Exploring AI Problem Formulation with Children via Teachable Machines
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866911786223009792 |
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| author | Dwivedi, Utkarsh Elsayed-Ali, Salma Bonsignore, Elizabeth Kacorri, Hernisa |
| author_facet | Dwivedi, Utkarsh Elsayed-Ali, Salma Bonsignore, Elizabeth Kacorri, Hernisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18688 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring AI Problem Formulation with Children via Teachable Machines Dwivedi, Utkarsh Elsayed-Ali, Salma Bonsignore, Elizabeth Kacorri, Hernisa Human-Computer Interaction 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. |
| title | Exploring AI Problem Formulation with Children via Teachable Machines |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.18688 |