Scientific coexistence and mutual learning between humans and ChatGPT 4.0

• Authors: Elżbieta Perzycka-Borowska

Title variants

PL

Naukowa koegzystencja i wzajemne uczenie się ludzi oraz ChatGPT 4.0

Abstracts

PL

Niniejsze badanie zgłębia dynamikę naukowej współpracy oraz wzajemnego uczenia się między nauczycielem akademickim – badaczem a sztuczną inteligencją, uosobioną przez ChatGPT 4.0 (nazwanego „Alex”). Badanie ujmuje AI, a w szczególności ChatGPT 4.0 nie tylko jako narzędzie poznawcze, lecz także jako partnera w dialogu. Wykorzystując autoetnografię, analiza ukazuje, w jaki sposób interakcje człowieka z AI kształtują ludzką percepcję, doświadczenie i praktyki w różnych obszarach życia akademickiego. Podkreśla również złożoność tych interakcji oraz konieczność refleksyjnego i krytycznego podejścia do relacji człowiek AI, wskazując na ewoluującą naturę paradygmatów edukacyjnych i metodologicznych w erze sztucznej inteligencji.

Keywords

PL

interakcja człowiek – AI; ChatGPT 4.0; wzajemne uczenie się; podejście krytyczne; autoetnografia

• Publisher: Polski Uniwersytet na Obczyźnie w Londynie
• Journal: Zeszyty Naukowe PUNO
• Year: 2024
• Issue: 1
• Pages: 181-193

