SIG ED - IS in Education, IS Curriculum, Education and Teaching Cases
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Paper Type
Complete
Paper Number
1209
Description
With recent advances in artificial intelligence, machine learning has been identified as particularly useful for organizations seeking to create value from data resources. However, this usefulness presupposes the existence of data with sufficient structure and quality to train machine learning models. Thus, in this study, we address the research question: How does data resourcing contribute to enabling AI opportunities? We address this question by investigating an example of data resourcing in a master’s level artificial intelligence (AI) course at Umeå University, Sweden. This is an empirical, case-based educational setting where students engaged in data resourcing using a no-code AI platform. Our results involve provide insights regarding constructs associated with two phases of data resourcing: data work practices, and data interpretation. Collectively, these constructs provide a framework for data resourcing that is the main contribution of this paper, together with insights into the benefits of using no-code AI tools in educational settings. Our framework reveals the nature of data resourcing as a creative process where iterative algorithmic mediation, data sensemaking and contextualization enables AI opportunities.
Recommended Citation
Sundberg, Leif T. and Holmström, Jonny, "Are AI Opportunities Discovered or Created? Investigating Data Resourcing Using a No-code AI Platform in an Educational Context" (2022). AMCIS 2022 Proceedings. 11.
https://aisel.aisnet.org/amcis2022/sig_ed/sig_ed/11
Are AI Opportunities Discovered or Created? Investigating Data Resourcing Using a No-code AI Platform in an Educational Context
With recent advances in artificial intelligence, machine learning has been identified as particularly useful for organizations seeking to create value from data resources. However, this usefulness presupposes the existence of data with sufficient structure and quality to train machine learning models. Thus, in this study, we address the research question: How does data resourcing contribute to enabling AI opportunities? We address this question by investigating an example of data resourcing in a master’s level artificial intelligence (AI) course at Umeå University, Sweden. This is an empirical, case-based educational setting where students engaged in data resourcing using a no-code AI platform. Our results involve provide insights regarding constructs associated with two phases of data resourcing: data work practices, and data interpretation. Collectively, these constructs provide a framework for data resourcing that is the main contribution of this paper, together with insights into the benefits of using no-code AI tools in educational settings. Our framework reveals the nature of data resourcing as a creative process where iterative algorithmic mediation, data sensemaking and contextualization enables AI opportunities.
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