A Generative Framework for Designing Interactions to Overcome the Gaps between Humans and Imperfect AIs Instead of Improving the Accuracy of the AIs

Yakura, Hiromu

doi:10.1145/3544549.3577036

Cited by 2 publications

References 32 publications

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Smart "Error"! Exploring Imperfect AI to Support Creative Ideation

Liu,

Lv,

Cui

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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Designers widely accept AI as a partner in the design process for its efficient and intelligent decision-making. However, AI is often not perfect, and AI error often makes humans dumbfounded. Literature has pointed out the value of such AI error, while still leaving its inspiration essence and application strategies uncharted from the practice perspective. This work focuses on bridging the practice gap by looking into and exploiting the imaginative "mislabeled" objects of object detection models. To gain insights into the inspiration of AI "error", we collected a dedicated AI "error" dataset from object detection and invited eight designers to share divergent comments on the "mislabeled" objects. Coding was then performed on the comments, which summarizes the inspiration of AI "error" into six atomic dimensions. Subsequently, we took a step further to an exploratory study, a comparative ideation experiment with 20 designers, investigating how to apply these inspiration dimensions to create ideas. Questionnaire and interview results revealed that essential inspiration of AI "error" could positively activate creativity, especially the "Outline" dimension. A design model CETR is then formulated by summarizing the application of atomic inspiration of "error" into four forms of creativity, which could be taken as a guideline for cooperative design with AI "error". In addition, we also sketch two approaches to generate more inspiring and applicable AI "error", elaborate on two principal characteristics of AI "error" for promoting creativity, and propose three strategies for better co-creating with AI "error". Finally, we provide insight into design research about AI self-awareness and human-AI collaboration.

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Smart "Error"! Exploring Imperfect AI to Support Creative Ideation

Liu,

Lv,

Cui

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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Stepping Stones for Self-Learning

Hai-Jew

2023

Generative AI in Teaching and Learning

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One of the themes in the emergence of text- and image-making (multimodal) generative AIs is their value in the learning space, with the vast potential just beginning to be explored by mass humanity. This chapter explores the potential and early use of large language models (LLMs) harnessed for their mass learning, human-friendly conversations, and their efficacies, for self-learning for individuals and groups, based on a review of the literature, system constraints and affordances, and abductive logic. There are insights shared about longitudinal and lifelong learning and foci on co-evolving processes between the human learner and the computing machines and large language models.

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A Generative Framework for Designing Interactions to Overcome the Gaps between Humans and Imperfect AIs Instead of Improving the Accuracy of the AIs

Cited by 2 publications

References 32 publications

Smart "Error"! Exploring Imperfect AI to Support Creative Ideation

Smart "Error"! Exploring Imperfect AI to Support Creative Ideation

Stepping Stones for Self-Learning

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