Educators are often described as practitioners and can be subject to deficit-oriented characterizations that position their work as focused on the passive dissemination of knowledge. This pictorial argues that educators are designers, and their curation of learning environments and experiences constitutes an underappreciated and complex design practice. Further, the design work that educators engage in is significant and consequential as it can define or reimagine who participates and what is valued in educational spaces – playing an important role in creating more equitable educational outcomes. In this pictorial, we leverage photos captured and curated by educators of their learning environments in library makerspaces and youth-serving technology centers to make their unseen design work and impact visible. Beyond making educators’ expertise more visible, this pictorial also offers design considerations for designers of technologies, materials, and experiences that may be situated in educational environments.

We leverage the visual format and affordances of pictorials to make visible and call attention to the unseen design work of educators and designers of creative learning experiences in making and tinkering spaces. We document the process of design and iteration that took place as researchers and educators co-designed a learning experience focused on the intersection of artmaking, circuitry, and computing for participants in a public library makerspace. We present this visual case study as provocations for interaction designers, educators, and researchers who aim to engage people in creative learning experiences with technologies.

Machine learning (ML) technologies are ubiquitous and increasingly influential in daily life. They are powerful tools people can use to build creative, personalized systems in a wide variety of contexts. We believe ML has vast potential for young people to use to make creative projects, especially when used in conjunction with programming. This potential is understudied. We know little about what projects youth might create, or what computational practices they could engage in while building them. We combined a beginner-level ML modeling toolkit with a beginning programming tool and then investigated how young people created and remixed projects to incorporate custom ML-based gestural inputs. We found that (1) participants were able to build and integrate ML models of their own gestures into programming projects; (2) the design of their gestures ranged from coherent to disjoint with respect to the narratives, characters, and actions of their interactive worlds; and (3) they tested their projects by assessing the programmed vs. modeled aspects of them as distinct units. We conclude with a discussion of how we might support youth in combining code and ML modeling going forward.