Skip to content
1981
  1. Home
  2. A-Z Publications
  3. International Journal of Education Through Art
  4. Volume 19, Issue 3
  5. Article
Volume 19, Issue 3
  • ISSN: 1743-5234
  • E-ISSN: 2040-090X
file format pdf download PDF

Abstract

The rise of image-generating artificial intelligence (AI) tools has triggered changes in digital art and graphic design, provoking debates in the creative industry. However, scant research exists about children’s and youths’ insights into and encounters with generative AI. Building on sociocultural and new materialist perspectives, this exploratory study proposed to address this gap by exploring middle schoolers’ ( = 10) creative interaction with generative AI, particularly with text-to-image generative models. Qualitative content analyses of emerging learning activities evidenced how generative AI-formed relations were externalized through novel digital artefacts and collaborative discussions. Ideas evolved through peer collaboration organized around creative making with AI. Teachers facilitated relations between people and technology using dialogic teaching, providing room for unpredictability and critical reflection on the impacts of generative AI, especially authorship and copyright. The study concludes with a discussion of the potential uses of generative AI in future art education research and practice.

Funding
This study was supported by the:
  • Strategic Research Council (SRC) (Award 352876 and 352859)
© 2023 The Author(s). Published by Intellect Ltd.
This article is Open Access under the terms of the Creative Commons Attribution 4.0 International licence (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. The CC BY licence permits commercial and noncommercial reuse. To view a copy of the licence, visit https://creativecommons.org/licenses/by/4.0/
Loading

Article metrics loading...

/content/journals/10.1386/eta_00143_1
2023-09-29
2025-03-27
Loading full text...

Full text loading...

/deliver/fulltext/eta/19/3/eta.19.3.405_Vartiainen.html?itemId=/content/journals/10.1386/eta_00143_1&mimeType=html&fmt=ahah

