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(2) (PDF) AI Literacy - Towards Measuring Human Competency in Artificial Intelligence
Source: researchgate.net
Article summary:
1. The article discusses the need for a measurement instrument to assess general AI literacy, which refers to humans' socio-technical competencies regarding AI.
2. The authors conducted a systematic literature review, expert interviews, and card sorting exercises to develop a validated measurement instrument with five dimensions and 13 items.
3. The developed scale can be used by academics and practitioners to investigate relationships in the future of human work with AI or to enhance understanding of AI acceptance.
Article analysis:
The article "AI Literacy - Towards Measuring Human Competency in Artificial Intelligence" by Pinski and Benlian aims to develop a measurement instrument for general AI literacy, which is defined as humans' socio-technical competencies regarding AI. The authors conducted a systematic literature review, expert interviews, card sorting exercises, and a pre-test study to develop and evaluate the scale.
Overall, the article provides valuable insights into the development of a measurement instrument for general AI literacy. However, there are some potential biases and limitations that need to be considered.
One potential bias is that the authors only focused on IS research and did not consider other fields such as psychology or education. This could limit the scope of the developed scale and overlook important aspects of AI literacy.
Another limitation is that the authors did not provide a clear definition of what they mean by "socio-technical competencies." This could lead to confusion or misinterpretation of the construct.
Additionally, while the authors mention the importance of humanistic outcomes such as well-being in IT professionals, they do not explore this aspect further in their study. This could be an important consideration for future research on AI literacy.
Furthermore, it is unclear how representative the sample size of 50 participants in the pre-test study is. A larger sample size would increase confidence in the validity and reliability of the developed scale.
Finally, while the authors acknowledge that there are first conceptualizations to measure AI literacy in IS-adjunct fields such as computer education, they do not explore these approaches further or compare them to their own approach. This could limit their understanding of existing measures and potentially overlook important aspects of AI literacy.
In conclusion, while the article provides valuable insights into developing a measurement instrument for general AI literacy, there are potential biases and limitations that need to be considered. Future research should address these limitations and explore other fields beyond IS research to develop a more comprehensive understanding of AI literacy.