Elizabeth Bonsignore

Motivated to create a children’s VR game for the Netherlands Institute for Sound and Vision (NISV), this research asks how might we design an immersive game for children to meaningfully interact with media and metadata in cultural heritage contexts? First, during a ‘design salon,’ 13 data and heritage experts challenged children’s ability to interact with metadata. In response, we ran workshops with 19 children focused on understanding abstract media and data. We found that while (1) metadata has many challenges, (2) children understand abstract data when it is grounded in concrete experiences, are (3) motivated to interact with archival media through in immersive and collaborative contexts, and (4) are interested in exploring media diversity through categorisation games with high-level narrative goals. These findings inform our game concept and three core insights for designing immersive experiences for cultural heritage: Considering the Contextual Complexity of Data and Audience Needs, Connecting Data Abstractions to Embodied Narratives Through Categorisation Mechanics, and Supporting Abstract Meaning Making Using the Immersive Affordances of VR.

Against the backdrop of growing screen time, rising mental health issues, increasing loneliness, and general ill effects from technology use, it is time for the CHI community to consider how technology for children can be better than ‘good’. There are many examples of good technologies across research and commercial products, for technology to be more than ‘good’ it needs to have a transformative effect on children’s lives that lasts beyond a monetary positive experience. Such technology could, for example, build resilience, encourage compassion, promote inclusive behaviors, and improve overall happiness. This workshop will explore what better than ‘good’ technology may look like and create a manifesto for the CHI community to support Transformative Technologies for children in our work.

Emphasizing problem formulation in AI literacy activities with children is vital, yet we lack empirical studies on their structure and affordances. We propose that participatory design involving teachable machines facilitates problem formulation activities. To test this, we integrated problem reduction heuristics into storyboarding and invited a university-based intergenerational design team of 10 children (ages 8-13) and 9 adults to co-design a teachable machine. We find that children draw from personal experiences when formulating AI problems; they assume voice and video capabilities, explore diverse machine learning approaches, and plan for error handling. Their ideas promote human involvement in AI, though some are drawn to more autonomous systems. Their designs prioritize values like capability, logic, helpfulness, responsibility, and obedience, and a preference for a comfortable life, family security, inner harmony, and excitement as end-states. We conclude by discussing how these results can inform the design of future participatory AI activities.