Title: The Design of Technology by, and for, Artistic Worlds

Abstract: I think it is a good thing when people make art. How can we help communities make art more often? Creativity Support Tools (CSTs) have made the creation of art (images, music, film) faster and easier than ever. But creation technology often fails to address (or even exacerbates) big issues threatening creative communities today. Communities share art online at the mercy of mysterious media recommendation algorithms, on platforms crowded with slop, at the risk of nonconsensual reuse (i.e., for AI training data.) How can we reimagine technology’s role in the face of these wicked artistic support problems? I’ll present Creativity Supportive Ecosystems, a framework for understanding how creation systems interdepend on systems of distribution, reception, and provenance. I’ll discuss how this ecosystemic lens grounds my perspective on designing creativity supportive infrastructure, and share Artographer, a system we built to explore spatial maps as a medium for art curation and exploration. Throughout, I’ll try to emphasize an aggressive optimism that–not only can we use data and “”AI”” technologies to support creativity & culture in awesome, powerful ways… we can (and should) do so while moving smaller, slower, with intention, and with care. 

Bio: Shm Garanganao Almeda (https://shmuh.co/) is a creativity support researcher, visual + performance artist, and computer scientist. Shm is currently a Postdoctoral Fellow at the Smithsonian, using data visualization + an unhinged passion for underappreciated historical art to imagine new encounters with cultural collections. They’re also currently driving across the country from SF to NYC! Over the past 7 years, Shm has been spotted around the Bay Area researching HCI+AI in the UC Berkeley EECS PhD program, at Adobe Research, at the Midjourney Storytelling Lab, and in other strange environments. Further sightings have caught them teaching Creative Programming & Electronics at the Jacobs Design Institute, using computer vision to push new paradigms of puppetry as a Stochastic Labs resident, and pushing their two rabbits around in a pet stroller. 

Title:
Future-Proof Developers: Optimizing and Training for Human-AI Collaboration in Programming

Abstract:
Large language models (LLMs) are redefining what it means to program and broadening who can develop software apps or analyze complicated data, from CS experts to non-programmers, without writing much code. However, there is a gap between this vision and reality: people benefit unequally from LLMs, and AI’s impact depends not only on algorithmic power but on how humans collaborate with it. How can we enable everyone, even the non-experts, to program productively with AI and solve complex tasks? What skills should we teach humans to effectively program with AI, and how should we best prepare humans for the AI era? Qianou(Christina) Ma from CMU will share her work that explores these questions for the future of programming with AI.

Bio:
Qianou (Christina) Ma is a PhD student at Carnegie Mellon University Human-Computer Interaction Institute, specializing in Human-AI interaction, GenAI, and Learning Sciences, and co-advised by Sherry Wu and Ken Koedinger. She focuses on designing, building, and evaluating LLM applications to help humans adapt and thrive in AI-infused development environments and optimize human‑AI collaboration. For example, she develops AI-based interactive learning that significantly improves end users’ skills to write prompt programs. Her work has been published across top research venues such as AIED, CHI, and ACL, and has been recognized by industry and media such as The New York Times. Her research has earned distinctions such as Best Paper at AIED and has been supported by NSF, Gates Foundation, and Google Academic Research Award.

Title: Designing for Algorithmic Autonomy: Lessons from Children and Families in a Datafied World

Abstract: In this talk, I will focus on the concept of algorithmic autonomy—how we can support users in understanding and gaining greater autonomy when interacting with algorithmic systems. I will illustrate this through a multi-year HCI project on children and online datafication. Children are a particularly important group to study because they are growing up in digital environments where algorithmic systems shape much of their everyday experiences, often in ways that are opaque and beyond their control. Datafication—the transformation of children’s activities, preferences, and interactions into data—raises pressing questions about privacy, agency, and how young people learn to navigate algorithmically mediated environments. This series of studies has engaged more than 500 children and their parents, demonstrating how a typical HCI research trajectory unfolds: from establishing ground truth, to conducting co-design, to developing systems, and ultimately to deployment. Together, these efforts highlight both the challenges and opportunities in designing for user autonomy in an era of pervasive algorithmic decision-making.

Bio: Ge “Tiffany” Wang is an Assistant Professor of Computer Science at the Siebel School of Computing and Data Science. Her research lies at the intersection of human-computer interaction (HCI), human-centered artificial intelligence (HAI), and usable security and privacy. As AI becomes increasingly embedded in everyday smart devices, these systems have shifted from passive tools to active agents that analyze personal data, infer characteristics, and make consequential decisions. Wang’s work explores how AI can be designed to be more autonomy-supportive, enabling individuals to better understand, challenge, and assert agency over the algorithmic decisions that shape their digital lives.

Title: Get to Know the Faculty Panel

Abstract: A selection of faculty from UIUC’s HCI community will share brief summaries of their research and labs, then take questions from the audience.