Dates

published

2024

Contributors

author

Elżbieta Perzycka-Borowska

• Polski Uniwersytet na Obczyźnie, Londyn

• https://orcid.org/0000000278293806

References

• Aggarwal A., Tam C. C., Wu D., Li X., Qiao S. (2022). Artificial intelligence (AI)-based chatbots in promoting health behavioural changes: a systematic review, https://doi.org/10.1101/2022.07.05.22277263.
• Anderson L. B., Kanneganti D., Houk M. B., Holm R. H., Smith T. (2023). Generative AI as a tool for environmental health research translation. GeoHealth, 7 (7), https://doi.org/10.1029/2023gh000875.
• Andujar C. S., Gutiérrez-Martín L., Miranda-Calero J. Á., Blanco-Ruiz M., López-Ongil C. (2022). Gender biases in the training methods of affective computing: redesign and validation of the self-assessment manikin in measuring emotions via audiovisual clips. Frontiers in Psychology, 13, https://doi.org/10.3389/fpsyg.2022.955530.
• Berens P., Cranmer K., Lawrence N. D., Luxburg U. v., Montgomery J. (2023). AI for science: an emerging agenda, https://doi.org/10.48550/arxiv.2303.04217.
• Caliskan A., Bryson J. J., Narayanan A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356 (6334), 183–186, https://doi.org/10.1126/science.aal4230.
• Ciechanowski L., Przegalinska A., Magnuski M., and Gloor P. (2019). In the shades of the uncanny valley: an experimental study of human-chatbot interaction. Future Gener. Comput. Syst. 92, 539–548, https://doi.org/10.1016/j.future.2018.01.055.
• Darcel K., Upshaw T., Craig-Neil A., Macklin J., Gray C. S., Chan T. C. Y., Pinto A. D. (2023). Implementing artificial intelligence in Canadian primary care: barriers and strategies identified through a national deliberative dialogue. Plos One, 18 (2), e0281733, https://doi.org/10.1371/journal.pone.0281733.
• Denzin N. K. (2017). Critical qualitative inquiry. Qualitative Inquiry, 23 (1), 8–16. https://doi.org/10.1177/1077800416681864
• Elisa F., Cesta A., Umbrico A., Orlandini A. (2021). Simplifying the a.i. planning modelling for human-robot collaboration. 2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN), https://doi.org/10.1109/ro-man50785.2021.9515431.
• Ellis C. (2004). The Ethnographic I: A Methodological Novel about Autoethnography. Walnut Creek, California: Altamira Press.
• Ellis C., Adams T. E., Bochner A. P. (2011). Autoethnography: an overview, Historical Sicial Research. 36, No. 4, 273–290, https://doi.org/10.12759/hsr.36.2011.4.273-290.
• Klichowski M. (2020). People Copy the Actions of Articial intelligence. Frontiers in Psychology, June, Vol. 11. Article 1130, 1–7, https://doi.org/10.3389/fpsyg.2020.01130.
• Köbis N., Bonnefon J., Rahwan I. (2021). Bad machines corrupt good morals. Nature Human Behaviour, 5 (6), 679–685, https://doi.org/10.1038/s41562-021-01128-2.
• Köllen T. (2019). Diversity management: a critical review and agenda for the future. Journal of Management Inquiry, 30 (3), 259–272, https://doi.org/10.1177/1056492619868025.
• Laakkonen M. (2021). Articial intelligence (AI): hidden rules of our society. Hallinnon Tutkimus, 40 (4), 276–283, https://doi.org/10.37450/ht.112201.
• Lee C. I., Houssami N., Elmore J. G., Buist D. S. (2020). Pathways to breast cancer screening artificial intelligence algorithm validation. The Breast, 52, 146–149, https://doi.org/10.1016/j.breast.2019.09.005.
• Lemaignan S., Warnier M., Sisbot E. A., Clodic A., and Alami R. (2017). Articial cognition for social human-robot interaction: an implementation. Artif. Intell. 247, 45–69, https://doi.org/10.1016/j.artint.2016.07.002.
• O’Meara S. (2019). AI researchers in China want to keep the global-sharing culture alive. Nature 569, S33–S35, https://doi.org/10.1038/d41586-019-01681-x.
• OpenAI. (2023). ChatGPT (wersja 4.0). Retrieved from https://chat.openai.com/.
• Roche C., Wall P. J., Lewis D. (2022). Ethics and diversity in artificial intelligence policies, strategies and initiatives. AI and Ethics, 3 (4), 1095–1115, https://doi.org/10.1007/s43681-022-00218-9.
• Smith J. (2023). Eects of Machine Learning Algorithms for Predicting and Optimizing the Properties of New Materials in the United States, European Journal of Physics Sciences, Vol. 6, Issue 1, 23–34, https://doi.org/10.47672/ejps.1444.
• Szwabowski O., Baron-Polańczyk E., Cywiński A., Gliniecka M., Lib W., Łuszczek K., Marek L., Perzycka E., Walat W., Warzocha T. (2022). A Story by Academic Teacher About Dustance Education in the Time of Lockdown. Cultural Studies – Critical Methodologies, Volume 22, Issue 4, 1–10, https://doi.org/10.1177/15327086221094283.
• Tenakwah E. S., Boadu G., Tenakwah E. J., Parzakonis M., Brady M., Kansiime P., Berman A. (2023). Generative AI and higher education assessments: a competency-based analysis, https://doi.org/10.21203/rs.3.rs-2968456/v1.
• Thoring K., Huettemann S., Mueller R. M. (2023). The augmented designer: a research agenda for generative AI-enabled design. Proceedings of the Design Society, 3, 3345–3354, https://doi.org/10.1017/pds.2023.335.
• Upshaw T., Craig-Neil A., Macklin J., Gray C. S., Chan T. C. Y., Gibson J. L., Pinto A. D. (2023). Priorities for artificial intelligence applications in primary care: a Canadian deliberative dialogue with patients, providers, and health system leaders. The Journal of the American Board of Family Medicine, 36 (2), 210–220, https:// doi.org/10.3122/jabfm.2022.220171r1.
• Vasoya N. H. (2023). The role of parents and educators in managing the risks of artificial intelligence. Asian Journal of Education and Social Studies, 41 (4), 1–5, https://doi.org/10.9734/ajess/2023/v41i4899.
• Victor B. G., Kubiak S. P., Angell B., Perron B. E. (2023). Time to move beyond the aswb licensing exams: can generative artificial intelligence oer a way forward for social work? Research on Social Work Practice, 33 (5), 511–517, https://doi.org/10.1177/10497315231166125.
• Vlasceanu M., Dudík M., Momennejad I. (2021). Interdisciplinarity, gender diversity, and network structure predict the centrality of AI organizations, https://doi.org/10.31234/osf.io/dp3ef.

Identifiers

Biblioteka Nauki

63008364