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.

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.

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.

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.

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.