Paper Number
1418
Paper Type
Short Paper
Abstract
Generative conversational artificial intelligence (GCAI) is considered one of the most disruptive technologies of our time, significantly changing the way jobs are performed. Despite its promised benefits, limited research exists on GCAI usage, making it unclear which usage strategies yield better outcomes. We plan to conduct a study to gain a better understanding of how working with GCAI impacts performance. Thus far, 15 participants have been recruited for a within-subject lab experiment in an agile requirements analysis setting. Preliminary findings indicate that working with GCAI may result in higher performance quality than working without it. We identified three usage strategies: information search, delegation, and augmentation, which differ in their composition of applied prompt types and utilized GCAI responses. Our initial findings indicate that performance quality may differ depending on the usage strategy. These findings underline the importance of considering usage strategies to enhance the workforce’s proficiency in using GCAIs.
Recommended Citation
Oberhofer, Viviana M.; Seeber, Isabella; and Maier, Ronald, "Delegation or Augmentation — Strategies for Working Effectively with Generative Conversational Artificial Intelligence" (2024). ECIS 2024 Proceedings. 8.
https://aisel.aisnet.org/ecis2024/track04_impactai/track04_impactai/8
Delegation or Augmentation — Strategies for Working Effectively with Generative Conversational Artificial Intelligence
Generative conversational artificial intelligence (GCAI) is considered one of the most disruptive technologies of our time, significantly changing the way jobs are performed. Despite its promised benefits, limited research exists on GCAI usage, making it unclear which usage strategies yield better outcomes. We plan to conduct a study to gain a better understanding of how working with GCAI impacts performance. Thus far, 15 participants have been recruited for a within-subject lab experiment in an agile requirements analysis setting. Preliminary findings indicate that working with GCAI may result in higher performance quality than working without it. We identified three usage strategies: information search, delegation, and augmentation, which differ in their composition of applied prompt types and utilized GCAI responses. Our initial findings indicate that performance quality may differ depending on the usage strategy. These findings underline the importance of considering usage strategies to enhance the workforce’s proficiency in using GCAIs.
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