Intentmaking and Sensemaking: Human Interaction with AI-Guided Mathematical Discovery
For designers of AI tools for scientific discovery, this work highlights the need to support iterative goal refinement rather than just question-answering, though the findings are based on a small user study with a specific tool.
The study identifies and characterizes 'intentmaking', a workflow where mathematicians iteratively define and refine experimental goals through interaction with an AI coding agent, extending the concept of sensemaking. This cycle of intentmaking and sensemaking was observed in 11 expert mathematicians using AlphaEvolve.
Artificial intelligence offers powerful new tools for scientific discovery, but the interaction paradigms required to effectively harness these systems remain underexplored. In this paper, we present findings from a formative user study with 11 expert mathematicians who used AlphaEvolve, an evolutionary coding agent, to tackle advanced problems in their fields of expertise. We identify and characterize a distinct workflow we term intentmaking, the iterative process of discovering, defining, and refining one's experimental goals through active system interaction. We frame this as a natural extension to sensemaking, the cognitive process of building an understanding of complex or novel data. We suggest that users enter a cycle of intentmaking (defining and updating their experiment) and sensemaking (interpreting the results) which repeats many times during the course of an investigation. Our documentation of these themes suggests an approach to designing AI tools for scientific discovery that goes beyond the existing question/answer model of many current systems, treating them as collaborative instruments rather than opaque black-box assistants.