopias and dystopias.

As above, we encourage people to add new topics and domains.

Contributing Your Work

Submissions may be up to 4 pages long (references may appear on additional pages), following the using the ACM Primary Article Template.

The deadline for submissions and submission website is found at the top of the page.

Please send any comments or questions to Michael Muller, michael_muller@us.ibm.com.

Accepted papers will be presented in the workshop and authors can choose to publish their paper here on the workshop website under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Program Committee

  • Mohit Agrawal, Wealthfront
  • Tarun Eldho Alias, Neem Inc
  • Andrew A Anderson, IBM Research
  • Jaehoo Bae, Seoul National University
  • Minsik Choi, The Australian National University
  • Lorenzo Corti, Delft University of Technology
  • Jordan Aiko Deja, De La Salle University
  • Hyo Jin Do, IBM Research
  • Werner Geyer, IBM Research
  • Shabnam Hakimi, Toyota Research Institute
  • Jessica He, IBM Research
  • Michael Hind, IBM Research
  • Jussi Holopainen, City University of Hong Kong
  • Balaji Shesharao Ingole, Gainwell Technologies LLC
  • Anthony Jameson, Contaction AG
  • Bharath Kumar Reddy Janumpally, Paypal
  • Anna-Kaisa Kaila, KTH Royal Institute of Technology, Stockholm
  • Anna Kantosalo, University of Helsinki
  • Siya Kunde, IBM Research
  • Manoj Varma Lakhamraju, CVS Health
  • Ishaani M, Independent
  • Mary Lou Maher, Univeristy of North Carolina
  • Charles Patrick Martin, Australian National University
  • Jacquelyn Martino, IBM
  • Abhinav Mehta, Amazon.com Services LLC
  • Mohit Menghnani, Twilio
  • Michael Muller, IBM
  • Anusha Musunuri, Snap Inc
  • Viktoria Pammer-Schindler, Graz University of Technology
  • Isan Sahoo, Oracle
  • Briane Paul Samson, De La Salle University
  • Muthu Selvam, University of North Carolina at Charlotte
  • Manas Srivastava, Google
  • Alexa Steinbrueck, Hochschule fur Gestaltung Schwabisch-Gmund
  • Jayant Tyagi, Salesforce
  • Mukund Milind Wagh, Amazon
  • Greg Walsh, University of Baltimore
  • Justin Weisz, IBM Research AI
  • Priya Yesare, Asurion

Organizers

Lydia B. Chilton is an Assistant Professor in the Computer Science Department at Columbia University. Her research shows how AI can augment human problem-solving, innovation, and creativity. She has co-organized 8 workshops on Human-centered AI at CHI, UIST, and IUI.

Minsik Choi is a PhD researcher in computing at the Australian National University at the intersections of sound, music, and HCI. He studies how sound design tools can be enhanced with generative AI to incorporate musical knowledge.

Anna Kantosalo is a Postdoctoral Researcher at the University of Helsinki. The focus of her research is Human–Computer Co-Creativity and she is defining models and methods for building and describing systems in which humans and autonomous creative agents can work together. She has chaired the Future of Co-Creative Systems workshop adjoined with the International Conference on Computational Creativity twice.

Mary Lou Maher is a Professor in the Software and Information Systems Department at the University of North Carolina at Charlotte. Her early research in AI-based generative design has lead to a human centered approach to computational creativity and co-creative systems. She has Chaired the Creativity and Cognition Conference (2019) and the International Conference on Computational Creativity (2019) as well as organized several workshops on AI-based design and creativity.

Charles Patrick Martin is a Lecturer in Computer Science at the Australian National University. Charles works at the intersection of music, AI/ML and HCI. He studies how humans can interact creatively with intelligent computing systems and how such systems might fit in the real world. Charles has organised multiple generative-AI-focused workshops at the New Interfaces for Musical Expression conference.

Michael Muller works as a Senior Research Scientist at IBM Research in Cambridge MA USA. With colleagues, he has analyzed how domain experts make use of generative AI outcomes, and how humans intervene between “the data” and “the model” as aspects of responsible and accountable data science work. His research occurs in a hybrid space of Human-Centered AI (HCAI), Human-Computer Interaction (HCI), design, and social justice.

Greg Walsh is an associate professor at the University of Baltimore where he teaches courses in Design. He is an interaction design researcher who focuses on user-centered, inclusive design for children and adults. His work seeks to include more voices in the design process and has been a recipient of a prestigious Google Faculty Research Award. His work has included participatory design sessions in Baltimore City libraries and is now exploring the use of generative AI as a co-design partner.