publications

2026

1. 

   Living Contracts: Beyond Document-Centric Interaction with Legal Agreements

   Ziheng Huang, Robin Kar, Hari Sundaram, and Tal August

   In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, , 2026

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   User interaction with legal contracts has been limited to document reading, which is often complicated by complex, ambiguous legal language. We explore possible futures where contract interfaces go beyond single document interfaces to (1) educate users with legal rights not stated in the contract, (2) transform legal language into alternative representations to aid information tasks before, during, and after signing, and (3) proactively supply contractual information at relevant moments. We refer to these future interfaces collectively as Living Contracts. Using residential leases as a case study, we created three design probes representing different possible Living Contracts. A three-part qualitative study (N=18) revealed participants’ barriers to interacting with contracts, including interpreting complex language, uncertainty about legal rights, and the pressure to sign quickly. Participants’ feedback on the probes highlighted how Living Contracts have the potential to address these challenges and open new design opportunities for human-contract interactions beyond document reading.

2. 

   TermSight: Making Service Contracts Approachable

   Ziheng Huang, Tal August, and Hari Sundaram

   In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, , 2026

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   Legal contracts govern much of our society, but their specialized language is difficult for non-experts to read. While AI has enabled simplification of complex language, legal contracts pose unique challenges because of their connection to readers’ values, ambiguity, and legally binding nature. Based on a formative study (N=20) using Terms of Service (ToS) as example contracts to study challenges in contract reading, we developed TermSight, an intelligent reading interface to probe the opportunities and challenges of designing augmentations for legal text. TermSight guides readers to relevant clauses with color-coded plain-language snippets of information and contextualizes ambiguous language with definitions and hypothetical scenarios. Importantly, TermSight’s features always foreground the original, legally-binding contract text (e.g., linking to associated clauses). Our within-subjects study (N=20) demonstrated the opportunities of TermSight in making ToS significantly easier to read and navigate while revealing the challenges of augmenting service contracts such as ToS.

2023

1. 

   Freeform Templates: Combining Freeform Curation with Structured Templates

   Stephen MacNeil, Ziheng Huang, Kenneth Chen, Zijian Ding, Alexander Yu, Kendall Nakai, and Steven P. Dow

   In Proceedings of the 15th Conference on Creativity and Cognition, Virtual Event, USA, 2023

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   Online whiteboards are becoming a popular way to facilitate collaborative design work, providing a free-form environment to curate ideas. However, as templates are increasingly being used to scaffold contributions from non-experts designers, it is crucial to understand their impact on the creative process. In this paper, we present the results from a study with 114 students in a large introductory design course. Our results confirm prior findings that templates benefit students by providing a starting point, a shared process, and the ability to access their own work from previous steps. While prior research has criticized templates for being too rigid, we discovered that using templates within a free-form environment resulted in visual patterns of free-form curation where concepts were spatially organized, clustered, color-coded, and connected using arrows and lines. We introduce the concept of ‘Free-form Templates’ to illustrate how templates and free-form curation can be synergistic.

2. 

   Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents

   Ziheng Huang, Sebastian Gutierrez, Hemanth Kamana, and Stephen Macneil

   In Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, San Francisco, CA, USA, 2023

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   The recent advent of large language models (LLM) has resulted in high-performing conversational agents such as ChatGPT. These agents must remember key information from an ongoing conversation to provide responses that are contextually relevant to the user. However, these agents have limited memory and can be distracted by irrelevant parts of the conversation. While many strategies exist to manage conversational memory, users currently lack affordances for viewing and controlling what the agent remembers, resulting in a poor mental model and conversational breakdowns. In this paper, we present Memory Sandbox, an interactive system and design probe that allows users to manage the conversational memory of LLM-powered agents. By treating memories as data objects that can be viewed, manipulated, recorded, summarized, and shared across conversations, Memory Sandbox provides interaction affordances for users to manage how the agent should ‘see’ the conversation.

3. 