References

  1. Audry, S. (2021), Art in the Age of Machine Learning, Cambridge, MA: The MIT Press, https://doi.org/10.7551/mitpress/12832.001.0001.
     [Google Scholar]
  2. Bennett, J. (2010), Vibrant Matter: A Political Ecology of Things, Durham, NC: Duke University Press.
     [Google Scholar]
  3. Blikstein, P. (2013), ‘Digital fabrication and “making” in education: The democratization of invention’, in J. Walter-Herrmann and C. Büching (eds), FabLabs: Of Machines, Makers and Inventors, Bielefeld: Transcript Publishers, pp. 203–21.
     [Google Scholar]
  4. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X. L., Li, X., Ma, T., Malik, A., Manning, C. D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J. C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J. S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., R é, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A. W., Tramèr, F., Wang, R. E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S. M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K. and Liang, P. (2021), ‘On the opportunities and risks of foundation models’, ArXiv, https://doi.org/10.48550/arxiv.2108.07258.
     [Google Scholar]
  5. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I. and Amodei, D. (2020), ‘Language models are few-shot learners’, ArXiv, https://doi.org/10.48550/arxiv.2005.14165.
     [Google Scholar]
  6. Caramiaux, B. and Fdili Alaoui, S. (2022), ‘“Explorers of Unknown Planets”: Practices and politics of artificial intelligence in visual arts’, Proceedings of the ACM on Human-Computer Interaction, 6:CSCW2, pp. 1–24, https://doi.org/10.1145/3555578.
     [Google Scholar]
  7. Dhariwal, P. and Nichol, A. (2021), ‘Diffusion models beat GANs on image synthesis’, Advances in Neural Information Processing Systems, 34, pp. 8780–94.
     [Google Scholar]
  8. Elo, S. and Kyngäs, H. (2008), ‘The qualitative content analysis process’, Journal of Advanced Nursing, 62:1, pp. 107–15.
     [Google Scholar]
  9. Fenwick, T., Edwards, R. and Sawchuk, P. (2011), Emerging Approaches to Educational Research: Tracing the Sociomaterial, New York: Routledge.
     [Google Scholar]
  10. Fiebrink, R. (2019), ‘Machine learning education for artists, musicians, and other creative practitioners’, ACM Transactions on Computing Education, 19:4, pp. 1–32, https://doi.org/10.1145/3294008.
     [Google Scholar]
  11. Fox, N. and Alldred, P. (2022), ‘New materialism’, in P. Atkinson, S. Delamont, M. A. Hardy and M. Williams (eds), SAGE Research Methods Foundations, London: Sage Publications, pp. 1–16.
     [Google Scholar]
  12. Freire, P. (1974), Pedagogy of the Oppressed, New York: Seabury Press.
     [Google Scholar]
  13. Garber, E. (2019), ‘Objects and new materialisms: A journey across making and living with objects’, Studies in Art Education, 60:1, pp. 7–21, https://doi.org/10.1080/00393541.2018.1557454.
     [Google Scholar]
  14. Guerrettaz, A. M., Engman, M. M. and Matsumoto, Y. (2021), ‘Empirically defining language learning and teaching materials in use through sociomaterial perspectives’, The Modern Language Journal, 105:S1, pp. 3–20, https://doi.org/10.1111/modl.12691.
     [Google Scholar]
  15. Hammer, D. and Berland, L. K. (2014), ‘Confusing claims for data: A critique of common practices for presenting qualitative research on learning’, Journal of the Learning Sciences, 23:1, pp. 37–46, https://doi.org/10.1080/10508406.2013.802652.
     [Google Scholar]
  16. Heikkilä, M. (2022), ‘This artist is dominating AI-generated art: And he’s not happy about it’, MIT Technology Review, 16 September, https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/. Accessed 21 August 2023.
     [Google Scholar]
  17. Jo, E. S. and Gebru, T. (2020), ‘Lessons from archives: Strategies for collecting sociocultural data in machine learning’, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, 27–30 January, New York: Association for Computing Machinery (FAT* ’20), pp. 306–16, https://doi.org/10.1145/3351095.3372829.
     [Google Scholar]
  18. Keune, A. and Peppler, K. (2019), ‘Materials-to-develop-with: The making of a makerspace’, British Journal of Educational Technology, 50:1, pp. 280–93, https://doi.org/10.1111/bjet.12702.
     [Google Scholar]
  19. Levendowski, A. (2018), ‘How copyright law can fix artificial intelligence’s implicit bias problem’, Washington Law Review, 93:2, pp. 579–630.
     [Google Scholar]
  20. Liu, V. and Chilton, L. B. (2022), ‘Design guidelines for prompt engineering text-to-image generative models’, Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25–30 April, New York: Association for Computing Machinery (CHI ’22), pp. 1–23, https://doi.org/10.1145/3491102.3501825.
     [Google Scholar]
  21. Mehto, V., Kangas, K. and Seitamaa-Hakkarainen, P. (2020), ‘Sociomateriality of collaboration within a small team in secondary school maker-centered learning project’, International Journal of Child-Computer Interaction, 26, https://doi.org/10.1016/j.ijcci.2020.100209.
     [Google Scholar]
  22. Oppenlaender, J. (2022), ‘The creativity of text-based generative art’, ArXiv, https://doi.org/10.48550/arXiv.2206.02904.
  23. Paullada, A., Raji, I. D., Bender, E. M., Denton, E. and Hanna, A. (2021), ‘Data and its (dis)contents: A survey of dataset development and use in machine learning research’, Patterns, 2:11, https://doi.org/10.1016/j.patter.2021.100336.
     [Google Scholar]
  24. Penuel, W. R., Fishman, B. J., Cheng, B. H. and Sabelli, N. (2011), ‘Organizing research and development at the intersection of learning, implementation, and design’, Educational Researcher, 40:7, pp. 331–37, https://doi.org/10.3102/0013189X11421826.
     [Google Scholar]
  25. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G. and Sutskever, I. (2021), ‘Learning transferable visual models from natural language supervision’, in M. Meila and T. Zhang (eds), Proceedings of the 38th International Conference on Machine Learning, vol. 139, virtual event, 18–24 July, PMLR, pp. 8748–63, https://doi.org/10.48550/arxiv.2103.00020.
     [Google Scholar]
  26. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021), ‘Zero-shot text-to-image generation’, in M. Meila and T. Zhang (eds), Proceedings of the 38th International Conference on Machine Learning, vol. 139, virtual event, 18–24 July, PMLR, pp. 8821–31, https://doi.org/10.48550/arxiv.2102.12092.
     [Google Scholar]
  27. Rezwana, J. and Maher, M. Lou (2022), ‘Designing creative AI partners with COFI: A framework for modeling interaction in human-AI co-creative systems’, ACM Transactions on Computer-Human Interaction, https://doi.org/10.1145/3519026.
     [Google Scholar]
  28. Rousell, D. and Fell, F. (2018), ‘Becoming a work of art: Collaboration, materiality and posthumanism in visual arts education’, International Journal of Education Through Art, 14:1, pp. 91–110, https://doi.org/10.1386/eta.14.1.91_1.
     [Google Scholar]
  29. Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M. (2022), ‘Photorealistic text-to-image diffusion models with deep language understanding’, ArXiv, https://doi.org/10.48550/arXiv.2205.11487.
  30. Taguchi, H. L. (2011), ‘Investigating learning, participation and becoming in early childhood practices with a relational materialist approach’, Global Studies of Childhood, 1:1, pp. 36–50, https://doi.org/10.2304/gsch.2011.1.1.36.
     [Google Scholar]
  31. TENK (2019), The Ethical Principles of Research with Human Participants and Ethical Review in the Human Sciences in Finland: Finnish National Board on Research Integrity TENK Guidelines 2019, https://tenk.fi/sites/default/files/2021-01/Ethical_review_in_human_sciences_2020.pdf. Accessed 15 October 2022.
  32. Vartiainen, H., Pellas, L., Kahila, J., Valtonen, T. and Tedre, M. (2022), ‘Pre-service teachers’ insights on data agency’, New Media & Society, online first, https://doi.org/10.1177/14614448221079626.
     [Google Scholar]
  33. Vartiainen, H. and Tedre, M. (2023), ‘Using artificial intelligence in craft education: Crafting with text-to-image generative models’, Digital Creativity, 34:1, pp. 1–21, https://doi.org/10.1080/14626268.2023.2174557.
     [Google Scholar]
  34. Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M. and Valtonen, T. (2021), ‘Machine learning for middle schoolers: Learning through data-driven design’, International Journal of Child-Computer Interaction, 29, https://doi.org/10.1016/J.IJCCI.2021.100281.
     [Google Scholar]
  35. Vygotsky, L. (1978), Mind in Society: Development of Higher Psychological Processes: Cambridge, MA: Harvard University Press.
     [Google Scholar]
/content/journals/10.1386/eta_00143_1
Loading
Co-creating digital art with generative AI in K-9 education: Socio-material insights
International Journal of Education Through Art 19, 405 (2023); https://doi.org/10.1386/eta_00143_1
/content/journals/10.1386/eta_00143_1
/content/journals/10.1386/eta_00143_1
Loading

Data & Media loading...

  • Article Type: Article
Keyword(s): artificial intelligence; generative AI; K-12; Midjourney; prompt crafting; prompt engineering; school education; text-to-image generative models

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a success
Invalid data
An error occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test