Speakers:

Camille Cobb, Tal August, Sarah Sterman

Speaker: Haoqi Zhang

Title: Computational Ecosystems: Tech-enabled Communities to Advance Human Values

Abstract: Despite decades of computing advances, some human problems and core human values have remained difficult to solve or promote at scale. Instead of advancing individual technologies, I focus our attention on making major leaps in system-level thinking and orchestration. Specifically, I describe our efforts to design, build, and study computational ecosystems that interweave community process, social structures, and intelligent systems to form new, integrative solutions. Computational ecosystems emphasize (1) computational thinking to decompose and distribute problem solving to diverse people or machines most able to address them; and (2) ecological thinking to create sustainable processes and interactions that foreground the value of human engagement. 

In the first half of my talk, I will share examples of computational ecosystems designed to advance community-based planning and research training, that respectively engages thousands of people in planning an event and empowers a single faculty member to provide authentic research training to 20+ students. The second half of my talk will share our more recent efforts to support human practices and human experiences computationally. I will close with a few thoughts on why we need computational ecosystems, especially if we intend to not only meet our consequentialist aims but also deepen our engagement in intrinsically valuable human activities. 

Bio: Haoqi Zhang is an associate professor in Computer Science and Design at Northwestern University. His work advances the design of integrated socio-technical models that solve complex problems and advance human values. His research work bridges across Computer Science, Design, Learning Science, Psychology, and Philosophy, and is generously supported by the National Science Foundation, the Buffett Institute of Global Affairs, and the Center for Advancing Safety in Machine Intelligence.

Haoqi received his PhD in Computer Science and BA in Computer Science and Economics from Harvard University. At Northwestern he founded and directs the Design, Technology, and Research (DTR) program, which provides an original model for learning and growing through research for over 170 students (read the DTR annual letters, available at dtr.northwestern.edu/letters; and watch the DTR documentary, Forward, at http://forward.movie). With Matt Easterday, Liz Gerber, and Nell O’Rourke, Haoqi co-directs the Delta Lab, an interdisciplinary research lab and design studio across computer science, learning science, and design.

Speaker: Andrew Chen

Abstract: Design is an iterative and collaborative process, yet existing version control and documentation tools struggle to capture the reasoning behind design decisions. While traditional versioning systems record changes, they often fail to make design intent explicit, leading to gaps in communication and misalignment in collaborative workflows. Hence, we explored the challenges of documenting creative design processes and examined how process-aware versioning can enhance communicative collaboration. Insights from this exploration inform the development of a prototype system, created through an iterative, research-through-design approach. In this talk, we present the journey towards the creation of a process-aware version control prototype to support the documentation of creative intent, while we also discuss the design implications for creating documentation systems in collaborative design workflows.

Bio: Andrew Chen is a final year MSCS student at the University of Illinois Urbana-Champaign, advised by Prof. Sarah Sterman in the Process, Interaction, and Creativity Lab. As both a designer and computer science researcher, his work focuses on understanding design processes while developing both digital and tangible tools for improving collaborative design workflows. He has received multiple awards in the design scene as well as top conferences in HCI – earning the Best Demo Award at ACM UbiComp/ISWC ’22, honored as a Mark Winner at the Golden Pin Design Awards in 2019, named finalist in the Red Dot Design Awards in both 2019 and 2020, and listed as one of the Top 100 Emerging Designers in the Asian Next Generation Design Exhibition in 2021.

Speaker: Mina Lee

Title: Writing with AI: Capturing Its Influence, Designing Its Future

Abstract: AI is changing how we write—not just the process itself, but also the content we produce and our identities as writers. In this talk, I approach these changes from three distinct angles. First, measuring AI’s impact on writing: I introduce CoAuthor, a platform that records keystroke-level human-AI interactions, allowing us to analyze AI’s effects on language, ideation, collaboration, and beyond. Second, understanding and designing AI writing assistants: I present a design space derived from a systematic review of over 100 AI writing assistants, highlighting potential trade-offs, alternative design choices, and gaps in current research. Finally, the evolving societal norms and expectations around AI in writing: I will share ongoing projects and invite an open discussion on the future of writing with AI.

Bio: Mina Lee is an Assistant Professor in the Computer Science and Data Science Institute at the University of Chicago. Previously, she was a postdoctoral researcher at Microsoft Research and received her Ph.D. in Computer Science from Stanford University. Her research focuses on Writing with AI, particularly how AI is transforming our writing process, the content we produce, and our identities as writers. She has co-founded and organized workshops on Intelligent and Interactive Writing Assistants (In2Writing), Human-centered Evaluation and Auditing of Language Models (HEAL), and , and Tools for Thought: Research and Design for Understanding, Protecting, and Augmenting Human Cognition with Generative AI at ACL 2022 and CHI 2023-2025. Named one of MIT Technology Review’s Korean Innovators under 35 in 2022, her work has been published in generalist journals (e.g., Nature Human Behavior) as well as top-tier conferences in NLP (e.g., ACL and NAACL), HCI (e.g., CHI), and machine learning (e.g., NeurIPS). Her research on human-AI collaborative writing received an Honorable Mention Award at CHI 2022 and was featured in various media outlets, including The Economist.