   DesignNet: a knowledge graph representation of the conceptual design space

   Ziheng Huang, and Stephen MacNeil

   In Proceedings of the 15th Conference on Creativity and Cognition, Virtual Event, USA, 2023

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   Designers explore design spaces to iteratively define and refine their problem statements and solutions. Yet, designers often get fixated on specific problems, solutions, or trivial surface details without considering the bigger system and the intricate relationships within. Previous works demonstrated how suggesting relevant design examples can help designers break out of fixation. However, current systems recommend design ideas that are decontextualized from other relevant design information. As a result, designers need to manually maintain key relationships and context during the design process. DesignNet is a system to explicitly model the inter-relationships between design information with a knowledge graph. Future work will investigate whether and how a knowledge graph representation and associated graph algorithms can support computational creativity and guide design exploration.

4. 

   CausalMapper: Challenging designers to think in systems with Causal Maps and Large Language Model

   Ziheng Huang, Kexin Quan, Joel Chan, and Stephen MacNeil

   In Proceedings of the 15th Conference on Creativity and Cognition, Virtual Event, USA, 2023

   Abs DOI

   Professional designers often construct and explore conceptual representations (e.g.: design spaces) to help them reason about complex design situations and consider potential design pitfalls. However, it is often challenging, even for professional designers, to exhaustively consider the many pitfalls that might result from design activity. We present CausalMapper, a mixed-initiative system, that leverages a large language model (LLM) and a causal map representation to teach design students how to reason about the relationships between problems and solutions. Where creativity support tools often focus on ideating creative solutions, our mixed-initiative approach focuses on ideating ecosystems of solutions that holistically address a set of related problems. By leveraging the generative creativity of LLMs, designers are inspired to consider solutions and potential consequences that emerge when solutions are adopted. At the same time, leveraging the designers’ domain knowledge to account for and correct the biases inherent in LLMs. Through a case study, we demonstrate the functionality of this mixed-initiative system. The goal of this demo is to present a creativity support tool that is intended to teach design students to think more systematically by generating ideas that challenge their thinking rather just augmenting their creative potential.

2022

1. 

   Generating Diverse Code Explanations using the GPT-3 Large Language Model

   Stephen MacNeil, Andrew Tran, Dan Mogil, Seth Bernstein, Erin Ross, and Ziheng Huang

   In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2, Lugano and Virtual Event, Switzerland, 2022

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   Good explanations are essential to efficiently learning introductory programming concepts [10]. To provide high-quality explanations at scale, numerous systems automate the process by tracing the execution of code [8, 12], defining terms [9], giving hints [16], and providing error-specific feedback [10, 16]. However, these approaches often require manual effort to configure and only explain a single aspect of a given code segment. Large language models (LLMs) are also changing how students interact with code [7]. For example, Github’s Copilot can generate code for programmers [4], leading researchers to raise concerns about cheating [7]. Instead, our work focuses on LLMs’ potential to support learning by explaining numerous aspects of a given code snippet. This poster features a systematic analysis of the diverse natural language explanations that GPT-3 can generate automatically for a given code snippet. We present a subset of three use cases from our evolving design space of AI Explanations of Code.

2021

1. 

   ProbMap: Automatically constructing design galleries through feature extraction and semantic clustering

   Stephen MacNeil, Zijian Ding, Kexin Quan, Ziheng Huang, Kenneth Chen, and Steven P. Dow

   In Adjunct Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology, Virtual Event, USA, 2021

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   Making sense of large unstructured problem spaces is cognitively demanding. Structure can help, but adding structure to a problem space also takes significant effort. ProbMap is a novel application for automatically constructing a design gallery from unstructured text input. Given a list of problem statements, ProbMap extracts and semantically groups the stakeholders to construct hierarchical search facets which enables designers to more efficiently navigate the problem statements. We contribute a novel feature extraction algorithm using natural language processing and a technique for automatically constructing a design gallery. These stakeholders are grouped semantically by clustering stakeholders with higher pairwise similarity together. Preliminary trials show that these techniques, which mirror traditional design activities like stakeholder identification and affinity mapping, provide an initial structure to a large unstructured problem space. This resulted in similar features that would be extracted by humans and sensible clusters.