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Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow

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arxiv 2402.09894 v2 pith:47PU2W53 submitted 2024-02-15 cs.HC cs.AIcs.CLcs.CY

classification cs.HCcs.AIcs.CLcs.CY
keywords generativeusersfamiliarizationnoveltythereutilityworkflowworkflows
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative AI brings novel and impressive abilities to help people in everyday tasks. There are many AI workflows that solve real and complex problems by chaining AI outputs together with human interaction. Although there is an undeniable lure of AI, it is uncertain how useful generative AI workflows are after the novelty wears off. Additionally, workflows built with generative AI have the potential to be easily customized to fit users' individual needs, but do users take advantage of this? We conducted a three-week longitudinal study with 12 users to understand the familiarization and customization of generative AI tools for science communication. Our study revealed that there exists a familiarization phase, during which users were exploring the novel capabilities of the workflow and discovering which aspects they found useful. After this phase, users understood the workflow and were able to anticipate the outputs. Surprisingly, after familiarization the perceived utility of the system was rated higher than before, indicating that the perceived utility of AI is not just a novelty effect. The increase in benefits mainly comes from end-users' ability to customize prompts, and thus potentially appropriate the system to their own needs. This points to a future where generative AI systems can allow us to design for appropriation.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AI-supported data analysis boosts student motivation and reduces stress in physics education

    physics.ed-ph 2024-12 unverdicted novelty 3.0 of 10

    AI-assisted data analysis increases engagement and enjoyment in physics education without changing cognitive learning outcomes compared to traditional Excel methods.

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