AI Literacy Research: Frontier for High-Impact Research and Ethics

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Ricardo Limongi
Carla Bonato Marcolin

Abstract

Artificial intelligence (AI) has emerged as a driving force in scientific development, influencing areas such as Administration and Public Administration, which are central themes of the publication of the Brazilian Administration Review (BAR). Despite transformative promises, AI’s appropriate and ethical use in academia still presents substantial challenges, especially for researchers who require specific skills to understand, critique, and utilize these technologies (Dwivedi et al., 2023; Susarla et al., 2023). Even though it is an important support tool to improve texts, the risks of hallucination (generating wrong information) and transformation of already written texts (plagiarism) worry the academic community. In addition, the continuous advancement of generative language models (LLMs) makes AI-written text detection tools often unreliable. AI literacy emerges, in this context, as an essential competence for researchers to deal with AI tools critically, ethically, and effectively.


This editorial discusses the growing need to promote AI literacy as a fundamental prerequisite for researchers. It reflects on the impacts of this competence on the training of scientists capable of exploring AI’s potential while understanding its limitations and ethical implications. We argue that research on AI literacy should be a priority, not only as an emerging field of study but also as a central axis in preparing the next generation of researchers, ensuring the responsible use of AI in advancing knowledge.

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How to Cite
Limongi, R., & Marcolin, C. B. (2024). AI Literacy Research: Frontier for High-Impact Research and Ethics. Brazilian Administration Review, 21(3), e240162. https://doi.org/10.1590/1807-7692bar2024240162
Section
Editorial

References

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1
Bender, E. M., Gebru, T., McMillan-Major, A., & Mitchell, M. (2021). On the dangers of stochastic parrots: Can language models be too big?. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, New York, NY, USA. https://doi.org/10.1145/3442188.3445922
Bi, Z., Cheng, S., Chen, J., Liang, X., Xiong, F., & Zhang, N. (2024). Relphormer: Relational graph transformer for knowledge graph representations. Neurocomputing, 566(6), 127044. https://doi.org/10.1016/j.neucom.2023.127044
Du, H., Sun, Y., Jiang, H., Islam, A. Y. M., & Gu, X. (2024). Exploring the effects of AI literacy in teacher learning: An empirical study. Humanities and Social Sciences Communications, 11, 559. https://doi.org/10.1057/s41599-024-03101-6
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... & Wright, R. (2023). Opinion Paper: ”So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082-1104. https://doi.org/10.1111/bjet.13411
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.
Sperling, K., Stenberg, C. J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169. https://doi.org/10.1016/j.caeo.2024.100169
Susarla, A., Gopal, R., Thatcher, J. B., & Sarker, S. (2023). The janus effect of generative AI: Charting the path for responsible conduct of scholarly activities in information systems. Information Systems Research, 34(2), 399-408. https://doi.org/10.1287/isre.2023.ed.v34.n2
Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., Dam, S. K., & Hong, C. S. (2023). One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era. arXiv preprint. https://doi.org/10.48550/arXiv.2304.06488