Speakers: Mehmet Arif Demirtas and Yoshee Jain

Title: PLAID: Supporting Computing Instructors to Identify Domain-Specific Programming Plans at Scale

Abstract: Pedagogical approaches focusing on stereotypical code solutions, known as programming plans, can increase problem-solving ability and motivate diverse learners. However, plan-focused pedagogies are rarely used beyond introductory programming. Our formative study (N=10 educators) showed that identifying plans is a tedious process. To advance plan-focused pedagogies in application-focused domains, we created an LLM-powered pipeline that automates the effortful parts of educators’ plan identification process by providing use-case-driven program examples and candidate plans. In design workshops (N=7 educators), we identified design goals to maximize instructors’ efficiency in plan identification by optimizing interaction with this LLM-generated content. Our resulting tool, PLAID, enables instructors to access a corpus of relevant programs to inspire plan identification, compare code snippets to assist plan refinement, and facilitates them in structuring code snippets into plans. We evaluated PLAID in a within-subjects user study (N=12 educators) and found that PLAID led to lower cognitive demand and increased productivity compared to the state-of-the-art. Educators found PLAID beneficial for generating instructional material. Thus, our findings suggest that human-in-the-loop approaches hold promise for supporting plan-focused pedagogies at scale.

Bios:

Mehmet Arif Demirtas (he/him) is a PhD student in the Siebel School of Computing and Data Science at UIUC, advised by Dr Katie Cunningham. Arif is interested in improving computer science education by modeling student learning through data-driven methods, supporting instructors in creating educational material for diverse domains, and developing learning environments for non-CS majors.

Yoshee Jain (she/her) is a junior in Computer Science at UIUC, working with Dr. Katie Cunningham. Yoshee is interested in developing AI-driven, human-in-the-loop systems to support instructors, enhance learner experiences, and bridge the gap between computing and other fields by making it more accessible and applicable across diverse domains.

Speaker: Hanxi Fang

Title: Enhancing Computational Notebooks with Code+Data Space Versioning

Abstract:
There is a gap between how people explore data and how Jupyter-like computational notebooks are designed. People explore data nonlinearly, using execution undos, branching, and/or complete reverts, whereas notebooks are designed for sequential exploration. Recent works like ForkIt are still insufficient to support these multiple modes of nonlinear exploration in a unified way. In this work, we address the challenge by introducing two-dimensional code+data space versioning for computational notebooks and verifying its effectiveness using our prototype system, Kishuboard, which integrates with Jupyter. By adjusting code and data knobs, users of Kishuboard can intuitively manage the state of computational notebooks flexibly, thereby achieving both execution rollbacks and checkouts across complex multi-branch exploration history. Moreover, this two-dimensional versioning mechanism can easily be presented along with a friendly one-dimensional history. Human subject studies indicate that Kishuboard significantly enhances user productivity in various data science tasks.

Bio:
Hanxi Fang is a second-year M.S. student in Computer Science at the University of Illinois Urbana-Champaign advised by Prof. Yongjoo Park. Her research interest is mostly in database systems and systems for data science. During her master’s program, she mostly works on version control systems for data science and optimizing systems for efficient large language model (LLM) inference. Before her graduate studies, she worked as an undergraduate research assistant at the Database and Big Data Analysis Lab at Zhejiang University in China, where she worked in spatiotemporal databases.

Speaker: Halil Kilicoglu

Title: Enhancing Rigor and Integrity of Biomedical Research using Natural Language Processing

Abstract:  There has been much debate about rigor, transparency, and reproducibility of biomedical research in recent years. Ongoing efforts aim to address issues in research conduct and reporting by developing standards, guidelines, and recommendations. As biomedical research output increases exponentially, automated tools are needed to complement such efforts and assist the stakeholders (e.g., researchers, journals, peer reviewers, funders, policymakers) in assessing and improving research output efficiently. In this talk, I will first motivate the use of NLP methods to address some of the rigor and transparency issues manifested in textual artifacts of biomedical communication (e.g., protocols, manuscripts, publications).  Next, I will discuss two research projects that we have been pursuing in this area: a) evaluating the reporting quality of clinical trial publications, and b) assessing citation integrity in biomedical publications. I will also highlight some of the challenges for NLP in this application domain.

Bio: Dr. Halil Kilicoglu is Associate Professor in the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign. He conducts research in natural language processing (NLP), artificial intelligence/machine learning (AI/ML), and knowledge representation with biomedical applications. He uses a combination of data-driven analytical techniques and knowledge-based semantic approaches to extract and organize knowledge buried in textual artifacts to benefit biomedical discovery and scholarship, and improve healthcare outcomes. His recent work includes development of automated methods to assess research rigor, transparency, and integrity of biomedical publications. Prior to joining the iSchool faculty in 2019, he was a research scientist at the U.S. National Library of Medicine, National Institutes of Health (NLM/NIH), where he led the Semantic Knowledge Representation project. His research has been funded by NIH, AHRQ, and HHS Office of Research Integrity.